0%
@inproceedings{Li2025b,
title = {State-Guided Spatial Cross-Attention for Enhanced End-to-End Autonomous Driving},
author = {Dongyang Li and Ehsan Javanmardi and Manabu Tsukada},
year = {2025},
date = {2025-09-30},
urldate = {2025-09-30},
booktitle = {IEEE International Automated Vehicle Validation Conference (IAVVC 2025)},
address = {Baden-Baden, Germany},
abstract = {Handling near-accident scenarios is a significant challenge for end-to-end autonomous driving (E2E-AD), as these situations often involve sudden environmental changes, complex interactions with other road users, and high-risk decision-making under uncertainty. Unlike routine driving tasks, near-accident scenarios require rapid and precise responses based on external perception and internal vehicle dynamics. Successfully navigating such situations demands not only a comprehensive understanding of the surrounding environment but also an accurate assessment of the ego vehicle's state, including speed, acceleration, and steering angle, to ensure safe and reliable control. However, conventional E2E-AD models struggle to handle these safety-critical situations effectively. Standard approaches primarily rely on raw sensor inputs to learn driving policies, often overlooking the crucial role of vehicle state information in decision-making. Since many near-accident scenarios involve conditions where the same environmental observation could require vastly different responses depending on the ego vehicle's motion state-such as whether the vehicle is braking, accelerating, or experiencing traction loss-ignoring these internal dynamics can lead to unsafe or suboptimal actions. Furthermore, E2E-AD models typically learn a direct mapping from sensory inputs to control outputs, making it difficult to generalize to highly dynamic and unpredictable interactions, such as emergency evasive maneuvers or sudden braking events. To address these challenges, we propose a state-guided cross-attention mechanism that explicitly models the interaction between the ego vehicle's states and its perception of the environment. By incorporating vehicle state information into the decision-making process, our approach ensures that the model can dynamically adjust its attention to critical sensory inputs based on real-time driving conditions. This allows the autonomous system to make more context-aware decisions, improving its ability to respond effectively to complex and safety-critical scenarios.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Li2025c,
title = {PrefDrive: Enhancing Autonomous Driving through Preference-Guided Large Language Models},
author = {Yun Li and Ehsan Javanmardi and Simon Thompson and Kai Katsumata and Alex Orsholits and Manabu Tsukada},
url = {https://github.com/LiYun0607/PrefDrive/
https://huggingface.co/liyun0607/PrefDrive
https://huggingface.co/datasets/liyun0607/PrefDrive},
year = {2025},
date = {2025-06-22},
urldate = {2025-06-22},
booktitle = {36th IEEE Intelligent Vehicles Symposium (IV2025)},
address = {Cluj-Napoca, Romania},
abstract = {This paper presents PrefDrive, a novel framework that integrates driving preferences into autonomous driving models through large language models (LLMs). While recent advances in LLMs have shown promise in autonomous driving, existing approaches often struggle to align with specific driving behaviors (e.g., maintaining safe distances, smooth acceleration patterns) and operational requirements (e.g., traffic rule compliance, route adherence). We address this challenge by developing a preference learning framework that combines multimodal perception with natural language understanding. Our approach leverages Direct Preference Optimization (DPO) to fine-tune LLMs efficiently on consumer-grade hardware, making advanced autonomous driving research more accessible to the broader research community. We introduce a comprehensive dataset of 74,040 sequences, carefully annotated with driving preferences and driving decisions, which, along with our trained model checkpoints, will be made publicly available to facilitate future research. Through extensive experiments in the CARLA simulator, we demonstrate that our preference-guided approach significantly improves driving performance across multiple metrics, including distance maintenance and trajectory smoothness. Results show up to 28.1% reduction in traffic rule violations and 8.5% improvement in navigation task completion while maintaining appropriate distances from obstacles. The framework demonstrates robust performance across different urban environments, showcasing the effectiveness of preference learning in autonomous driving applications. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Jiang2025,
title = {Towards Efficient Roadside LiDAR Deployment: A Fast Surrogate Metric Based on Entropy-Guided Visibility},
author = {Yuze Jiang and Ehsan Javanmardi and Manabu Tsukada and Hiroshi Esaki},
url = {https://arxiv.org/abs/2504.06772},
year = {2025},
date = {2025-06-22},
urldate = {2025-06-22},
booktitle = {36th IEEE Intelligent Vehicles Symposium (IV2025)},
address = {Cluj-Napoca, Romania},
abstract = {The deployment of roadside LiDAR sensors plays a crucial
role in the development of Cooperative Intelligent
Transport Systems (C-ITS). However, the high cost of LiDAR
sensors necessitates efficient placement strategies to
maximize detection performance. Traditional roadside LiDAR
deployment methods rely on expert insight, making them
time-consuming. Automating this process, however, demands
extensive computation, as it requires not only visibility
evaluation but also assessing detection performance across
different LiDAR placements. To address this challenge, we
propose a fast surrogate metric, the Entropy-Guided
Visibility Score (EGVS), based on information gain to
evaluate object detection performance in roadside LiDAR
configurations. EGVS leverages Traffic Probabilistic
Occupancy Grids (TPOG) to prioritize critical areas and
employs entropy-based calculations to quantify the
information captured by LiDAR beams. This eliminates the
need for direct detection performance evaluation, which
typically requires extensive labeling and computational
resources. By integrating EGVS into the optimization
process, we significantly accelerate the search for optimal
LiDAR configurations. Experimental results using the AWSIM
simulator demonstrate that EGVS strongly correlates with
Average Precision (AP) scores and effectively predicts
object detection performance. This approach offers a
computationally efficient solution for roadside LiDAR
deployment, facilitating scalable smart infrastructure
development. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@misc{Tsukada2025b,
title = {V2X Communication Technologies in the Era of End-to-End Autonomous Driving},
author = {Manabu Tsukada},
url = {https://sites.google.com/view/b-stem-iot/},
year = {2025},
date = {2025-06-22},
urldate = {2025-06-22},
abstract = {Autonomous driving technology is undergoing a significant paradigm shift from traditional rule-based systems to integrated End-to-End (E2E) deep learning architectures. This transition necessitates a fundamental rethinking of Vehicle-to-Everything (V2X) communication, as existing V2X standards, primarily designed for rule-based systems, may not fully leverage the capabilities or address the needs of E2E models. This presentation explores the evolution required for V2X technologies in the E2E era. We contrast rule-based and E2E architectures, highlighting the limitations of current V2X approaches like object-level message sharing for E2E systems that benefit from richer data. While intermediate feature sharing via V2X is promising, its practical implementation faces hurdles, notably the heterogeneity of sensors, AI models, and tasks across vehicles. To address these challenges, we introduce a research approach aiming to maximize V2X value through an E2E pipeline encompassing data foundation (Co3SOP dataset for collaborative 3D semantic occupancy), perception adaptation (PHCP framework for heterogeneous collaboration during inference), and decision optimization (PrefDrive integrating LLMs with preference learning). Through these interconnected efforts, we aim to unlock the full potential of V2X communication to enhance the safety, efficiency, and robustness of E2E autonomous driving systems.},
howpublished = {The 2nd Workshop on Secure connected vehicles: Digital Twin, UAVs, and Smart Transportation, at IEEE IV 2025},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
@workshop{Hu2025,
title = {A Low PAPR Layered Multi-User OTFS Modulation},
author = {Dou Hu and Jin Nakazato and Kazuki Maruta and Omid Abbassi Aghda and Rui Dinis and Manabu Tsukada},
year = {2025},
date = {2025-06-17},
urldate = {2025-06-17},
booktitle = {AI-Driven Connectivity for Vehicular and Wireless Networks in VTC2025-Spring},
address = {Oslo, Norway},
abstract = {In modern communication systems, meeting the growing demand for high-capacity transmission requires developing efficient and robust modulation techniques. To address
this, we propose a low-PAPR page-style Orthogonal Time Frequency Space (OTFS) modulation framework that enhances communication capacity while maintaining a low peak-to-average power ratio (PAPR). The proposed design introduces a novel pilot signal placement and analysis method, improving channel estimation accuracy and system performance in high-mobility multi-user scenarios. This paper provides an overview of recent advancements in OTFS-based multi-user communication systems, emphasizing their contributions to enhancing spectral efficiency, reliability, and robustness. Through extensive simulations, we demonstrate the effectiveness of the proposed framework in achieving superior BER performance, improved interference mitigation, and robust transmission capabilities compared to traditional methods, validating its suitability for next-generation communication networks.},
howpublished = {Workshop on AI-Driven Connectivity for Vehicular and Wireless Networks in VTC2025-Spring},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@inproceedings{Rao2025,
title = {A LEO satellite routing method based on incremental evolutionary graph reinforcement learning},
author = {Zheheng Rao and Zhenyu Zhu and Wei Yang Bryan Lim and Ye Yao and Yanyan Xu and Manabu Tsukada and Yanyu Cheng},
year = {2025},
date = {2025-06-08},
booktitle = {IEEE International Conference on Communications (ICC 2025)},
address = {Montreal, Canada},
abstract = {Only the chairs can edit With the advent of sixth-generation (6G) technologies and growing communication demands, Low Earth Orbit (LEO) satellite networks have become essential in modern communications. However, due to the dynamic topology and complex network state of LEO environments, existing routing methods often fail to make effective decisions, limiting transmission performance. This paper proposes a novel routing method, DGA-IES. To address network state perception challenges, we introduce a topological learning model using deep graph attention (DGA), which captures complex inter-satellite connectivity and resource states. Additionally, by integrating incremental evolution strategies (IES) into deep reinforcement learning (DRL), we replace sequential interactive proximal policy optimization (PPO) with global parallel ES, achieving efficient routing convergence in the highly dynamic LEO environment. Experimental results demonstrate that our DGA-IES approach enhances LEO network load balancing by reducing end-to-end (E2E) network latency, decreasing packet loss and improving throughput compared with the benchmark approaches.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{ITO2025108157,
title = {A multipath redundancy communication framework for enhancing 5G mobile communication quality},
author = {Koki Ito and Jin Nakazato and Romain Fontugne and Manabu Tsukada and Esaki Hiroshi},
url = {https://www.sciencedirect.com/science/article/pii/S0140366425001148},
doi = {https://doi.org/10.1016/j.comcom.2025.108157},
issn = {0140-3664},
year = {2025},
date = {2025-04-23},
urldate = {2025-04-23},
journal = {Computer Communications},
pages = {108157},
abstract = {As networks increasingly become the backbone of modern society, the demands placed on them by various applications have become more complex. In particular, the demand for high-capacity, low-latency services such as real-time streaming is increasing every year. Although 5G has been deployed to meet these needs, its effectiveness can vary significantly by location and time, and sometimes falls short of requirements. Traditionally, much of the research to improve communication stability has focused on TCP-based systems, which do not translate well to real-time UDP streaming applications. To address the above challenges, we propose a multipath redundant communication framework designed to improve the quality of real-time media streaming. This framework has been tested using multipath redundant communication over two mobile networks with a moving vehicle in an urban environment. Using a real-time streaming application based on WebRTC, our framework demonstrates a significant reduction in packet loss and an increase in bitrate, outperforming existing multipath redundant communication systems without interfering with the application’s congestion control mechanisms.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@online{hanlin2025,
title = {Co3SOP: A Collaborative 3D Semantic Occupancy Prediction Dataset and Benchmark for Autonomous Driving},
url = {https://github.com/tlab-wide/Co3SOP},
year = {2025},
date = {2025-04-13},
urldate = {2025-04-13},
abstract = {To facilitate 3D semantic occupancy prediction in collaborative scenarios, we present a simulated dataset featuring a 3D semantic occupancy voxel sensor in Carla, which precisely and comprehensively annotate every surrounding voxel with semantic and occupancy states. In addition, we establish two benchmarks with varying detection ranges to investigate the impact of vehicle collaboration across different spatial extents and propose a baseline model that allows collaborative feature fusion. Experiments on our proposed benchmark demonstrate the superior performance of our baseline model.},
keywords = {},
pubstate = {published},
tppubtype = {online}
}
@inproceedings{Orsholits2025,
title = {Context-Rich Interactions in Mixed Reality through Edge AI Co-Processing},
author = {Alex Orsholits and Manabu Tsukada},
url = {https://link.springer.com/chapter/10.1007/978-3-031-87772-8_3},
doi = {10.1007/978-3-031-87772-8_3},
isbn = {978-3-031-87771-1},
year = {2025},
date = {2025-04-09},
urldate = {2025-04-09},
booktitle = {The 39-th International Conference on Advanced Information Networking and Applications (AINA 2025)},
address = {Barcelona, Spain},
abstract = {Spatial computing is evolving towards leveraging data streaming for computationally demanding applications, facilitating a shift to lightweight, untethered, and standalone devices. These devices are therefore ideal candidates for co-processing, where real-time context understanding and low-latency data streaming are fundamental for seamless, general-purpose Mixed Reality (MR) experiences. This paper demonstrates and evaluates a scalable approach to augmented contextual understanding in MR by implementing multi-modal edge AI co-processing through a Hailo-8 AI accelerator, a low-power ARM-based single board computer (SBC), and the Magic Leap 2 AR headset. The proposed system utilises the native WebRTC streaming capabilities of the Magic Leap 2 to continuously stream camera data to the edge co-processor, where a collection of vision AI models-object detection, pose estimation, face recognition, and depth estimation-are executed. The resulting inferences are then streamed back to the headset for spatial re-projection and transmitted to cloud-based systems for further integration with large-scale AI models, such as LLMs and VLMs. This seamless integration enhances real-time contextual understanding in MR while facilitating advanced multi-modal, multi-device collaboration, supporting richer, scalable spatial cognition across distributed systems.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@online{V2X_E2E_Simulator2024,
title = {V2X End-to-End simulator},
url = {https://github.com/tlab-wide/V2X_E2E_Simulator
https://tlab-wide.github.io/V2X_E2E_Simulator/},
year = {2025},
date = {2025-03-31},
keywords = {},
pubstate = {published},
tppubtype = {online}
}
@misc{Orsholits2025b,
title = {Edge Vision AI Co-Processing for Dynamic Context Awareness in Mixed Reality},
author = {Alex Orsholits and Manabu Tsukada},
url = {https://www.youtube.com/watch?v=xxahKZl4K9w
https://ieeevr.org/2025/awards/conference-awards/#poster-honorable},
doi = {10.1109/VRW66409.2025.00293},
year = {2025},
date = {2025-03-08},
urldate = {2025-03-08},
booktitle = {2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)},
address = {Saint-Malo, France},
abstract = {Spatial computing is evolving towards leveraging data streaming for computationally demanding applications, facilitating a shift to lightweight, untethered, and standalone devices. These devices are ideal candidates for co-processing, where real-time scene context understanding and low-latency data streaming are fundamental for general-purpose Mixed Reality (MR) experiences. This poster demonstrates and evaluates a scalable approach to augmented contextual understanding in MR by implementing edge AI co-processing through a Hailo-8 AI accelerator, a low-power ARM-based single board computer (SBC), and the Magic Leap 2 AR headset. The resulting inferences are streamed back to the headset for spatial reprojection into the user’s vision.},
howpublished = {IEEE VR 2025, Poster},
note = {Honorable mention},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
@inproceedings{Zhu2025,
title = {A Distributed Content Subscription Mechanism with Revision Discovery to Decouple Content Sharing Platform and Creator ID},
author = {Zhihai Zhu and Ye Tao and Manabu Tsukada and Hiroshi Esaki},
year = {2025},
date = {2025-02-18},
urldate = {2025-02-18},
booktitle = {International Conference on Artificial Intelligence in Information and Communication (ICAIIC 2025) },
address = {Fukuoka, Japan},
abstract = {Only the chairs can edit This paper proposes a distributed content subscription mechanism that enables content creators to share updates with their audience while maintaining platform independence and anonymity. The mechanism extends the Kademlia distributed hash table (DHT) protocol by incorporating revision numbers and republication timestamps into the DHT key computation, allowing subscribers to discover content updates through heuristic revision queries. It leverages public key cryptography for creator identification and content authenticity, while integrating with established peer-to-peer protocols like BitTorrent for efficient content distribution. Preliminary testing with 200 simulated nodes demonstrates the mechanism's ability to maintain content availability and update discovery even when content creators are offline. This approach particularly benefits creators operating under strict content controls or surveillance, offering them greater creative freedom and distribution autonomy compared to existing centralized and decentralized solutions.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Asad2024b,
title = {Federated Learning for Secure and Efficient Vehicular Communications in Open RAN},
author = {Muhammad Asad and Saima Shaukat and Jin Nakazato and Ehsan Javanmardi and Manabu Tsukada},
url = {https://rdcu.be/d7RSW},
doi = {10.1007/s10586-024-04932-3},
issn = {1386-7857},
year = {2025},
date = {2025-01-28},
urldate = {2024-11-25},
journal = {Cluster Computing},
volume = {28},
number = {211},
abstract = {This paper presents a comprehensive exploration of federated learning applied to vehicular communications within the context of Open RAN. Through an in-depth review of existing literature and analysis of fundamental concepts, critical challenges are identified within the current methodologies employed in this sphere. A novel framework is proposed to address these shortcomings, fundamentally based on federated learning principles. This framework aims to enhance security and efficiency in vehicular communications, leveraging the flexibility of Open RAN architecture. The paper further delves into a rigorous justification of the proposed solution, highlighting its potential impact and the improvements it could bring to vehicular communications. Ultimately, this study provides a roadmap for future research in applying federated learning for more secure and efficient vehicular communications in Open RAN, opening up new avenues for exploration in this exciting interdisciplinary domain.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Yamane2025,
title = {Low Latency Redundant Network Architecture for Enhanced 5G Mobile Communication Quality},
author = {Nayuta Yamane and Jin Nakazato and Koki Ito and Manabu Mikami and Takahiro Tsuchiya and Manabu Tsukada and Hiroshi Esaki},
year = {2025},
date = {2025-01-14},
booktitle = {39th International Conference on Information Networking (ICOIN) 2025},
address = {Chiang Mai, Thailand},
abstract = {With growing demand for high-capacity, low-latency communication driven by the increasing use of video streaming and real-time data applications, efficient data transfer methods have become essential. While 5G technology provides enhanced data transfer capabilities, its directional nature can lead to stability issues, including connection interruptions, frequent handoffs, data loss, and increased latency. To address these challenges, we propose a system architecture with a redundant configuration that separates LTE and 5G-SA within a single MNO, alongside a method to reduce overhead. Initial verification in a stationary environment demonstrated the reduction of latency by 38.1 ms compared to the conventional method, underscoring the potential of our approach for stable and efficient data transmission.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@misc{Nakazato2025,
title = {Toward 6G Mobility Network: Design of a Wireless Digital Twin for Connected Autonomous Vehicle},
author = {Jin Nakazato and Tetsuya Iye and Yuki Susukida and Eisaku Sato and Yuki Sasaki and Kazuki Maruta and Manabu Tsukada
},
year = {2025},
date = {2025-01-10},
pages = {1-2},
howpublished = {2025 IEEE 22nd Consumer Communications & Networking Conference (CCNC), Poster},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
@inproceedings{Lin2024c,
title = {A Rule-Compliance Path Planner for Lane-Merge Scenarios Based on Responsibility-Sensitive Safety},
author = {Pengfei Lin and Ehsan Javanmardi and Yuze Jiang and Manabu Tsukada},
doi = {10.1109/ICARCV63323.2024.10821557},
year = {2024},
date = {2024-12-12},
urldate = {2024-12-12},
booktitle = {2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV)},
address = {Dubai, UAE},
abstract = {Lane merging is one of the critical tasks for selfdriving cars, and how to perform lane-merge maneuvers effectively and safely has become one of the important standards
in measuring the capability of autonomous driving systems.
However, due to the ambiguity in driving intentions and
right-of-way issues, the lane merging process in autonomous
driving remains deficient in terms of maintaining or ceding
the right-of-way and attributing liability, which could result
in protracted durations for merging and problems such as
trajectory oscillation. Hence, we present a rule-compliance
path planner (RCPP) for lane-merge scenarios, which initially
employs the extended responsibility-sensitive safety (RSS) to
elucidate the right-of-way, followed by the potential field-based
sigmoid planner for path generation. In the simulation, we have
validated the efficacy of the proposed algorithm. The algorithm
demonstrated superior performance over previous approaches
in aspects such as merging time (Saved 72.3%), path length
(reduced 53.4%), and eliminating the trajectory oscillation.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@misc{Chauhan2024c,
title = {Reimagining Smart Poles: A Sustainable Interface for Pedestrian-AV Ecosystems},
author = {Vishal Chauhan and Anubhav and Megha Sharma and Manabu Tsukada},
year = {2024},
date = {2024-12-03},
urldate = {2024-12-03},
address = {Brisbane, Australia},
howpublished = {OzCHI Student Design Challenge (Poster, Video)},
note = {Finalists Award},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
@inproceedings{Sugizaki2024,
title = {Digital Twin Based Open Platform for IoT Offloading Control: Enabling System Transparency and User Participation},
author = {Yusuke Sugizaki and Jin Nakazato and Manabu Tsukada},
year = {2024},
date = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Chauhan2024b,
title = {Connected Shared Spaces: Expert Insights into the Impact of eHMI and SPIU for Next-Generation Pedestrian-AV Communication},
author = {Vishal Chauhan and Anubhav Anubhav and Chia-Ming Chang and Jin Nakazato and Ehsan Javanmardi and Alex Orsholits and Takeo Igarashi and Kantaro Fujiwara and Manabu Tsukada},
year = {2024},
date = {2024-11-28},
urldate = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Nakazato2024,
title = {Toward 6G Mobility Networks: A Proposal for Cell-Free Cooperative Distributed Beamforming},
author = {Jin Nakazato and Sojin Ozawa and Yuki Sasaki and Kengo Suzuki and Kazuki Maruta andTetsuya Iye and Yuki Susukida and Eisaku Sato and Manabu Tsukada},
year = {2024},
date = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Kambara2024,
title = {Geographic-Aware Network Analysis and Visualization System for CAVs},
author = {Koichi Kambara and Ehsan Javanmardi and Jin Nakazato and Shunya Yamada and Hiroaki Takada and Yousuke Watanabe and Kenya Sato and Manabu Tsukada},
year = {2024},
date = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Dolatabadi2024,
title = {Neural Error Covariance Estimation for Precise LIDAR Localization},
author = {Minoo Dolatabadi and Fardin Ayar and Ehsan Javanmardi and Manabu Tsukada and Mahdi Javanmardi},
url = {https://arxiv.org/abs/2501.02558},
year = {2024},
date = {2024-11-28},
urldate = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Zakerian2024,
title = {Unsupervised Person re-identification Using Generative Adversarial Networks},
author = {Romina Zakerian and Ehsan Javanmardi and Manabu Tsukada and Mahdi Javanmardi and Mohammad Rahmati},
year = {2024},
date = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Ayar2024,
title = {LiDAR-Camera Fusion for Video Panoptic Segmentation without Video Training},
author = {Fardin Ayar and Ehsan Javanmardi and Manabu Tsukada and Mahdi Javanmardi and Mohammad Rahmati},
url = {https://arxiv.org/abs/2412.20881},
year = {2024},
date = {2024-11-28},
urldate = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
abstract = {Panoptic segmentation, which combines instance and semantic segmentation, has gained a lot of attention in autonomous vehicles, due to its comprehensive representation of the scene. This task can
be applied for cameras and LiDAR sensors, but there has been a limited focus on combining both sensors to enhance image panoptic segmentation (PS). Although previous research has acknowledged the benefit of 3D data on camera-based scene perception, no specific study has explored the influence of 3D data on image and video panoptic segmentation (VPS). This work seeks to introduce a feature fusion module that enhances PS and VPS by fusing LiDAR and image data for autonomous vehicles. We also illustrate that, in
addition to this fusion, our proposed model, which utilizes two simple modifications, can further deliver even more high-quality VPS without being trained on video data. The results demonstrate a substantial improvement in both the image and video panoptic segmentation evaluation metrics by up to 5 points.},
note = {Best Paper Award (Bronze)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{nokey,
title = {Where Do You Go? Pedestrian Trajectory Prediction using Scene Features},
author = {Mohammad Ali Rezaei and Fardin Ayar and Ehsan Javanmardi and Manabu Tsukada and Mahdi Javanmardi},
url = {https://arxiv.org/abs/2501.13848},
year = {2024},
date = {2024-11-28},
urldate = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Li2024,
title = {Cross-Attention Enhanced Imitation Learning for End-to-end Autonomous Driving in Unprotected Turns},
author = {Dongyang Li and Ehsan Javanmardi and Naren Bao and Manabu Tsukada},
year = {2024},
date = {2024-11-28},
urldate = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
abstract = {Performing an unprotected turn in the intersection is a complex scenario for autonomous vehicles. It not only requires a comprehensive understanding of the surrounding environment but also highly relies on the ego vehicle’s current state to make safe decisions. A conventional way to learn end-to-end autonomous driving is imitation learning, which is learning from expert demonstrations. While most imitation learning methods focus on imitating the expert action, they often fail to imitate a complex policy efficiently when the ego vehicle’s states are crucial to the scenario because there might be arbitrary optimal actions under different states. To address this issue and investigate how vehicle states affect autonomous driving, we present a novel cross-attention enhanced imitation learning approach for end-to-end autonomous driving in unprotected turns, focusing on capturing the relationships between the ego vehicle’s states and its perception of the environment. We evaluate our model in AWSIM, an open-source autonomous driving
simulator, and the results demonstrate that our model outperformed conventional imitation learning-based baselines in performing unprotected turn scenarios, showcasing its ability to imitate a complex policy efficiently.},
note = {Best Paper Award (Silver)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Orsholits2024b,
title = {PLATONE: Assessing Simulation Accuracy of Environment-Dependent Audio Spatialization},
author = {Alex Orsholits and Eric Nardini and Tsukada Manabu},
year = {2024},
date = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Gui2024b,
title = {"Text + Eye" on Autonomous Taxi to Provide Geospatial Instructions to Passenger},
author = {Xinyue Gui and Ehsan Javanmardi and Stela Hanbyeol Seo and Vishal Chauhan and Chia-Ming Chang and Manabu Tsukada and Takeo Igarashi},
doi = {10.1145/3687272.3690906},
year = {2024},
date = {2024-11-24},
urldate = {2024-11-24},
booktitle = {Proceedings of the 12th International Conference on Human-Agent Interaction(HAI 2024)},
pages = {429-431},
address = {Swansea University, UK},
abstract = {While text-based external human-machine interface (eHMI) is widely accepted, one limitation is the lack of capability to communicate spatial information such as a different person or location. We built a mixed-eHMI using "eye" as a target-specifier when "text" shows the clear intention to their communication partners. We conducted a pre-experimental observation to develop two testbed scenarios, followed by a video-based user study via life-size projection with a real-car prototype mounted a text display and a set of robotic eyes. The results demonstrated that our proposed "text + eye" combination may represent geospatial information by increasing the success pick-up rate.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@online{AVVV2024,
title = {Autonomous Vehicle V2X Visualiser (AVVV)},
url = {https://github.com/tlab-wide/avvv_etsi
https://tlab-wide.github.io/avvv_etsi/},
year = {2024},
date = {2024-11-24},
abstract = {The AVVV project, standing for Autonomous Vehicle V2X Visualiser, aims to analyse and visualise V2X communications. V2X refers to the communications between the autonomous vehicle and everything else, including the road-side units (RSUs) and other intelligent vehicles (On-boar units or OBUs, for short).},
keywords = {},
pubstate = {published},
tppubtype = {online}
}
@article{Zhou2024,
title = {Cellular Connected UAV Anti-Interference Path Planning Based on PDS-DDPG and TOPEM},
author = {Quanxi Zhou and Yongjing Wang and Ruiyu Shen and Jin Nakazato and Manabu Tsukada and Zhenyu Guan},
doi = {10.1109/JMASS.2024.3490762},
issn = {2576-3164},
year = {2024},
date = {2024-11-04},
urldate = {2024-11-04},
journal = {IEEE Journal on Miniaturization for Air and Space Systems},
abstract = {Due to the randomness of channel fading, communication devices, and malicious interference sources, unmanned aerial vehicles (UAVs) face a complex and ever-changing task scenario, which poses significant communication security challenges, such as transmission outages. Fortunately, these communication security challenges can be transformed into path planning problems that minimize the weighted sum of UAV mission time and transmission outage time. In order to design the complex communication environment faced by UAVs in actual scenarios, we propose a system model, including building distribution, communication channel, and antenna design in this paper. Besides, we introduce other UAVs with fixed flight paths and ground interference resources with random locations to ensure mission UAVs have better anti-interference ability. However, it is challenging for classical search algorithms and heuristic algorithms to cope with the complex path problems mentioned above. In this paper, we propose an improved deep deterministic policy gradient (DDPG) algorithm with better performance compared with basic DDPG and DDQN algorithms. Specifically, a post-decision state (PDS) mechanism has been introduced to accelerate the convergence rate and enhance the stability of the training process. In addition, a transmission outage probability experience memory (TOPEM) has been designed to quickly generate wireless communication quality maps and provide temporary experience for the post-decision process, resulting in better training results. Simulation experiments have proven that, compared to basic DDPG, the improved algorithm increases training speed by at least 50%, significantly improves convergence rate, and reduces the episode required for convergence to 20%. It can also help UAVs choose better paths than basic DDPG and DDQN algorithms.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Grosset2024,
title = {Generation of V2X messages from Carla Simulator for cooperative perception: Application to pedestrian safety},
author = {Juliette Grosset and Jean-Marie Bonnin and Alain-Jérôme Fougères and Manabu Tsukada and Moise Djoko-Kouam},
doi = {10.1109/VTC2024-Fall63153.2024.10757467},
year = {2024},
date = {2024-10-07},
urldate = {2024-10-07},
booktitle = {The IEEE 100th Vehicular Technology Conference (VTC2024-Fall)},
address = {Washington DC, USA},
abstract = {Despite advancements in connected and autonomous vehicles (CAVs), vulnerable road users (VRUs) face a challenge as they lack Communication-Intelligent Transport System (C-ITS) equipment. This deficiency impedes their interaction with CAVs. We underscore the significance of Vehicle-to-Everything (V2X) communication in enhancing road safety with VRUs by facilitating information exchange between CAVs and the infrastructure. This communication is pivotal for reintegrating VRUs into the environmental awareness of CAVs. The Carla Simulator, used for autonomous vehicle training, currently lacks comprehensive V2X communication capabilities. In response, we propose an architecture for Carla, integrating OpenCDA and ROS2 to establish a simulated V2X network communication system for CAVs and roadside units (RSUs) within the Carla environment. This setup allows for the generation of V2X datasets and the refinement of algorithms for Advanced Driver Assistance Systems (ADAS). To illustrate and assess our proposed architecture, we present a scenario involving a pedestrian concealed in a blind spot for a connected vehicle.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@misc{Tsukada2024d,
title = {Cooperative Autonomous Mobility through Open Standards and Real-World Experiments},
author = {Manabu Tsukada},
url = {https://coop-intelligence.github.io/},
year = {2024},
date = {2024-09-30},
urldate = {2024-09-30},
address = {Milan, Italy},
abstract = {This talk explores how open standards and real-world experiments are driving the advancement of cooperative autonomous mobility. It highlights the transformative potential of integrating Vehicle-to-Everything (V2X) communication and Roadside Perception Units (RSPUs) to enhance autonomous driving capabilities. Through field tests and simulations, the presentation showcases the effectiveness of these systems in enabling cooperative perception and maneuver coordination, paving the way for a safer and more efficient transportation future. The emphasis on open standards underscores their crucial role in fostering interoperability and innovation within the intelligent transportation ecosystem.},
howpublished = {1st Workshop on Cooperative Intelligence for Embodied AI in The 18th European Conference on Computer Vision (ECCV 2024 Workshop)},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
@inproceedings{Yun2024,
title = {Large Language Models for Human-like Autonomous Driving Decision Making: A Survey},
author = {Yun Li and Kai Katsumata and Ehsan Javanmardi and Manabu Tsukada},
doi = {10.1109/ITSC58415.2024.10919629},
year = {2024},
date = {2024-09-24},
urldate = {2024-09-24},
booktitle = {27th IEEE International Conference on Intelligent Transportation Systems (ITSC 2024)},
address = {Edmonton, Canada},
abstract = {Large Language Models (LLMs), AI models trained on massive text corpora with remarkable language understanding and generation capabilities, are transforming the field of Autonomous Driving (AD). As AD systems evolve from rule-based and optimization-based methods to learning-based techniques like deep reinforcement learning, they are now poised to embrace a third and more advanced category: knowledge-based AD empowered by LLMs. This shift promises to bring AD closer to human-like AD. However, integrating LLMs into AD systems poses challenges in real-time inference, safety assurance, and deployment costs. This survey provides a comprehensive and critical review of recent progress in leveraging LLMs for AD, focusing on their applications in modular AD pipelines and end- to-end AD systems. We highlight key advancements, identify pressing challenges, and propose promising research directions to bridge the gap between LLMs and AD, thereby facilitating the development of more human-like AD systems. The survey first introduces LLMs’ key features and common training schemes, then delves into their applications in modular AD pipelines and end-to-end AD, respectively, followed by discussions on open challenges and future directions. Through this in-depth analysis, we aim to provide insights and inspiration for researchers and practitioners working at the intersection of AI and autonomous vehicles, ultimately contributing to safer, smarter, and more human-centric AD technologies.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Jiang2024b,
title = {Accurate Cooperative Localization Utilizing LiDAR-equipped Roadside Infrastructure for Autonomous Driving},
author = {Yuze Jiang and Ehsan Javanmardi and Manabu Tsukada and Hiroshi Esaki},
url = {https://arxiv.org/abs/2407.08384 },
doi = {10.1109/ITSC58415.2024.10920101},
year = {2024},
date = {2024-09-24},
urldate = {2024-09-24},
booktitle = {27th IEEE International Conference on Intelligent Transportation Systems (ITSC 2024)},
address = {Edmonton, Canada},
abstract = {Recent advancements in LiDAR technology have significantly lowered costs and improved both its precision and resolution, thereby solidifying its role as a critical component in autonomous vehicle localization. Using sophisticated 3D reg- istration algorithms, LiDAR now facilitates vehicle localization with centimeter-level accuracy. However, these high-precision techniques often face reliability challenges in environments devoid of identifiable map features. To address this limitation, we propose a novel approach that utilizes road side units (RSU) with vehicle-to-infrastructure (V2I) communications to assist vehicle self-localization. By using RSUs as stationary reference points and processing real-time LiDAR data, our method enhances localization accuracy through a cooperative localization framework. By placing RSUs in critical areas, our proposed method can improve the reliability and precision of vehicle localization when the traditional vehicle self-localization technique falls short. Evaluation results in an end-to-end autonomous driving simulator AWSIM show that the proposed method can improve localization accuracy by up to 80% under vulnerable environments compared to traditional localization methods. Additionally, our method also demonstrates robust resistance to network delays and packet loss in heterogeneous network environments.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Gui2024,
title = {Shrinkable Arm-based eHMI on Autonomous Delivery Vehicle for Effective Communication with Other Road Users},
author = {Xinyue Gui and Mikiya Kusunoki and Bofei Huang and Stela Hanbyeol Seo and Chia-Ming Chang and Haoran Xie and Manabu Tsukada and Takeo Igarashi},
doi = {10.1145/3640792.3675716},
year = {2024},
date = {2024-09-22},
urldate = {2024-09-22},
booktitle = {16th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI 2024)},
address = {California, USA},
abstract = {When employing autonomous driving technology in logistics, small autonomous delivery vehicles (aka delivery robots) encounter challenges different from passenger vehicles when interacting with other road users. We conducted an online video survey as a pre-study and found that autonomous delivery vehicles need external human-machine interfaces (eHMIs) to ask for help due to their small size and functional limitations. Inspired by everyday human communication, we chose arms as eHMI to show their request through limb motion and gesture. We held an in-house workshop to identify the arm’s requirements for designing a specific arm with shrink-ability (conspicuous when delivering messages but not affect traffic at other times). We prototyped a small delivery robot with a shrinkable arm and filmed the experiment videos. We conducted two studies (a video-based and a 360-degree-photo VR-based) with 18 participants. We demonstrated that arm-on-delivery robots can increase interaction efficiency by drawing more attention and communicating specific information.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Chauhan2024,
title = {Transforming Pedestrian and Autonomous Vehicles Interactions in Shared Spaces: A Think-Tank Study on Exploring Human-Centric Designs},
author = {Vishal Chauhan and Anubhav Anubhav and Chia-Ming Chang and Jin Nakazato and Ehsan Javanmardi and Alex Orsholits and Takeo Igarashi and Kantaro Fujiwara and Manabu Tsukada
},
doi = {10.1145/3641308.3685037},
year = {2024},
date = {2024-09-22},
urldate = {2024-09-22},
booktitle = {16th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI 2024), Work in Progress (WiP)},
pages = {1-8},
address = {California, USA},
abstract = {Our research focuses on the smart pole interaction unit (SPIU) as an infrastructure external human-machine interface (HMI) to enhance pedestrian interaction with autonomous vehicles (AVs) in shared spaces. We extensively study SPIU with external human-machine interfaces (eHMI) on AVs as an integrated solution. To discuss interaction barriers and enhance pedestrian safety, we engaged 25 participants aged 18-40 to brainstorm design solutions for pedestrian-AV interactions, emphasising effectiveness, simplicity, visibility, and clarity. Findings indicate a preference for real-time SPIU interaction over eHMI on AVs in multiple AV scenarios. However, the combined use of SPIU and eHMI on AVs is crucial for building trust in decision-making. Consequently, we propose innovative design solutions for both SPIU and eHMI on AVs, discussing their pros and cons. This study lays the groundwork for future autonomous mobility solutions by developing human-centric eHMI and SPIU prototypes as ieHMI.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@misc{Tsukada2024c,
title = {Integrating Infrastructure-Assisted V2X Communication for Enhanced Cooperative Autonomous Driving},
author = {Manabu Tsukada},
url = {https://www.iiotbdsc.net/},
year = {2024},
date = {2024-09-21},
abstract = {This presentation delves into the integration of smart infrastructure with Vehicle-to-Everything (V2X) communication to advance the safety and intelligence of Cooperative Autonomous Driving (CAD). We will explore how roadside sensors and V2X communication enhance vehicles' environmental awareness, enabling them to make safer and more informed decisions. The session will highlight key projects such as AutowareV2X and AutoMCM, demonstrating the potential of coordinated vehicle interactions to improve driving efficiency and safety. Furthermore, the talk will discuss the role of roadside LiDAR in providing precise localization, even in challenging environments. By showcasing the synergy of these cutting-edge technologies, the presentation aims to illustrate a future of safer, more connected, and efficient autonomous driving.},
howpublished = {Invited talk at the 5th International Conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC 2024)},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
@article{nokey,
title = {A Survey on Recent Advancements in Autonomous Driving Using Deep Reinforcement Learning: Applications, Challenges, and Solutions},
author = {Rui Zhao and Yun Li and Yuze Fan and Fei Gao and Manabu Tsukada and Zhenhai Gao},
doi = {10.1109/TITS.2024.3452480},
isbn = {1524-9050},
year = {2024},
date = {2024-09-18},
urldate = {2024-09-18},
journal = {IEEE Transactions on Intelligent Transportation Systems},
volume = {25},
issue = {2},
pages = {19365 - 19398},
abstract = {Autonomous driving (AD) endows vehicles with the capability to drive partly or entirely without human intervention. AD agents generate driving policies based on online perception results, which are crucial to the realization of safe, efficient, and comfortable driving behaviors, particularly in high-dimensional and stochastic traffic scenarios. Currently, deep reinforcement learning (DRL) techniques to derive and validate AD policies have witnessed vast research efforts and have shown rapid development in recent years. However, a comprehensive interpretation and evaluation of their strengths and limitations concerning the full-stack AD tasks remain uncharted. This paper presents a survey of this body of work, which is conducted at three levels. First, it analyzes the multi-level AD task characteristics and delves deeply into the current DRL methodologies primarily employed in AD. Second, a taxonomy of the literature studies is constructed from the system perspective, identifying six modes of DRL model integration into an AD architecture that span the entire spectrum of AD policy processes, from perception understanding and decision-making to motion control, as well as verification and validation. Each literature review comprehensively encompasses the main elements of designing such a system, including modeling partially observable environments, state and action spaces, reward structuring, and the design and training methodologies of neural network models. Finally, an in-depth foresight is conducted on how the eight critical issues of AD application development are addressed by the DRL models tailored for real-world AD challenges.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Trumpp2024,
title = {RaceMOP: Mapless Online Path Planning for Multi-Agent Autonomous Racing using Residual Policy Learning},
author = {Raphael Trumpp and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada and Marco Caccamo},
url = {http://github.com/raphajaner/racemop},
doi = {10.1109/IROS58592.2024.10801657},
year = {2024},
date = {2024-09-14},
urldate = {2024-09-14},
booktitle = {The 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)},
address = {Abu Dhabi ,UAE},
abstract = {The interactive decision-making in multi-agent autonomous racing offers insights valuable beyond the domain of self-driving cars. Mapless online path planning is particularly of practical appeal but poses a challenge for safely overtaking opponents due to the limited planning horizon. Accordingly, this paper introduces RaceMOP, a novel method for mapless online path planning designed for multi-agent racing of F1TENTH cars. Unlike classical planners that depend on predefined racing lines, RaceMOP operates without a map, relying solely on local observations to overtake other race cars at high speed. Our approach combines an artificial potential field method as a base policy with residual policy learning to introduce long-horizon planning capabilities. We advance the field by introducing a novel approach for policy fusion with the residual policy directly in probability space. Our experiments for twelve simulated racetracks validate that RaceMOP is capable of long-horizon decision-making with robust collision avoidance during over- taking maneuvers. RaceMOP demonstrates superior handling over existing mapless planners while generalizing to unknown racetracks, paving the way for further use of our method in robotics. We make the open-source code for RaceMOP available at http://github.com/raphajaner/racemop.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Asabe2024,
title = {Enhancing Reliability in Infrastructure-based Collective Perception: A Dual-Channel Hybrid Delivery Approach with Real-Time Monitoring},
author = {Yu Asabe and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
doi = {10.1109/OJVT.2024.3443877},
issn = {2644-1330},
year = {2024},
date = {2024-08-30},
urldate = {2024-08-30},
journal = {IEEE Open Journal of Vehicular Technology},
volume = {5},
pages = {1124-1138},
abstract = {Standalone autonomous vehicles primarily rely on their onboard sensors and may have blind spots or limited situational awareness in complex or dynamic traffic scenarios, leading to difficulties in making safe decisions. Collective perception enables connected autonomous vehicles (CAVs) to overcome the limitations of standalone autonomous vehicles by sharing sensory information with nearby road users. However, unfavorable conditions of the wireless communication medium it uses can lead to limited reliability and reduced quality of service. In this paper, we propose methods for increasing the reliability of collective perception through real-time packet delivery rate monitoring and a dual-channel hybrid delivery approach. We have implemented AutowareV2X, a vehicle-to-everything (V2X) communication module integrated into the autonomous driving (AD) software Autoware. AutowareV2X provides connectivity to the AD stack, enabling end-to-end (E2E) experimentation and evaluation of CAVs. The Collective Perception Service (CPS) was also implemented, allowing the transmission of Collective Perception Messages (CPMs). Our proposed methods using AutowareV2X were evaluated using actual hardware and vehicles in reallife field tests. Results have indicated that the E2E network latency of the perception information sent is around 30 ms, and the AD software can use shared object data to conduct collision avoidance maneuvers. The dual-channel delivery of CPMs enabled the CAV to dynamically select the best CPM from CPMs received from different links, depending on the freshness of their information. This enabled the reliable transmission of CPMs even when there was significant packet loss on one of the transmitting channels.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Orsholits2024,
title = {PLATONE: An Immersive Geospatial Audio Spatialization Platform},
author = {Alex Orsholits and Yiyuan Qian and Eric Nardini and Yusuke Obuchi and Manabu Tsukada},
doi = {10.1109/MetaCom62920.2024.00020},
year = {2024},
date = {2024-08-12},
urldate = {2024-08-12},
booktitle = {The 2nd Annual IEEE International Conference on Metaverse Computing, Networking, and Applications (MetaCom 2024)},
address = {Hong Kong, China},
abstract = {In the rapidly evolving landscape of mixed reality (MR) and spatial computing, the convergence of physical and virtual spaces is becoming increasingly crucial for enabling immersive, large-scale user experiences and shaping inter-reality dynamics. This is particularly significant for immersive audio at city-scale, where the 3D geometry of the environment must be considered, as it drastically influences how sound is perceived by the listener. This paper introduces PLATONE, a novel proof-of-concept MR platform designed to augment urban contexts with environment-dependent spatialized audio. It leverages custom hardware for localization and orientation, alongside a cloud-based pipeline for generating real-time binaural audio. By utilizing open-source 3D building datasets, sound propagation effects such as occlusion, reverberation, and diffraction are accurately simulated. We believe that this work may serve as a compelling foundation for further research and development.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Takada2024,
title = {Design of Digital Twin Architecture for 3D Audio Visualization in AR},
author = {Tokio Takada and Jin Nakazato and Alex Orsholits and Manabu Tsukada and Hideya Ochiai and Hiroshi Esaki},
doi = {10.1109/MetaCom62920.2024.00044},
year = {2024},
date = {2024-08-12},
urldate = {2024-08-12},
booktitle = {The 2nd Annual IEEE International Conference on Metaverse Computing, Networking, and Applications (MetaCom 2024)},
address = {Hong Kong, China},
abstract = {Digital twins have recently attracted attention from academia and industry as a technology connecting physical space and cyberspace. Digital twins are compatible with Augmented Reality (AR) and Virtual Reality (VR), enabling us to understand information in cyberspace. In this study, we focus on music and design an architecture for a 3D representation of music using a digital twin. Specifically, we organize the requirements for a digital twin for music and design the architecture. We establish a method to perform 3D representation in cyberspace and map the recorded audio data in physical space. In this paper, we implemented the physical space representation using a smartphone as an AR device and employed a visual positioning system (VPS) for self-positioning. For evaluation, in addition to system errors in the 3D representation of audio data, we conducted a questionnaire evaluation with several users as a user study. From these results, we evaluated the effectiveness of the implemented system. At the same time, we also found issues we need to improve in the implemented system in future works.},
key = {CREST},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Lin2024b,
title = {Clothoid Curve-based Emergency Stopping Path-Planning with Adaptive Potential Field for Autonomous Vehicles},
author = {Pengfei Lin and Ehsan Javanmardi and Manabu Tsukada},
doi = {10.1109/TVT.2024.3380745},
issn = {0018-9545},
year = {2024},
date = {2024-07-24},
urldate = {2024-03-22},
journal = {IEEE Transactions on Vehicular Technology},
volume = {73},
issue = {7},
pages = {9747-9762},
abstract = {Potential Field-based path planning methods are widely embraced in the context of autonomous vehicles due to their real-time efficiency and simplicity. While the potential field effectively enforces a rigid road boundary to keep the vehicle within the confines of the road, it can lead to the “blind alley” problem caused by local minima in specific high- speed scenarios, resulting in indecision, erratic behavior, or even accidents. Therefore, the objective of this research is to anticipate and address the aforementioned problem in order to proactively avoid potential collisions. We have also found that existing methods do not offer a root cause analysis or practical solutions for this issue, which limits the practicality of the potential field in handling complicated traffic situations. In this paper, we propose an Emergency-Stopping Path Planning (ESPP) approach that incorporates an adaptive potential field with the clothoid curve. First, we design an emergency triggering estimation to detect the ”blind alley” problem. Second, we regionalize the driving scene to search for the optimal breach point on the road PF and the final stopping point for the vehicle by considering the motion range of the obstacle. Finally, we use the optimized clothoid curve to fit these calculated points under vehicle dynamics constraints to generate a smooth emergency avoidance path. The proposed ESPP method was evaluated by conducting the co-simulation between MATLAB/Simulink and CarSim Simulator in a freeway scene. The simulation results reveal that the proposed method shows increased performance in emergency collision avoidance and renders the vehicle safer, in which the duration of wheel slip is 61.9% shorter, and the maximum steering angle amplitude is 76.9% lower than other potential field-based methods.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Iwaki2024b,
title = {Enhancing V2X Communication: Machine Learning Assisted Dynamic mmWave Beam Search},
author = {Ryo Iwaki and Jin Nakazato and Kazuki Maruta and Manabu Tsukada and Hideya Ochiai and Hiroshi Esaki},
doi = {10.1109/ICUFN61752.2024.10625435},
year = {2024},
date = {2024-07-02},
urldate = {2024-07-02},
booktitle = {The 15th International Conference on Ubiquitous and Future Networks (ICUFN2024)},
address = {Budapest, Hungary},
abstract = {Only the chairs can edit This paper addresses the challenges of dynamic beam search in millimeter wave (mmWave) communications for vehicle-to-everything (V2X) applications. With the rapid mobility of connected autonomous vehicles (CAVs) and dense urban environments, maintaining high-quality mmWave connections is critical for the reliability and efficiency of V2X communications. We propose a novel machine learning-assisted framework for dynamic mmWave beam search, which significantly enhances the adaptability and performance of V2X communication systems. Our approach leverages real-time environmental data and CAV dynamics to predict optimal beam directions, improving connection stability. Simulation results demonstrate the effectiveness of the proposed method in a real-world road scenario, offering a partial improvement over conventional beam search techniques.},
note = {Best Paper Award},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Yoshimura2024,
title = {Towards Robust Communication in ITS: A Comprehensive Study of Blockchain for V2I},
author = {Atsuki Yoshimura and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
doi = {10.1109/ICUFN61752.2024.10624948},
year = {2024},
date = {2024-07-02},
urldate = {2024-07-02},
booktitle = {The 15th International Conference on Ubiquitous and Future Networks (ICUFN2024)},
address = {Budapest, Hungary},
abstract = {V2X in connected autonomous vehicles (CAV) plays an important role in information sharing through communication. The integration of V2X and blockchain has the potential to create functionalities such as seamless V2X information sharing, similar to Bitcoin, and post-accident investigation utilities that leverage data immutability. However, the integration of blockchain into V2X communication requires addressing CAV mobility. In this study, we propose a framework that takes into account the high mobility of CAVs. Furthermore, we propose a method that not only addresses this challenge but also achieves load balancing by facilitating cooperation among nodes responsible for member management. In this paper, we integrate the ITS simulator, the communication simulator, and the blockchain simulator to build an infrastructure that can be evaluated end-to-end. Using the integrated simulator, we perform an evaluation based on metrics such as latency and member change rate in a mobile environment with a single roadside unit (RSU). In the future, we plan to implement the proposed methodology and perform evaluations in environments with multiple RSUs.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Chi2024,
title = {V2I Blockage Modeling and Performance Evaluation for Connected Autonomous Vehicle},
author = {Weiqi Chi and Jin Nakazato and Tomoki Murakami and Manabu Tsukada},
doi = {10.1109/VTC2024-Spring62846.2024.10683381},
year = {2024},
date = {2024-06-24},
urldate = {2024-06-24},
booktitle = {The IEEE 99th Vehicular Technology Conference (VTC2024-Spring)},
address = {Singapore},
abstract = {The burgeoning Intelligent Transportation System (ITS) spurs global technological advancements, notably in innovative community development through vehicle-to-everything (V2X) communication. This study focuses on the high data rates and low latency offered by a millimeter-wave (mmWave) enabled vehicular network while addressing the significant challenge of link quality degradation due to blockages, exacerbated by the mmWave band’s small wavelength in high mobility and traffic conditions. We propose an RSU-assisted ITS system tailored for multi-lane, straight-road scenarios, effectively identifying blockage status for vehicles. Combining Simulation of Urban Mobility (SUMO) and MATLAB, this blockage-aware scheme lays the groundwork for future ITS enhancements. The research also delves into the effects of various frequency bands, vehicle types, and communication ranges, offering a holistic system performance analysis.},
note = {IEEE VTS Tokyo/Japan Chapter Young Researcher's Encouragement Award},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@workshop{Ozawa2024b,
title = {Toward O-RAN-based Cell-Free Architecture: Cooperative O-RU/V2X mmWave Beam Tracking},
author = {Sojin Ozawa and Yuki Sasaki and Jin Nakazato and Manabu Tsukada and Kazuki Maruta},
year = {2024},
date = {2024-06-24},
urldate = {2024-06-24},
booktitle = {Technologies and Proof-of-Concept Activities for 6G 2024 (TPoC6G 2024) at The IEEE 99th Vehicular Technology Conference (VTC2024-Spring)},
address = {Singapore},
abstract = {Coordination of connected autonomated vehicles (CAVs) is expected to provide a more efficient sequential route design and enhance safety. This is accomplished by sharing sensor data among roadside equipment and other vehicles. Given the substantial volume of sensor data involved, it is advantageous to employ millimeter-wave (mmWave) band. MmWave offers high-speed and large-capacity for transmission. However, wireless communication systems designed for CAVs face the challenge of radio signal degradation caused by the movement of vehicles. This paper proposes a fast beam tracking Open Radio Access Network (O-RAN) architecture for CAVs. The most prominent aspect of this system is its Near-Realtime (Near-RT) RAN Intelligent Controller (RIC), which swiftly adjusts and tracks the beam using vehicle information transmitted every 100 msec by CAV. By conducting simulations using Simulation of Urban Mobility (SUMO), which emulates vehicle movement on various roads, we verified the effective operation of the proposed architechture.
},
note = {IEEE VTS Tokyo/Japan Chapter Young Researcher's Encouragement Award},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@inproceedings{Ito2024,
title = {Enhancing Real-Time Streaming Quality through a Multipath Redundant Communication Framework},
author = {Koki Ito and Jin Nakazato and Romain Fontugne and Manabu Tsukada and Hiroshi Esaki},
doi = {10.23919/IFIPNetworking62109.2024.10619885},
year = {2024},
date = {2024-06-03},
urldate = {2024-06-03},
booktitle = {IFIP/IEEE Networking 2024},
address = {Thessaloniki, Greece},
abstract = {Recently, as networks operate as the infrastructure of modern society, the demands placed on the network by applications have become more complex. In particular, an increasing annual demand for high-capacity and low-latency services, including real-time streaming. 5G services have been launched to meet this demand, but their stability varies de- pending on location and time and can only sometimes be considered sufficient. One method to improve communication stability is multipath redundant communication, and much research has been conducted in this area. However, most of this research has focused on TCP-based communication and cannot be applied to real-time UDP streaming. Hence, we propose a multipath redundant communication framework to improve the quality of real-time media streaming communication. Tunneling at the IP layer in our proposed framework was performed to overcome the limitations of transport layer protocols, which was a challenge for traditional multipath redundant communication systems. Furthermore, to address the packet order inconsistency caused by multipath redundant communication, a buffering mechanism was implemented on the receiving side of our system. Our proposed system was verified using multipath redundant communication and multiple mobile networks from a vehicle moving in an urban area. The experiments used a real-time streaming application based on WebRTC, and the framework significantly reduced packet loss and improved bitrate compared to existing multipath redundant communication systems without interfering with the application’s congestion control.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tao2024b,
title = {Zero-Knowledge Proof of Distinct Identity: a Standard-compatible Sybil-resistant Pseudonym Extension for C-ITS},
author = {Ye Tao and Hongyi Wu and Ehsan Javanmardi and Manabu Tsukada and Hiroshi Esaki},
url = {https://arxiv.org/abs/2403.14020},
doi = {10.1109/IV55156.2024.10588511},
year = {2024},
date = {2024-06-02},
urldate = {2024-06-02},
booktitle = {35th IEEE Intelligent Vehicles Symposium (IV2024)},
address = {Jeju Island, Korea},
abstract = {Pseudonyms are widely used in Cooperative Intelligent Transport Systems (C-ITS) to protect the location privacy of vehicles. However, the unlinkability nature of pseudonyms also enables Sybil attacks, where a malicious vehicle can pretend to be multiple vehicles at the same time. In this paper, we propose a novel protocol called zero-knowledge Proof of Distinct Identity (zk-PoDI,) which allows a vehicle to prove that it is not the owner of another pseudonym in the local area, without revealing its actual identity. Zk-PoDI is based on the Diophantine equation and zk-SNARK, and does not rely on any specific pseudonym design or infrastructure assistance. We show that zk-PoDI satisfies all the requirements for a practical Sybil-resistance pseudonym system, and it has low latency, adjustable difficulty, moderate computation overhead, and negligible communication cost. We also discuss the future work of implementing and evaluating zk-PoDI in a realistic city-scale simulation environment.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@misc{Tsukada2024b,
title = {Integrating Infrastructure-Assisted V2X Communication for Next-Generation Cooperative Autonomous Driving},
author = {Manabu Tsukada},
url = {https://www.fzi.de/en/veranstaltungen/iv-workshop-intelligent-infrastructure-and-uav/},
year = {2024},
date = {2024-06-02},
urldate = {2024-06-02},
abstract = {This talk explores how combining smart road technology and vehicle communication (V2X) can make self-driving cars (CAD) safer and smarter. It will discuss how roadside sensors and V2X can help cars understand their surroundings better and make safer decisions. The talk will also introduce projects like AutowareV2X and AutoMCM, showing how cars working together can make driving smoother and safer. Additionally, it will cover how roadside LiDAR can help cars know exactly where they are, even in tough situations. Overall, the presentation will show how these advanced technologies work together to make future driving safer, more connected, and efficient.},
howpublished = {Invited talk at 1st Workshop on Application of Intelligent Infrastructure for Automated Driving as part of the IEEE IV Symposium 2024},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
@article{electronics13112037,
title = {A State-Interactive MAC Layer TDMA Protocol Based on Smart Antennas},
author = {Donghui Li and Jin Nakazato and Manabu Tsukada},
url = {https://www.mdpi.com/2079-9292/13/11/2037},
doi = {10.3390/electronics13112037},
issn = {2079-9292},
year = {2024},
date = {2024-05-23},
urldate = {2024-01-01},
journal = {Electronics},
volume = {13},
number = {11},
abstract = {Mobile ad hoc networks are self-organizing networks that do not rely on fixed infrastructure. Smart antennas employ advanced beamforming technology, enabling ultra-long-range directional transmission in wireless networks, which leads to lower power consumption and better utilization of spatial resources. The media access control (MAC) protocol design using smart antennas can lead to efficient usage of channel resources. However, during ultra-long-distance transmissions, there may be significant transport delays. In addition, when using the time division multiple access (TDMA) schemes, it can be difficult to manage conflicts arising from adjacent time slot advancement caused by latency compensation in ultra-long-range propagation. Directional transmission and reception can also cause interference between links that reuse the same time slot. This paper proposes a new distributed dynamic TDMA protocol called State Interaction-based Slot Allocation Protocol (SISAP) to address these issues. This protocol is based on slot states and includes TDMA frame structure, slot allocation process, interference self-avoidance strategy, and slot allocation algorithms. According to the simulation results, the MAC layer design scheme suggested in this paper can achieve ultra-long-distance transmission without conflicts. Additionally, it can reduce the interference between links while space multiplexing. Furthermore, the system exhibits remarkable performance in various network aspects, such as throughput and link delay.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@workshop{Suzuki2024,
title = {Toward B5G/6G Connected Autonomous Vehicles: O-RAN-Driven Millimeter-Wave Beam Management and Handover Management},
author = {Kengo Suzuki and Jin Nakazato and Yuki Sasaki and Kazuki Maruta and Manabu Tsukada and Hiroshi Esaki},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/378439256_Toward_B5G6G_Connected_Autonomous_Vehicles_O-RAN-Driven_Millimeter-Wave_Beam_Management_and_Handover_Management/links/664b976f0b0d2845744b1ea8/Toward-B5G-6G-Connected-Autonomous-Vehicles-O-RAN-Driven-Millimeter-Wave-Beam-Management-and-Handover-Management.pdf},
year = {2024},
date = {2024-05-20},
urldate = {2024-05-20},
booktitle = {IEEE INFOCOM WKSHPS, Next-generation Open and Programmable Radio Access Networks (NG-OPERA 2024)},
address = {Vancouver, Canada},
abstract = {Connected autonomous vehicles (CAVs) are crucial to a future society that embraces advanced technologies. For these vehicles to effectively share information with nearby vehicles or infrastructures through vehicle-to-everything (V2X) interfaces, stable mmWave communication is essential yet challenging. This paper presents an innovative approach to millimeter-wave beam management for CAVs, utilizing open radio access network (O- RAN) architecture to improve beam and handover efficiency in diverse road scenarios. Our approach uniquely combines CAV application data with mobility management to predict vehicles’ location. Our findings show that this method substantially surpasses the traditional beam sweeping method by consistently maintaining a higher signal-to-noise ratio (SNR), even in challenging scenarios such as vehicle obstruction at intersections. This research underscores the potential of integrating O-RAN with vehicle-to-infrastructure (V2I) communication in CAVs, paving the way for future advancements in autonomous transportation technology.},
note = {Best Paper Award},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@misc{Tsukada2024,
title = {V2X and Beyond: Integrating Roadside Units for Next-Level Cooperative Automated Driving},
author = {Manabu Tsukada},
url = {https://www.pervehicle.org/},
year = {2024},
date = {2024-03-15},
urldate = {2024-03-15},
abstract = {As the frontier of vehicular technology continues to expand, the integration of cooperative systems, particularly through Vehicle-to-Everything (V2X) communication and Roadside Perception Units (RSPUs), emerges as a pivotal force in redefining road safety, efficiency, and vehicular autonomy. This talk delves into the transformative potential of networked RSPUs in enhancing autonomous driving capabilities, underpinned by the robust foundation of V2X communication. It explores the innovative concept of infrastructure-assisted vehicular communication, spotlighting the pivotal role of RSPUs in facilitating cooperative perception – a critical component for achieving comprehensive situational awareness and bolstering the cooperative intelligent transport system (ITS). By harnessing the synergy of V2X and advanced middleware, such as AutowareV2X and AutoMCM, the discourse presents a comprehensive overview of the seamless integration of autonomous vehicles within the intelligent vehicular ecosystem. Through a series of rigorous field tests and simulation experiments, the talk showcases the efficacy of these advanced systems, highlighting their proficiency in delivering vital messages with minimal latency, even under challenging traffic conditions. Moreover, it underscores the significance of collective perception and maneuver coordination in optimizing vehicular operations, thereby paving the way for a future where autonomous vehicles and intelligent infrastructure coalesce to form a harmonious, safe, and highly efficient transportation network. The insights shared in this talk not only reflect a deep understanding of current technological advancements but also chart a course for future innovations in the realm of pervasive computing for vehicular systems.},
howpublished = {Keynote speech at 6th International Workshop on Pervasive Computing for Vehicular Systems (PerVehicle) 2024},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
@workshop{Hu2024,
title = {A Research of Kalman Filter enabled Beam Tracking for Multiple Vehicles},
author = {Dou Hu and Jin Nakazato and Ehsan Javanmardi and Muhammad Asad and Maruta Kazuki and Manabu Tsukada},
url = {https://www.ieice.org/publications/proceedings/bin/pdf_link.php?fname=15.pdf&iconf=ASPIRE_WS&year=2024&vol=80&number=P-15&lang=E?.pdf},
doi = {10.34385/proc.80.P-15},
isbn = {2188-5079},
year = {2024},
date = {2024-03-05},
urldate = {2024-03-05},
booktitle = {ASPIRE Workshop 2024 in conjunction with the IEICE General Conference},
address = {Hiroshima, Japan},
abstract = {In the era of Beyond 5G, the significance of interdisciplinary research has become increasingly important. Within this context, the Kalman filter, a technology integral to self-positioning estimation in autonomous driving, is already being adopted in various societal applications. This study proposes a method wherein beam tracking, in conjunction with the Kalman filter, is an alternative to GPS in specific scenarios. This research is particularly relevant in environments such as intersections flanked by high-rise buildings, where GPS signals are prone to interference.
},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@workshop{Sasaki2024b,
title = {Beam-Space Expansion with Broad-Range Null-Steering for V2I Multiuser MIMO Transmission},
author = {Yuki Sasaki and Sojin Ozawa and Kabuto Arai and Jin Nakazato and Manabu Tsukada and Kazuki Maruta},
url = {https://www.ieice.org/publications/proceedings/bin/pdf_link.php?fname=8.pdf&iconf=ASPIRE_WS&year=2024&vol=80&number=P-8&lang=E?.pdf},
doi = {10.34385/proc.80.P-8},
isbn = {2188-5079},
year = {2024},
date = {2024-03-05},
urldate = {2024-03-05},
booktitle = {ASPIRE Workshop 2024 in conjunction with the IEICE General Conference},
address = {Hiroshima, Japan},
abstract = {This paper proposes a novel inter-user interference (IUI) suppression approach in multiuser MIMO (MU-MIMO) with time-varying channel environments. It introduces location based beam-space expansion (BSE) scheme for vehicle-to-infrastructure (V2I) MU-MIMO transmission. This scheme expands the main lobe of the beam based on vehicle location information. MU-MIMO enables to enhance spectral efficiency in V2I by multiplexing a number of user terminals (UTs) in spatial domain. However, UTs move at high speed, which causes IUI. Null-space expansion (NSE) and broad-range null-steering (BRNS) improves IUI suppression capability by only steering nulls to interfering users. The proposed BSE expands the beam for the desired user. The performance of NSE, BRNS and BSE are compared through computer simulation to show the effectiveness of BSE.},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@workshop{Ozawa2024,
title = {Laboratory Experiment of Broad-Range Null-Steering for Millimeter-Wave V2I Multiuser MIMO},
author = {Sojin Ozawa and Yuki Sasaki and Ryo Iwaki and Jin Nakazato and Manabu Tsukada and Kazuki Maruta
},
url = {https://www.ieice.org/publications/proceedings/bin/pdf_link.php?fname=19.pdf&iconf=ASPIRE_WS&year=2024&vol=80&number=P-19&lang=E?.pdf},
doi = {10.34385/proc.80.P-19},
issn = {2188-5079},
year = {2024},
date = {2024-03-05},
urldate = {2024-03-05},
booktitle = {ASPIRE Workshop 2024 in conjunction with the IEICE General Conference},
address = {Hiroshima, Japan},
abstract = {This paper demonstrates the practical effectiveness of our proposed broad-range null-steering (BRNS) scheme through indoor experiment using 28-GHz band 4-element array antenna transmission system.For realization of cooperative automated driving, use of millimeter-wave band and multiuser multiple-input multiple-output (MIMO) are essential. However, inter-user interference is an challenging issue to perform spatial multiplexing under mobility environment due to outdated channel state information (CSI). BRNS was previously proposed that additionally nullifies around interfering users based on geometrical information of vehicles that can be obtained through cooperative awareness message (CAM). Its effectiveness have been confirmed through computer simulations, supposing cooperative automated operation in V2X. Our laboratory-experimental result confirms null-steering range can be expanded.},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@article{Asad2024,
title = {Secure and Efficient Blockchain-based Federated Learning Approach For VANETs},
author = {Muhammad Asad and Saima Shaukat and Ehsan Javanmardi and Jin Nakazato and Naren Bao and Manabu Tsukada},
doi = {10.1109/JIOT.2023.3322221},
issn = {2327-4662},
year = {2024},
date = {2024-03-01},
urldate = {2023-10-05},
journal = {IEEE Internet of Things Journal},
volume = {11},
issue = {5},
pages = {9047-9055},
abstract = {The rapid increase in the number of connected vehicles on roads has made Vehicular Ad-hoc Networks (VANETs) an attractive target for malicious actors. As a result, VANETs require secure data transmission to maintain the network’s integrity. Federated Learning (FL) has been proposed as a secure data-sharing method for VANETs, but it is limited in its ability to protect sensitive data. This paper proposes integrating Blockchain technology into FL to provide an additional layer of security for VANETs. In particular, we propose a Secure and Efficient Blockchain-based FL (SEBFL) approach to ensure communication efficiency and data privacy in VANETs. To this end, we use the FL model for VANETs, where computation tasks are decomposed from a base station to individual vehicles. This effectively reduces the congestion delay and communication overhead. Integrating blockchain with the FL model provides a reliable and secure data communication system between vehicles, roadside units, and a cloud server. Additionally, we use a Homomorphic Encryption System (HES) that effectively preserves the confidentiality and credibility of vehicles. Besides, the proposed SEBFL leverages the asynchronous FL model, minimizing the long delay while avoiding possible threats and attacks using HES. The experiment results show the proposed SEBFL achieves 0.87% accuracy while a model inversion attack and 0.86% accuracy while a membership inference attack.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Maruta2024,
title = {Millimeter-Wave Fast Beam Tracking Enabled by RAN/V2X Cooperation},
author = {Kazuki Maruta and Jin Nakazato and Kengo Suzuki and Dou Hu and Ryo Iwaki and Sojin Ozawa and Yuki Sasaki and Hideya So and Manabu Tsukada},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/376818826_Millimeter-Wave_Fast_Beam_Tracking_Enabled_by_RANV2X_Cooperation/links/658ac62e6f6e450f19a60664/Millimeter-Wave-Fast-Beam-Tracking-Enabled-by-RAN-V2X-Cooperation.pdf},
doi = {10.1109/ICAIIC60209.2024.10463482},
year = {2024},
date = {2024-02-19},
urldate = {2024-02-19},
booktitle = {International Conference on Artificial Intelligence in Information and Communication (ICAIIC 2024)},
address = {Osaka, Japan},
abstract = {Only the chairs can edit Automated driving has the same limitations as human drivers because it functions as a replacement for humans and operates based on local information using onboard sensors and computers. Cooperative automated driving is expected to achieve both safety and efficiency, which could not be achieved by imitating human driving, by sharing sensor information from roadside equipment and other vehicles. Since such sensor information is enormous, it is desirable to utilize millimeter-waves, which are capable of high-capacity transmission. However, wireless communication systems for cooperative automated driving have the challenge of radio quality degradation due to vehicle movement. Our research project aims to realize stable millimeter-wave transmission by incorporating Open RAN (O-RAN) and vehicle-to-everything (V2X) functions. This paper presents the overall proposed concept and an example of validation; we show the results of evaluating our previously proposed fast beam following scheme in a handover environment with multiple roadside units.},
note = {Excellent Paper Award},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{nokey,
title = {Location-Aided Fast Beam Tracking Algorithm for Millimeter-Wave V2I},
author = {Sojin Ozawa and Tokio Ikuta and Yuki Sasaki and Ryo Iwaki and Jin Nakazato and Manabu Tsukada and Hideya So and Kazuki Maruta},
doi = {10.23919/comex.2024XBL0001},
year = {2024},
date = {2024-02-15},
urldate = {2024-02-15},
journal = {IEICE Communications Express (ComEX)},
abstract = {This article proposes a millimeter-wave fast beam tracking algorithm for moving vehicles, considering a geometry of road environment. Focusing on the fact that vehicle movement is constrained on roads, horizontal and vertical beam directions are determined based on obtainable driving direction and road shape. In addition, we perform a two-pattern beam selection for the vehicle’s forward and rearward directions to esti- mate the beam tracking speed. By conducting simulations using SUMO, which emulates vehicle movement on various roads, we verified the effective operation of the proposed scheme and confirmed its superiority over the existing beam sweeping approach.},
note = {ComEX Top Downloaded Letter Award},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Karimata2024,
title = {Multi-UAVs Strategies for Ad Hoc Network with Multi-Agent Reinforcement Learning},
author = {George Karimata, Jin Nakazato, Gia Khanh Tran, Katsuya Suto, Manabu Tsukada and Hiroshi Esaki, },
url = {https://icoin.org/media?key=site/icoin2024/abs/P-4-1.pdf},
doi = {10.1109/ICOIN59985.2024.10572031},
year = {2024},
date = {2024-01-17},
urldate = {2024-01-17},
booktitle = {The 38th International Conference on Information Networking (ICOIN2024)},
address = {Ho Chi Minh City, Vietnam},
abstract = {In recent years, extensive research has focused on leveraging advanced technologies beyond 5G and for Industry 5.0 to promote sustainability and prosperity in society. Our study advances this effort by seeking to create an aerial perspective using Unmanned Aerial Vehicles (UAVs). This paper introduces a method for optimizing UAV deployment strategies using multi-agent reinforcement learning, facilitating the formation of a flying ad hoc network. The results demonstrate practical cooperation among UAVs in flight.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@misc{Sasaki2024,
title = {Location-Based Broad-Range Null-Steering in V2X Multiuser MIMO Transmission},
author = {Yuki Sasaki and Sojin Ozawa and Kabuto Arai and Jin Nakazato and Manabu Tsukada and Kazuki Maruta },
url = {https://tlab.hongo.wide.ad.jp/papers/2023_CCNC2023_poster_Sasaki.pdf},
year = {2024},
date = {2024-01-06},
urldate = {2024-01-06},
address = {Las Vegas, NV, USA},
abstract = {This paper proposes an intensive null-steering around the target in angular domain to effectively suppress inter-user interference (IUI) leakage caused by channel varying environment such as vehicular multiuser spatial multiplexing. Multiuser MIMO can enhance spectral efficiency by multiplexing a number of user terminals in spatial domain. Suppose applying multiuser MIMO downlink in vehicle-to-everything (V2X) scenario, vehicles move at high speed which causes IUI. Null-space expansion has been conceived that can improve IUI suppression capability by steering nulls to the past and the present channel states on interfered users. Collective perception in intelligent transport systems (ITS) provides location information of vehicles every 100 ms. Exploiting this feature, this paper proposes angular-domain null-space expansion; broad-range null-steering (BRNS). Computer simulation verifies its effectiveness.},
howpublished = {2024 IEEE 21st Consumer Communications & Networking Conference (CCNC), Poster},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
@misc{Iwaki2024,
title = {Optimizing mmWave Beamforming for High-Speed Connected Autonomous Vehicles: An Adaptive Approach},
author = {Ryo Iwaki and Jin Nakazato and Muhammad Asad and Ehsan Javanmardi and Kazuki Maruta and Manabu Tsukada and Hideya Ochiai and Hiroshi Esaki},
url = {https://tlab.hongo.wide.ad.jp/papers/2023_CCNC2023_poster_Iwaki.pdf},
year = {2024},
date = {2024-01-06},
urldate = {2024-01-06},
abstract = {The commercialization of 5G has been initiated for a while. Furthermore, millimeter wave (mmWave) has been introduced to small cells with small coverage due to its strong linearity and non-winding characteristics. On the other hand, in connected autonomous vehicles (CAVs), where various traffic systems can cooperatively perform recognition, decision-making, and execution, communication is assumed to be always connected. Therefore, to use low latency mmWave for high-speed moving CAV, beamforming in 5G cannot follow them at high speed. This paper proposes an improved beam tracking algorithm for high-speed CAVs, which can be evaluated in a more general environment using a traffic simulator. We proposed an adaptive algorithm for a general road environment by increasing the number of beam searches and search dimensions.},
howpublished = {2024 IEEE 21st Consumer Communications & Networking Conference (CCNC), Poster},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
@article{Tao2023b,
title = {Zero-Knowledge Proof of Traffic: A Deterministic and Privacy-Preserving Cross Verification Mechanism for Cooperative Perception Data},
author = {Ye Tao and Ehsan Javanmardi and Pengfei Lin and Yuze Jiang and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
url = {https://arxiv.org/abs/2312.07948},
doi = {10.1109/ACCESS.2023.3343405},
issn = {2169-3536},
year = {2023},
date = {2023-12-17},
urldate = {2023-12-17},
journal = {IEEE Access},
volume = {11},
pages = {142846-142861},
abstract = {Cooperative perception is crucial for connected automated vehicles in intelligent transportation systems (ITSs); however, ensuring the authenticity of perception data remains a challenge as the vehicles cannot verify events that they do not witness independently. Various studies have been conducted on establishing the authenticity of data, such as trust-based statistical methods and plausibility-based methods. However, these methods are limited as they require prior knowledge such as previous sender behaviors or predefined rules to evaluate the authenticity. To overcome this limitation, this study proposes a novel approach called zero-knowledge Proof of Traffic (zk-PoT), which involves generating cryptographic proofs to the traffic observations. Multiple independent proofs regarding the same vehicle can be deterministically cross-verified by any receivers without relying on ground truth, probabilistic, or plausibility evaluations. Additionally, no private information is compromised during the entire procedure. A full on-board unit software stack that reflects the behavior of zk-PoT is implemented within a specifically designed simulator called Flowsim. A comprehensive experimental analysis is then conducted using synthesized city-scale simulations, which demonstrates that zk-PoT’s cross-verification ratio ranges between 80 % to 96 %, and 90 % of the verification is achieved in 5 s, with a protocol overhead of approximately 25 %. Furthermore, the analyses of various attacks indicate that most of the attacks could be prevented, and some, such as collusion attacks, can be mitigated. The proposed approach can be incorporated into existing works, including the European Telecommunications Standards Institute (ETSI) and the International Organization for Standardization (ISO) ITS standards, without disrupting the backward compatibility.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Jiang2023,
title = {Roadside LiDAR Assisted Cooperative Localization for Connected Autonomous Vehicles},
author = {Yuze Jiang and Ehsan Javanmard and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
url = {https://arxiv.org/abs/2311.07913},
year = {2023},
date = {2023-12-14},
urldate = {2023-12-14},
booktitle = {ACM Intelligent Computing and its Emerging Applications (ICEA 2023)},
abstract = {Advancements in LiDAR technology have led to more cost-effective production while simultaneously improving precision and resolution. As a result, LiDAR has become integral to vehicle localization, achieving centimeter-level accuracy through techniques like Normal Distributions Transform (NDT) and other advanced 3D registration algorithms. Nonetheless, these approaches are reliant on high-definition 3D point cloud maps, the creation of which involves significant expenditure. When such maps are unavailable or lack sufficient features for 3D registration algorithms, localization accuracy diminishes, posing a risk to road safety. To address this, we proposed to use LiDAR-equipped roadside unit and Vehicle-to-Infrastructure (V2I) communication to accurately estimate the connected vehicle's position and help the vehicle when its self-localization is not accurate enough. Our simulation results indicate that this method outperforms traditional NDT scan matching-based approaches in terms of localization accuracy.},
note = {Best paper award (Silver)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Matsumoto2023b,
title = {Localizability Estimation for Autonomous Driving: A Deep Learning-Based Place Recognition Approach},
author = {Kazuto Matsumoto and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada},
doi = {10.1109/IRC59093.2023.00052},
year = {2023},
date = {2023-12-11},
urldate = {2023-12-11},
booktitle = {IEEE Robotic Computing 2023},
address = {California, USA},
abstract = {In recent years, research and development aimed at the societal implementation of autonomous driving have attracted increasing attention. Localization, which involves obtaining in- formation about the surrounding environment from sensor data and estimating the vehicle’s position, is necessary for realizing autonomous driving. Localization is commonly performed with 3D LiDAR as a sensor owing to its high measurement accuracy and immunity to ambient light conditions, which allow for precise localization. However, localization accuracy may decrease when the surrounding area does not have distinctive features. In this study, we proposed a method based on deep learning to estimate localization accuracy for autonomous driving. The overall localization accuracy can be improved by estimating the accuracy of localization using other sensors, such as GNSS and IMU, or pavement markings in areas with poor accuracy. We created a dataset for estimating localization accuracy using an open-source autonomous driving simulator. In an experiment, we applied the proposed method to the created dataset. Estimations with low MSE were obtained. The results indicate that the proposed method can accurately estimate localization accuracy.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Furuta2023,
title = {Web-Based BIM Platform for Building Digital Twin},
author = {Satoru Furuta and Jin Nakazato and Manabu Tsukada},
doi = {10.1109/DTPI59677.2023.10365476},
year = {2023},
date = {2023-11-07},
urldate = {2023-11-07},
booktitle = {3rd Annual IEEE International Conference on Digital Twins and Parallel Intelligence (DTPI 2023)},
address = {Florida, USA},
abstract = {Building digital twin (BDT) is utilized throughout the lifecycle of a building. It serves for efficient operations during the design and construction phases, and during the operational and maintenance phases, it’s used for asset management and as field maps for robots. Building Information Modeling (BIM), which contains both semantics and geometry data of building elements, holds promise as a data source for BDT. We have extracted four key technical challenges of the digital twin, particularly vital during the operational and maintenance phases: software, visualization, update, and real-scene reconstruction. Due to the exclusive and static nature of BIM, these challenges also pose significant issues in the context of BDT. To address these challenges, we designed and implemented a Web-based BIM platform. The implemented application shows not only geometry data but also semantics data, enables easy overlay with the latest indoor conditions, and provides updating functionality. The developed system is essential for the continuous operation of BDT in dynamic indoor environments. We evaluated the application through a questionnaire survey. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@workshop{Bao2023b,
title = {Towards a Trusted Inter-Reality: Exploring System Architectures for Digital Identification},
author = {Naren Bao and Jin Nakazato and Muhammad Asad and Ehsan Javanmardi and Manabu Tsukada},
doi = {10.1145/3627050.3631566},
year = {2023},
date = {2023-11-07},
urldate = {2023-11-07},
booktitle = {The 1st International Workshop on Internet of Realities (IoR-WS 2023) at International Conference on the Internet of Things},
address = {Nagoya, Japan},
abstract = {The concept of a trusted inter-reality, where physical and virtual worlds seamlessly converge, represents a paradigm shift in how digital identities are formed and managed. This paper explores the complex landscape of system architectures designed to enable secure and user-centric digital identification within interconnected realities. Our survey focuses on user-centric security, recognizing the prevalence of wearable devices and immersive technologies in inter-reality environments. We advocate for user-friendly authentication methods and privacy-preserving techniques that prioritize user control within the trust model. Furthermore, we delve into the influence of social and cultural factors, particularly age and gender, on the shaping of digital identity within interconnected realities. We argue in favor of adaptable system architectures that respect generational and gender diversity. In conclusion, we emphasize the alignment of system architectures with these principles to promote a secure, user-centric, and culturally sensitive digital identity experience. This research contributes to the ongoing discourse on digital identification in interconnected realities, providing actionable guidance for stakeholders in the evolving landscape of trusted inter-reality.
},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@workshop{Sugizaki2023b,
title = {Umpire Assistance System in Baseball Game},
author = {Yusuke Sugizaki and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
doi = {10.1145/3627050.3631569},
year = {2023},
date = {2023-11-07},
urldate = {2023-11-07},
booktitle = {The 1st International Workshop on Internet of Realities (IoR-WS 2023) at International Conference on the Internet of Things},
address = {Nagoya, Japan},
abstract = {In recent years, information technology has become an important element in the pursuit of a more prosperous society, and this extends to the realm of sports. Events across various domains are undergo- ing digitalization. Within the context of baseball, the introduction of automated technology for umpiring is garnering significant at- tention. Conversely, the frequent misjudgments by human referees have become a contentious issue. Both Major League Baseball (MLB) and Nippon Professional Baseball (NPB) have introduced a chal- lenge system that requires replay verification in instances where there’s disagreement with an umpire’s decision. However, this sys- tem is not permissible in venues lacking the necessary facilities. A consequential shortage of umpires intensifies their workload, potentially leading to more misjudgments. This paper proposes an architecture for an umpire assistance system designed to ad- dress these challenges in baseball games. Our proposed system architecture facilitates the filming of a baseball game from multiple angles, rendering the challenge system independent of the venue’s infrastructure. Moreover, the system autonomously makes judg- ments using footage from multiple cameras, thereby supporting both human umpires and automated officiating. Looking ahead, we also discuss the potential advancements when integrating this with digital twins and explore its applicability to other sports.},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@workshop{Cheng2023,
title = {Pedestrian-centric Augmented Reality Visualization of Real-time Autonomous Vehicle Dynamics},
author = {Yiwei Cheng and Jin Nakazato and Ehsan Javanmardi and Chia-Ming Chang and Manabu Tsukada},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/374387897_Pedestrian-centric_Augmented_Reality_Visualization_of_Real-time_Autonomous_Vehicle_Dynamics/links/651bda961e2386049df3c4ee/Pedestrian-centric-Augmented-Reality-Visualization-of-Real-time-Autonomous-Vehicle-Dynamics.pdf},
doi = {10.1109/CloudNet59005.2023.10490048},
year = {2023},
date = {2023-11-04},
urldate = {2023-11-04},
booktitle = {The Workshop on Intelligent Cloud Continuum for B5G Services in the IEEE International Conference on Cloud Networking (CloudNet) 2023},
address = {New York City, USA},
abstract = {Connected Autonomous Vehicles (CAVs) produce a variety of information within their systems. With the advancement of communication and V2X (Vehicle-to-Everything) communication technology, there is a growing challenge to effectively convey this information to pedestrians and enhance their sense of safety when encountering such vehicles. Efforts to communicate this information to pedestrians have been made through various means, with Augmented Reality (AR) emerging as a notable approach. However, previous studies have yet to integrate a functional AR application with a real-world autonomous driving system. In response to this gap, we proposed an architecture for an AR application that visualizes real-time data from an active CAV and subsequently developed the system. Furthermore, we conducted field experiments using this developed system and conducted user surveys during exhibitions to gather insights into the public’s perception of the system. Our results showed that the system can effectively transmit information from the CAV, and when provided with additional information, people tend to feel safer regarding the vehicle. },
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@article{Nakazato2023,
title = {WebRTC over 5G: A Study of Remote Collaboration QoS in Mobile Environment},
author = {Jin Nakazato and Kousuke Nakagawa and Koki Itoh and Romain Fontugne and Manabu Tsukada and Hiroshi Esaki},
url = {https://link.springer.com/content/pdf/10.1007/s10922-023-09778-5.pdf},
doi = {10.1007/s10922-023-09778-5},
issn = {1573-7705},
year = {2023},
date = {2023-10-24},
urldate = {2023-10-24},
journal = {Journal of Network and Systems Management},
volume = {32},
issue = {1},
abstract = {The increasing demand for remote collaboration and remote working has become crucial to daily life owing to the Covid-19 pandemic and the development of internet-based video distribution services. Furthermore, low-latency remote collaboration, such as teleoperation and support applications designed for in-vehicle environments, has gained considerable attention. The 5G technology is considered as a key infrastructure for remote collaboration. This study aimed to evaluate the actual 5G capability to achieve high quality of service (QoS) for remote collaboration. We designed and implemented a measurement tool to monitor the QoS of remote collaboration under real-world 5G conditions. We performed measurements encompassing the various 5G frequency bands. During these experiments, we employed various tools to obtain detailed mobile signal conditions to analyze the relationship between various environmental factors (e.g. signal quality, band, handoff, geographic conditions, and mobility) and the QoS performance of remote collaboration in a real-world 5G environment. This study elucidated the correlation between the WebRTC performance and various environmental factors as well as the performance improvement potential by leveraging the communication technologies of multiple mobile carriers. The collected data has been made publicly available to foster research on QoS and 5G.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Lin2023c,
title = {Potential Field-based Path Planning with Interactive Speed Optimization for Autonomous Vehicles},
author = {Pengfei Lin and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada},
url = {https://arxiv.org/abs/2306.06987},
doi = {10.1109/IECON51785.2023.10311890},
year = {2023},
date = {2023-10-16},
urldate = {2023-10-16},
booktitle = {49th Annual Conference of the IEEE Industrial Electronics Society (IECON 2023)},
abstract = {Path planning is critical for autonomous vehicles (AVs) to determine the optimal route while considering constraints and objectives. The potential field (PF) approach has become prevalent in path planning due to its simple structure and computational efficiency. However, current PF methods used in AVs focus solely on the path generation of the ego vehicle while assuming that the surrounding obstacle vehicles drive at a preset behavior without the PF-based path planner, which ignores the fact that the ego vehicle’s PF could also impact the path generation of the obstacle vehicles. To tackle this problem, we propose a PF-based path planning approach where local paths are shared among ego and obstacle vehicles via vehicle-to- vehicle (V2V) communication. Then by integrating this shared local path into an objective function, a new optimization function called interactive speed optimization (ISO) is designed to allow driving safety and comfort for both ego and obstacle vehicles. The proposed method is evaluated using MATLAB/Simulink in the urgent merging scenarios by comparing it with conventional methods. The simulation results indicate that the proposed method can mitigate the impact of other AVs’ PFs by slowing down in advance, effectively reducing the oscillations for both ego and obstacle AVs.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Sugizaki2023,
title = {Wireless Ad-Hoc Federated Learning for Cooperative Map Creation and Localization Models},
author = {Yusuke Sugizaki and Hideya Ochiai and Muhammad Asad and Manabu Tsukada and Hiroshi Esaki},
doi = {10.1109/WF-IoT58464.2023.10539517},
year = {2023},
date = {2023-10-12},
urldate = {2023-10-12},
booktitle = {The 9th IEEE World Forum on Internet of Things (IEEE WFIoT2023)},
address = {Aveiro, Portugal},
abstract = {Although Wi-Fi signals have been used for localization, many existing methods require gathering Wi-Fi information about the area in advance. This study proposed a novel system in which wireless ad-hoc federated learning is used to learn localization models and create maps cooperatively during regular movement. In this system, a combination of classification models is used to perform localization from Wi-Fi signal strength measured as received signal strength indicator (RSSI). In this study, RSSI data in a real-world Wi-Fi environment were collected to train and test localization models. The proposed method achieved localization accuracy between 91.30% and 96.11 %, which demonstrated the ability of the method to train localization models collaboratively.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Chauhan2023,
title = {Keep Calm and Cross: Smart Pole Interaction Unit for Easing Pedestrian Cognitive Load},
author = {Vishal Chauhan and Chia-Ming Chang and Ehsan Javanmardi and Jin Nakazato and Koki Toda and Pengfei Lin and Takeo Igarashi and Manabu Tsukada},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/374582122_Keep_Calm_and_Cross_Smart_Pole_Interaction_Unit_for_Easing_Pedestrian_Cognitive_Load/links/6525681eb32c91681fb2e1b5/Keep-Calm-and-Cross-Smart-Pole-Interaction-Unit-for-Easing-Pedestrian-Cognitive-Load.pdf},
doi = {10.1109/WF-IoT58464.2023.10539511},
year = {2023},
date = {2023-10-12},
urldate = {2023-10-12},
booktitle = {The 9th IEEE World Forum on Internet of Things (IEEE WFIoT2023)},
address = {Aveiro, Portugal},
abstract = {Recently, there has been a growing emphasis on autonomous vehicles (AVs), and as they coexist with pedestrians, ensuring pedestrian safety at crosswalks has become paramount. While AVs exhibit commendable performance on traditional roads with established traffic infrastructure, their interaction in different environments, such as shared spaces lacking traffic lights or sign rules (also known as naked streets), can present significant challenges, including right-of-way and accessibility concerns. To address these challenges, this study proposes a novel approach to enhance pedestrian safety in shared spaces, focusing on the proposed smart pole interaction unit (SPIU) combined with an external human-machine interface (eHMI). By evaluating the proposal of SPIU developed by a virtual reality system, we explore its usability and effectiveness in facilitating vehicle-to-pedestrian (V2P) interactions at crosswalks. Our findings from this study showed that SPIU facilitates safe, quicker decision-making to stop and pass at crosswalks in shared space and reduces cognitive load compared to scenarios where an SPIU is absent for pedestrians and reduce the need for eHMI to see on multiple AVs. The SPIU addition with the eHMI in vehicles yields a noteworthy 21 % improvement in response time, enhancing efficiency during pedestrian stops. In both scenarios, whether with a single AV (1-way) or multiple AVs (2-way), SPIU has a positive impact on interaction dynamics and statistically demonstrates a significant improvement (p = 0.001). },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Chauhan2023c,
title = {Fostering Fuzzy Logic in Enhancing Pedestrian Safety: Harnessing Smart Pole Interaction Unit for Autonomous Vehicle-to-Pedestrian Communication and Decision Optimization},
author = {Vishal Chauhan and Chia-Ming Chang and Ehsan Javanmardi and Jin Nakazato and Pengfei Lin and Takeo Igarashi and Manabu Tsukada},
url = {https://www.mdpi.com/2079-9292/12/20/4207},
doi = {10.3390/electronics12204207},
issn = {2079-9292},
year = {2023},
date = {2023-10-11},
urldate = {2023-10-11},
journal = {Electronics},
volume = {12},
number = {20},
abstract = {In autonomous vehicles (AVs), ensuring pedestrian safety within intricate and dynamic settings, particularly at crosswalks, has gained substantial attention. While AVs perform admirably in standard road conditions, their integration into unique environments like shared spaces devoid of traditional traffic infrastructure control presents complex challenges. These challenges involve issues of right-of-way negotiation and accessibility, particularly in “naked streets”. This research delves into an innovative smart pole interaction unit (SPIU) with an external human–machine interface (eHMI). Utilizing virtual reality (VR) technology to evaluate the SPIU efficacy, this study investigates its capacity to enhance interactions between vehicles and pedestrians at crosswalks. The SPIU is designed to communicate the vehicles’ real-time intentions well before arriving at the crosswalk. The study findings demonstrate that the SPIU significantly improves secure decision making for pedestrian passing and stops in shared spaces. Integrating an SPIU with an eHMI in vehicles leads to a substantial 21% reduction in response time, greatly enhancing the efficiency of pedestrian stops. Notable enhancements are observed in unidirectional (one-way) and bidirectional (two-way) scenarios, highlighting the positive impact of the SPIU on interaction dynamics. This work contributes to AV–pedestrian interaction and underscores the potential of fuzzy-logic-driven solutions in addressing complex and ambiguous pedestrian behaviors.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Bao2023,
title = {Personalized Causal Factor Generalization for Subjective Risky Scene Understanding with Vision Transformer},
author = {Naren Bao and Alexander Carballo and Manabu Tsukada and Kazuya Takeda},
doi = {10.1109/ITSC57777.2023.10422148},
year = {2023},
date = {2023-09-24},
urldate = {2023-09-24},
booktitle = {The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)},
address = {Bilbao, Bizkaia, Spain},
abstract = {This paper presents a framework to understanding subjective driving scene perception by Vision Transformer for Environmental Feature Extraction within a Causal Modeling Analysis method. By leveraging vision transformer models, informative features are extracted from video camera images capturing the surrounding environment. Through the causal analysis, the causal effects of these variables on subjective risk perception are explored, shedding light on the factors influencing individuals' perception of driving risk. The findings demonstrate understanding of environmental features and individual difference on risk perception, providing a deeper understanding of risky scene perception. The paper concludes with this approach unifies selective attentional phenomena can improve the scene understanding for subjective perception in real-world driving scenarios aiming to enhance driving safety based on the identified causal factors. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Yamazaki2023,
title = {ToST: Tokyo SUMO traffic scenario },
author = {Yuji Yamazaki and Yasumasa Tamura and Xavier Defago and Ehsan Javanmardi and Manabu Tsukada},
url = {https://github.com/dfg-lab/ToSTScenario},
doi = {10.1109/ITSC57777.2023.10422517},
year = {2023},
date = {2023-09-24},
urldate = {2023-09-24},
booktitle = {The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)},
address = {Bilbao, Bizkaia, Spain},
abstract = {In recent years, research, development, and demonstrations aimed at the societal implementation of autonomous driving have attracted increasing attention. Localization, which involves obtaining information of the surrounding environment from sensor data and estimating the vehicle's position, is necessary for realizing autonomous driving. Localization is commonly performed with 3D LiDAR as a sensor owing to its high measurement accuracy and immunity to ambient light conditions, which allow for precise localization. However, when the surrounding area has distinctive features, localization accuracy may decrease. In this study, we proposed a method based on deep learning to predict the localization accuracy for autonomous driving. The overall localization accuracy can be improved by predicting the accuracy of localization using other sensors, such as GNSS and IMU, or pavement markings in areas with poor accuracy. We created a dataset for predicting the localization accuracy using an open-source autonomous driving simulator. In an experiment, we applied the proposed method to the created dataset. Thresholds were set for errors in the x-direction, y-direction, and distance for localization. Predictions with high accuracy and F-values were obtained. The results indicate that the proposed method can accurately predict the localization accuracy. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Lin2023b,
title = {Occlusion-Aware Path Planning for Collision Avoidance: Leveraging Potential Field Method with Responsibility-Sensitive Safety},
author = {Pengfei Lin and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada},
url = {https://arxiv.org/abs/2306.06981},
doi = {10.1109/ITSC57777.2023.10422621},
year = {2023},
date = {2023-09-24},
urldate = {2023-09-24},
booktitle = {The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)},
series = {Bilbao, Bizkaia, Spain},
abstract = {Collision avoidance (CA) has always been the foremost task for autonomous vehicles (AVs) under safety criteria. And path planning is directly responsible for generating a safe path to accomplish CA while satisfying other commands. Due to the real-time computation and simple structure, the potential field (PF) has emerged as one of the mainstream path-planning algorithms. However, the current PF is primarily simulated in ideal CA scenarios, assuming complete obstacle information while disregarding occlusion issues where obstacles can be partially or entirely hidden from the AV's sensors. During the occlusion period, the occluded obstacles do not possess a PF. Once the occlusion is over, these obstacles can generate an instantaneous virtual force that impacts the ego vehicle. Therefore, we propose an occlusion-aware path planning (OAPP) with the responsibility-sensitive safety (RSS)-based PF to tackle the occlusion problem for non-connected AVs. We first categorize the detected and occluded obstacles, and then we proceed to the RSS violation check. Finally, we can generate different virtual forces from the PF for occluded and non-occluded obstacles. We compare the proposed OAPP method with other PF-based path planning methods via MATLAB/Simulink. The simulation results indicate that the proposed method can eliminate instantaneous lateral oscillation or sway and produce a smoother path than conventional PF methods.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tao2023c,
title = {Flowsim: A Modular Simulation Platform for Microscopic Behavior Analysis of City-Scale Connected Autonomous Vehicles},
author = {Ye Tao and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
url = {https://github.com/tlab-wide/flowsim
https://arxiv.org/abs/2306.05738},
doi = {10.1109/ITSC57777.2023.10421900},
year = {2023},
date = {2023-09-24},
urldate = {2023-09-24},
booktitle = {The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)},
address = {Bilbao, Bizkaia, Spain},
abstract = {As connected autonomous vehicles (CAVs) become increasingly prevalent, there is a growing need for simulation platforms that can accurately evaluate CAV behavior in large-scale environments. In this paper, we propose Flowsim, a novel simulator specifically designed to meet these requirements. Flowsim offers a modular and extensible architecture that enables the analysis of CAV behaviors in large-scale scenarios. It provides researchers with a customizable platform for studying CAV interactions, evaluating communication and networking protocols, assessing cybersecurity vulnerabilities, optimizing traffic management strategies, and developing and evaluating policies for CAV deployment. Flowsim is implemented in pure Python in approximately 1,500 lines of code, making it highly readable, understandable, and easily modifiable. We verified the functionality and performance of Flowsim via a series of experiments based on realistic traffic scenarios. The results show the effectiveness of Flowsim in providing a flexible and powerful simulation environment for evaluating CAV behavior and data flow. Flowsim is a valuable tool for researchers, policymakers, and industry professionals who are involved in the development, evaluation, and deployment of CAVs. The code of Flowsim is publicly available on GitHub under the MIT license. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Yosodipuro2023,
title = {Mixed-traffic Intersection Management using Traffic-load-responsive Reservation and V2X-enabled Speed Coordination},
author = {Nicholaus Danispadmanaba Yosodipuro and Ehsan Javanmardi and Jin Nakazato and Yasumasa Tamura and Xavier Defago and Manabu Tsukada},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/374470825_Mixed-traffic_Intersection_Management_using_Traffic-load-responsive_Reservation_and_V2X-enabled_Speed_Coordination/links/651ee8d63ab6cb4ec6bde79a/Mixed-traffic-Intersection-Management-using-Traffic-load-responsive-Reservation-and-V2X-enabled-Speed-Coordination.pdf},
doi = {10.1109/ITSC57777.2023.10422248},
year = {2023},
date = {2023-09-24},
urldate = {2023-09-24},
booktitle = {The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)},
address = {Bilbao, Bizkaia, Spain},
abstract = {Vehicle-to-everything (V2X) communication enables connected autonomous vehicles (CAVs) to share information and generate optimal decisions. The networking abilities of CAVs have led to the development of unsignalized autonomous intersection management (AIM) methods that leverage CAVs to significantly improve traffic flows. However, AIM methods assume 100% CAV market penetration, which is currently unrealistic owing to the gradual adoption of CAVs. Therefore, CAVs must share road usage with nonconnected vehicles (NCVs). Thus, we propose a mixed-traffic intersection management method that considers NCVs while ensuring high traffic flow, called traffic-load-responsive reservation for intersection management (TLRRIM). In TLRRIM, the roadside unit (RSU) first classifies vehicles and groups them into clusters before selecting a reservation cluster to cross an intersection. The reservation cluster selection considers both traffic load and crossing urgency. In addition, the RSU utilizes V2X-enabled speed coordination (VESC) for CAVs within the reservation cluster to further improve traffic flow, while utilizing traffic lights to guide NCVs. Simulation-based experiments using OpenCDA and CARLA showed that TLRRIM can increase throughput and reduce waiting time by up to 89.63% and 60.71%, respectively, compared with the fixed-time signaling method. Moreover, adding VESC can increase throughput by 12.21% and reduce waiting time by 10.80%, further enhancing traffic flow. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@misc{Yamamoto2023b,
title = {Evaluation of IPv6-only-Capable Iterative Resolvers},
author = {Momoka Yamamoto and Jin Nakazato and Romain Fontugne and Manabu Tsukada and Hiroshi Esaki},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/373512394_Evaluation_of_IPv6-only-Capable_Iterative_Resolvers/links/64ef62530f7ab20a8666c879/Evaluation-of-IPv6-only-Capable-Iterative-Resolvers.pdf},
doi = {10.1145/3603269.3610850},
year = {2023},
date = {2023-09-10},
urldate = {2023-09-10},
pages = {1102-1104},
address = {New York City, USA},
abstract = {This paper introduces an "IPv6-only-Capable resolver" to address the issue of many zones remaining unresolvable due to a lack of IPv6 connectivity in authoritative name servers. The proposed method utilizes NAT64 to transmit packets to IPv4-only authoritative name servers and increases resolution success rates with competitive response times compared to a traditional IPv6-only resolver.},
howpublished = {ACM Special Interest Group on Data Communication (SIGCOMM), Poster},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
@article{Asad2023b,
title = {Limitations and Future Aspects of Communication Costs in Federated Learning: A Survey},
author = {Muhammad Asad and Saima Shaukat and Dou Hu and Zekun Wang and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada},
doi = {10.3390/s23177358},
issn = {1424-8220},
year = {2023},
date = {2023-08-23},
urldate = {2023-08-23},
journal = {Sensors},
volume = {23},
number = {17},
abstract = {This paper explores the potential for communication-efficient federated learning (FL) in modern distributed systems. FL is an emerging distributed machine learning technique that allows for distributed training of a single machine learning model across multiple geographically distributed clients. This paper surveys the various approaches to communication-efficient FL, including model updates, compression techniques, resource management for edge and cloud, and client selection. We also review the various optimization techniques associated with communication-efficient FL, such as compression schemes and structured updates. Finally, we highlight the current research challenges and discuss the potential future directions for communication-efficient FL.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@misc{Yamamoto2023,
title = {Iterative Resolution with IPv6 Packets Failing},
author = {Momoka Yamamoto and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki
},
url = {https://tlab.hongo.wide.ad.jp/papers/ICCCN2023_poster.pdf},
doi = {10.1109/ICCCN58024.2023.10230170},
year = {2023},
date = {2023-07-24},
urldate = {2023-07-24},
address = {Waikiki Beach/Honolulu, Hawaii, USA},
abstract = {The exhaustion of IPv4 addresses has driven the rapid adoption of IPv6 networks, which has created challenges in the domain name resolution process, particularly for IPv6-only iterative resolvers. This paper presents an experimental analysis to quantify the extent of this problem, revealing a significantly lower success rate of name resolution using IPv6-only resolvers (64.1%) compared to IPv4-only resolvers (98.8%). By analysing the success rates and percentages of A and AAAA records for the top 1,000,000 domains in the Tranco list, we identify the limitations of IPv6-only iterative resolvers and highlight the urgent need for comprehensive solutions to improve DNS resolution in IPv6-only networks. Our findings emphasise the importance of full IPv6 adoption for improved compatibility in IPv6-only environments, and serve as a basis for addressing the challenges faced by IPv6-only networks.},
howpublished = {The 32nd International Conference on Computer Communication and Networks (ICCCN 2023), Poster},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
@inproceedings{Wang2023,
title = {Overcoming Environmental Challenges in CAVs Through MEC-Based Federated Learning},
author = {Zekun Wang and Jin Nakazato and Muhammad Asad and Ehsan Javanmardi and Manabu Tsukada},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/371685830_Overcoming_Environmental_Challenges_in_CAVs_through_MEC-based_Federated_Learning/links/64a420ea8de7ed28ba7465c7/Overcoming-Environmental-Challenges-in-CAVs-through-MEC-based-Federated-Learning.pdf},
doi = {10.1109/ICUFN57995.2023.10200688},
year = {2023},
date = {2023-07-04},
urldate = {2023-07-04},
booktitle = {14th International Conference on Ubiquitous and Future Networks (ICUFN2023)},
pages = {1-6},
address = {Paris, France},
abstract = {Connected autonomous vehicles (CAVs), through vehicle-to-everything communication and computing resources, enable the vital exchange of information. Although deep learning is crucial in this landscape, it requires extensive and intricate datasets covering all potential scenarios. Furthermore, this situation poses a hazard, as the likelihood of accidents associated with imbalanced datasets increases, particularly in scenarios where processing analysis is compromised due to fluctuating weather conditions. We propose a Federated Learning (FL) framework undergirded by Multi-Access Edge Computing (MEC) to counter these challenges. This local device-focused framework enhances task-specific models' caching and continual updating across various conditions. In a more specific sense, edge nodes (ENs) operate as MEC, each caching multiple dedicated models and serving as the aggregator as part of the FL process. Additionally, we have engineered two innovative algorithms that categorize various states into multiple classes, thereby ensuring the efficient utilization of computing resources in ENs. Simulation results substantiate the effectiveness of our approach, showing that the proposed dedicated model consistently outperforms a general model designed for all situations.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Inokuchi2023b,
title = {Semantic Digital Twin for interoperability and comprehensive management of data assets},
author = {Kazuma Inokuchi and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/371903091_Semantic_digital_twin_for_interoperability_and_Comprehensive_Management_of_Data_Assets/links/649b13318de7ed28ba5ca665/Semantic-digital-twin-for-interoperability-and-Comprehensive-Management-of-Data-Assets.pdf},
doi = {10.1109/MetaCom57706.2023.00049},
year = {2023},
date = {2023-06-26},
urldate = {2023-06-26},
booktitle = {IEEE International Conference on Metaverse Computing, Networking and Applications (IEEE MetaCom 2023)},
address = {Kyoto, Japan},
abstract = {Fusion of the real and virtual worlds is essential for applying digital technology to the infrastructure of human life. A digital twin is one of the technologies that aim to integrate real and virtual space. It creates a digital world with high fidelity to reality by accumulating exhaustive information from sensors to improve simulation and prediction accuracy. However, traditional digital twins have data asset management challenges owing to the physical, temporal, and structural heterogeneity of their objects. In this paper, we propose two metadata schemas that leverage semantics to construct a designer-oriented digital twin. Moreover, we implemented a viewer that reproduced the office-like demonstration field to verify the application of the proposed ontology. The proposed method enables a generic description of the dynamic behaviors of any entity by integrating physical twins faithful to the real world with virtual models expected by designers. We compared the proposed ontologies with existing techniques, conducted user evaluations, and discussed possible approaches for further enhancements for widespread use.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Sone2023,
title = {An Ontology for Spatio-Temporal Media Management and an Interactive Application},
author = {Takuro Sone and Shin Kato and Ray Atarashi and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
url = {https://github.com/sdm-wg/web360square-vue
https://tlab.hongo.wide.ad.jp/sdmo/},
doi = {10.3390/fi15070225},
issn = {1999-5903},
year = {2023},
date = {2023-06-23},
urldate = {2023-06-23},
journal = {Future Internet},
volume = {15},
number = {225},
issue = {7},
abstract = {In addition to traditional viewing media, metadata that record the physical space from multiple perspectives will become extremely important in realizing interactive applications such as Virtual Reality(VR), Augmented Reality(AR). This paper proposes the Software Defined Media (SDM) Ontology designed to describe spatio-temporal media and the systems that handle them comprehensively. Spatio-temporal media refers to video, audio, and various sensor values recorded together with time and location information. The SDM Ontology can flexibly and precisely represent spatio-temporal media, equipment, and functions that record, process, edit, and play them and related semantic information. In addition, we recorded classical and jazz concerts using many video cameras and audio microphones, and then processed and edited the video and audio data with related metadata. Then, we created a dataset using the SDM Ontology and published it as linked open data(LOD). Furthermore, we developed "Web360^2" an application that enables users to interactively view and experience 360-degree video and spatial acoustic sounds by referring to this dataset. We conducted a subjective evaluation by using a user questionnaire. Web360^2 is a data-driven web application that obtains video and audio data and related metadata by querying the Dataset.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Asabe2023b,
title = {AutowareV2X: Reliable V2X Communication and Collective Perception for Autonomous Driving},
author = {Yu Asabe and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
url = {https://github.com/tlab-wide/AutowareV2X
https://tlab-wide.github.io/AutowareV2X/main/
https://www.youtube.com/watch?v=57fx3-gUNxU},
doi = {10.1109/VTC2023-Spring57618.2023.10199425},
year = {2023},
date = {2023-06-20},
urldate = {2023-06-20},
booktitle = {The 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)},
address = {Florence, Italy},
abstract = {For cooperative intelligent transport systems (C-ITS), vehicle-to-everything (V2X) communication is utilized to allow autonomous vehicles to share critical information with each other. We propose AutowareV2X, an implementation of a V2X communication module that is integrated into the autonomous driving (AD) software, Autoware. AutowareV2X provides external connectivity to the entire AD stack, enabling the end-to-end (E2E) experimentation and evaluation of connected autonomous vehicles (CAV). The Collective Perception Service was also implemented, allowing the transmission of Collective Perception Messages (CPMs). A dual-channel mechanism that enables wireless link redundancy on the critical object information shared by CPMs is also proposed. Performance evaluation in field experiments has indicated that the E2E latency of perception information is around 30 ms, and shared object data can be used by the AD software to conduct collision avoidance maneuvers. Dual-channel delivery of CPMs enabled the CAV to dynamically select the best CPM from CPMs received from different links, depending on the freshness of their information.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@misc{Hu2023,
title = {An Extended Kalman Filter Enabled Beam Tracking Framework in Intersection Management},
author = {Dou Hu and Jin Nakazato and Ehsan Javanmardi and Muhammad Asad and Manabu Tsukada
},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/371358188_An_Extended_Kalman_Filter_Enabled_Beam_Tracking_Framework_in_Intersection_Management/links/64807e24b3dfd73b776baeed/An-Extended-Kalman-Filter-Enabled-Beam-Tracking-Framework-in-Intersection-Management.pdf},
year = {2023},
date = {2023-06-06},
urldate = {2023-06-06},
address = {Gothenburg, Sweden},
abstract = {Recently, vehicle-to-everything (V2X) has been at- tracting attention for its potential to improve traffic safety and increase traffic volume worldwide, improving the accuracy of data and parameters collected from moving vehicles is widely discussed in the V2X. The most common technique of GPS may not be efficient during some specific scenarios, like some intersections full of skyscrapers, or some special terrains with obstacles. In such cases, GPS technology has a longer detection period and lower tracking accuracy, so beam tracking can be a fast and efficient solution in these circumstances. Therefore we propose an anti-diverge extend Kalman filter-enabled beam tracking method in V2X to help the intersection management. The numerical results show that our method has the ability to resist the Kalman filter’s divergence and can detect data in an accurate manner.},
howpublished = {European Conference on Networks and Communications (EuCNC) & 6G Summit Poster},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
@inproceedings{Lin2023,
title = {Time-To-Collision-Aware Lane-Change Strategy Based on Potential Field and Cubic Polynomial for Autonomous Vehicles},
author = {Pengfei Lin and Ehsan Javanmardi and Ye Tao and Vishal Chauhan and Jin Nakazato and Manabu Tsukada},
url = {https://arxiv.org/abs/2306.06981},
year = {2023},
date = {2023-06-04},
urldate = {2023-06-04},
booktitle = {2023 IEEE Intelligent Vehicles Symposium (IEEE IV 2023)},
address = {Anchorage, Alaska, USA},
abstract = {Making safe and successful lane changes (LCs) is one of the many vitally important functions of autonomous vehicles (AVs) that are needed to ensure safe driving on expressways. Recently, the simplicity and real-time performance of the potential field (PF) method have been leveraged to design decision and planning modules for AVs. However, the LC trajectory planned by the PF method is usually lengthy and takes the ego vehicle laterally parallel and close to the obstacle vehicle, which creates a dangerous situation if the obstacle vehicle suddenly steers. To mitigate this risk, we propose a time-to-collision-aware LC (TTCA-LC) strategy based on the PF and cubic polynomial in which the TTC constraint is imposed in the optimized curve fitting. The proposed approach is evaluated using MATLAB/Simulink under high-speed conditions in a comparative driving scenario. The simulation results indicate that the TTCA-LC method performs better than the conventional PF-based LC (CPF-LC) method in generating shorter, safer, and smoother trajectories. The length of the LC trajectory is shortened by over 27.1%, and the curvature is reduced by approximately 56.1% compared with the CPF-LC method.
},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Asad2023,
title = {A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems},
author = {Muhammad Asad and Saima Shaukat and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada},
doi = {10.3390/app13106201},
issn = {2076-3417},
year = {2023},
date = {2023-05-18},
urldate = {2023-05-18},
journal = {Applied Sciences },
abstract = {Big data is a rapidly growing field, and new developments are constantly emerging to address various challenges. One such development is the use of federated learning for recommendation systems (FRSs). An FRS provides a way to protect user privacy by training recommendation models using intermediate parameters instead of real user data. This approach allows for cooperation between data platforms while still complying with privacy regulations. In this paper, we explored the current state of research on FRSs, highlighting existing research issues and possible solutions. Specifically, we looked at how FRSs can be used to protect user privacy while still allowing organizations to benefit from the data they share. Additionally, we examined potential applications of FRSs in the context of big data, exploring how these systems can be used to facilitate secure data sharing and collaboration. Finally, we discuss the challenges associated with developing and deploying FRSs in the real world and how these challenges can be addressed.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Lin2022e,
title = {Safety Tunnel-Based Model Predictive Path-Planning Controller with Potential Functions for Emergency Navigation},
author = {Pengfei Lin and Ying Shuai Quan and Jin Ho Yang and Chung Choo Chung and Manabu Tsukada},
doi = {10.1109/TITS.2022.3229699},
issn = {1524-9050},
year = {2023},
date = {2023-04-01},
urldate = {2023-04-01},
journal = {IEEE Transactions on Intelligent Transportation Systems},
volume = {24},
issue = {4},
pages = {3974 - 3985},
abstract = {The potential functions (PFs) have generally shown good performances in real-time path planning with computation efficiency conforming to the requirements of lower control systems in autonomous driving. However, several inherent limitations exist in using the PFs, including a local minimum in specific scenarios and no passage between closely spaced obstacles. Recent studies have focused on conventional scenarios where PFs are assumed to work normally, without malfunctioning, occurring during perilous situations. Therefore, we propose a specific safety tunnel (ST)-based model predictive controller (MPC) combined with PFs (PF-STMPC) to handle path-planning in extreme-emergency traffic scenarios (e.g., emergency braking and lane-changing obstacles). To further guarantee driving safety, we improve PFs with the responsibility-sensitive safety (RSS) model that accurately calculates the minimum safe longitudinal and lateral distances. Furthermore, a sigmoid-based ST is designed for emergency navigation if the PFs fail to plan a safe path due to the aforementioned inherent limitations, enabling the controller with planning functionality if necessary. The ST is embedded in the MPC-based tracking controller as a safe constraint sensitive to surrounding environments (e.g., road structure and obstacles). The proposed PF-STMPC was co-simulated using MATLAB/Simulink and CarSim Simulator under the constant speed condition. Compared with the state-of-the-art method, the proposed method demonstrated better performance in finding a safe path and eliminating severe yawing of the ego-vehicle (82.8% less in sideslip yawing amplitude and 57.7% shorter in the oscillation period of yaw angle) when facing traffic emergencies.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Tao2023,
title = {zk-PoT: Zero-Knowledge Proof of Traffic for Privacy Enabled Cooperative Perception},
author = {Ye Tao and Yuze Jiang and Pengfei Lin and Manabu Tsukada and Hiroshi Esaki},
url = {http://arxiv.org/abs/2211.07875},
doi = {10.1109/CCNC51644.2023.10059601},
year = {2023},
date = {2023-01-08},
urldate = {2023-01-08},
booktitle = {2023 IEEE 20th Annual Consumer Communications & Networking Conference (CCNC)},
address = {Las Vegas, NV, USA},
abstract = {Cooperative perception is an essential and widely discussed application of connected automated vehicles. However, the authenticity of perception data is not ensured, because the vehicles cannot independently verify the event they did not see. Many methods, including trust-based (i.e., statistical) approaches and plausibility-based methods, have been proposed to determine data authenticity. However, these methods cannot verify data without a priori knowledge. In this study, a novel approach of constructing a self-proving data from the number plate of target vehicles was proposed. By regarding the pseudonym and number plate as a shared secret and letting multiple vehicles prove they know it independently, the data authenticity problem can be transformed to a cryptography problem that can be solved without trust or plausibility evaluations. Our work can be adapted to the existing works including ETSI/ISO ITS standards while maintaining backward compatibility. Analyses of common attacks and attacks specific to the proposed method reveal that most attacks can be prevented, whereas preventing some other attacks, such as collusion attacks, can be mitigated. Experiments based on realistic data set show that the rate of successful verification can achieve 70% to 80% at rush hours.
},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@misc{Asabe2022,
title = {AutowareV2X: Enabling V2X Communication and Collective Perception for Autonomous Driving},
author = {Yu Asabe and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
url = {https://tlab.hongo.wide.ad.jp/papers/2022_AINTEC2022_poster_asabe.pdf
https://tlab-wide.github.io/AutowareV2X/main/},
year = {2022},
date = {2022-12-20},
urldate = {2022-12-20},
abstract = {For cooperative intelligent transport systems (C-ITS), vehicle-to-everything (V2X) communication is utilized to allow autonomous vehicles to share critical information with each other, enabling cooperatively enhanced environmental awareness and decision-making. We propose AutowareV2X, an implementation of a V2X communication module that is integrated into the autonomous driving (AD) software, Autoware. AutowareV2X provides external connectivity to the entire AD stack, enabling the end-to-end experimentation and evaluation of connected autonomous vehicles (CAV).},
howpublished = {Asian Internet Engineering Conference (AINTEC) 2022 Poster},
note = {Best Poster Award},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
@misc{Kambara2022,
title = {Towards Cooperative Automated Driving: Geographic-Aware Network Analysis and Visualization tool},
author = {Koichi Kambara and Ehsan Javanmardi and Jin Nakazato and Yousuke Watanabe and Kenya Sato and Hiroaki Takada and Manabu Tsukada},
url = {https://tlab.hongo.wide.ad.jp/papers/2022_AINTEC2022_poster_kambara.pdf},
year = {2022},
date = {2022-12-19},
urldate = {2022-12-19},
abstract = {In recent years the cooperative automated vehicle (CAV) concept has been gaining attention due to its potential to increase traffic safety and traffic flow by utilizing the vehicle-to-everything communication capability. One of the key requirements for CAV is ensuring every vehicle receives relevant messages at the right time and place; therefore, measuring and visualizing network performance is vital. However, for CAV applications, more network analyzers than those extant are needed because these do not consider geographical characteristics. In this study, we proposed a geographically-aware CAV-specific network analysis and visualization tool that can report the network performance factors such as packet loss, bandwidth, and jitter in real time. Further, we developed a proposal tool and evaluated it in an outdoor proof-of-concept study at the University of Tokyo’s Hongo Campus.},
howpublished = {Asian Internet Engineering Conference (AINTEC) 2022 Poster},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
@article{Asad2022,
title = {SHFL: K-Anonymity-based Secure Hierarchical Federated Learning Framework for Smart Healthcare Systems},
author = {Muhammad Asad and Muhammad Aslam and Syeda Fizzah Jilani and Saima Shoukat and Manabu Tsukada},
url = {https://www.mdpi.com/1999-5903/14/11/338},
doi = {10.3390/fi14110338},
isbn = {1999-5903},
year = {2022},
date = {2022-11-18},
urldate = {2022-11-18},
journal = {Future Internet},
volume = {14},
number = {11},
abstract = {Dynamic and smart infrastructures of the Internet of Things (IoT) allow the development of smart healthcare systems. These smart healthcare systems are equipped with mobile health and embedded healthcare sensors to provide a broad range of healthcare applications. These IoT applications provide the key availability of clients’ health information. However, the boost in the number of mobile devices and social networks intends to share the locations without the clients’ concern. In this regard, Federated Learning (FL) is an emerging paradigm of decentralized machine learning that guarantees to train a shared global model without compromising client data privacy. To this end, in this paper, we propose a K-Anonymity-based Secure Hierarchical Federated Learning (SHFL) framework for smart healthcare systems. In the proposed hierarchical FL approach, a centralized server communicates with multiple directly and indirectly connected devices hierarchically. In particular, the proposed SHFL formulates the location-based services (LBS)-hierarchical clusters to execute distributed FL. Besides, the proposed SHFL utilizes the K-Anonymity method to hide the location of the clusters’ devices. In the end, we evaluate the performance of the proposed SHFL by configuring the different hierarchical networks with multiple model architectures and datasets. The experiments validate that the proposed SHFL provides a suitable generalization to enable network scalability of accurate healthcare systems without compromising data and location privacy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Masuda2022,
title = {Feature-based Vehicle Identification Framework for Optimization of Collective Perception Messages in Vehicular Networks},
author = {Hidetaka Masuda and Oussama El Marai and Manabu Tsukada and Tarik Taleb and Hiroshi Esaki},
doi = {10.1109/TVT.2022.3211852},
isbn = {0018-9545},
year = {2022},
date = {2022-10-04},
urldate = {2022-10-04},
journal = {IEEE Transactions on Vehicular Technology},
volume = {72},
issue = {2},
pages = {2120-2129},
abstract = {The world is moving towards a fully connected digital world, where objects produce and consume data, at a sultry pace. Autonomous vehicles will play a key role in bolstering the digitization of the world. These connected vehicles must communicate timely data with their surrounding objects and road participants to fully and accurately understand their environments and eventually operate smoothly. As a result, the hugely exchanged data would scramble the network traffic that, at a given point, would no longer increase the awareness level of the vehicle. In this paper, we propose a vision-based approach to identify connected vehicles and use it to optimize the exchange of collective perception messages (CPMs), in terms of both the CPM generation frequency and the number of generated CPMs. To validate our proposed approach, we created a Cartery framework that integrates SUMO, Carla, and OMNeT++. We also compared our solution with both baselines and European Telecommunications Standards Institute solutions, considering three main KPIs: the channel busy ratio, environmental awareness, and the CPM generation frequency. Simulation results show that our proposed solution exhibits the best trade-off between the network load and situational awareness.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@misc{Kiuchi2022,
title = {ZIGEN: A Windowing System Enabling Multitasking Among 3D and 2D Applications in Immersive Environments},
author = {Akihiro Kiuchi and Taishi Eguchi and Jun Rekimoto and Manabu Tsukada and Hiroshi Esaki},
doi = {10.1145/3532719.3543200},
year = {2022},
date = {2022-08-08},
urldate = {2022-08-08},
address = {Vancouver, Canada},
abstract = {In modern desktop environments, a windowing system allows multiple applications to be displayed simultaneously and work together, but in today's immersive environments, multitasking with multiple applications is very limited. Therefore, we designed and developed a windowing system, ZIGEN, from the ground up to achieve multitasking among 3D and 2D applications.},
howpublished = {ACM SIGGRAPH 2022 Posters},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
@inproceedings{Lin2022b,
title = {Cooperative Path Planning Using Responsibility-Sensitive Safety (RSS)-based Potential Field with Sigmoid Curve},
author = {Pengfei Lin and Manabu Tsukada},
url = {https://youtu.be/AhgptWUyzSc},
doi = {10.1109/VTC2022-Spring54318.2022.9860508},
year = {2022},
date = {2022-06-19},
urldate = {2022-06-19},
booktitle = {The 2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring)},
address = {Helsinki, Finland},
abstract = {Potential field (PF)-based path planning is reported to be highly efficient for autonomous vehicles because it performs risk-aware computation and has a simple structure. However, the inherent limitations of the PF make it vulnerable in some specific traffic scenarios, such as local minima and oscillations in close obstacles. Therefore, a hybrid path planning with the sigmoid curve has recently been presented to generate better trajectories than those generated by the PF for collision avoidance. However, it is time-consuming and less applicable in complex dynamic environments, especially in traffic emergencies. To address these limitations, we propose a cooperative hybrid path planning (CHPP) approach that involves collaboration with adjacent vehicles for emergency collision avoidance via V2V communication. Moreover, the responsibility-sensitive safety (RSS) model is introduced to enhance the PF and sigmoid curve for safe-critical and time-saving requirements. The effectiveness of the proposed CHPP method compared with the state-of-the-art methods is studied through simulation of both static and dynamic traffic emergency scenarios. The simulation results prove that the CHPP approach performs better in terms of computation time (0.02 s faster) and driving safety (avoiding collision) than other methods, which are more supportive for emergency cooperative driving.
},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Lin2022c,
title = {Adaptive Potential Field with Collision Avoidance for Connected Autonomous Vehicles},
author = {Pengfei Lin and Manabu Tsukada},
doi = {10.23919/ASCC56756.2022.9828160},
year = {2022},
date = {2022-05-03},
urldate = {2022-05-03},
booktitle = {13th Asian Control Conference (ASCC) 2022},
address = {Jeju, Korea},
abstract = {Potential field (PF), as a risk assessment method, is proposed to enhance autonomous vehicles’ (AVs) safety in collision avoidance. However, current PF targets mainly standalone-mode AVs (SAVs) by evaluating their relative position and velocity. In addition, the risk energy of the PF is usually assigned an infinite value along the z-axis. Therefore, this study presents an adaptive potential field (APF) for connected autonomous vehicles (CAVs). Valuable information (heading angle, steering wheel angle, etc.) other than relative position and velocity is supplemented to PF. Furthermore, we separate the APF from the cost function of the model predictive controller (MPC) to compute the desired reference signals directly, saving more computation time. The proposed APF-MPC is co-simulated in a comparative driving scenario via MATLAB/Simulink and CarSim simulator compared with the latest PF-MPC method.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Lin2022,
title = {Model Predictive Path-Planning Controller with Potential Function for Emergency Collision Avoidance on Highway Driving},
author = {Pengfei Lin and Manabu Tsukada},
doi = {10.1109/LRA.2022.3152693},
isbn = {2377-3766},
year = {2022},
date = {2022-04-22},
urldate = {2022-04-22},
journal = {Robotics and Automation Letters (RA-L) with IEEE International Conference on Robotics and Automation (ICRA) option},
volume = {7},
issue = {2},
pages = {4662-4669},
abstract = {Existing potential functions (PFs) utilized in autonomous vehicles mainly focus on solving the path-planning problems in some conventional driving scenarios; thus, their performance may not be satisfactory in the context of emergency obstacle avoidance. Therefore, we propose a novel model predictive path-planning controller (MPPC) combined with PFs to handle complex traffic scenarios (e.g., emergency avoidance when a sudden accident occurs). Specifically, to enhance the safety of the PFs, we developed an MPPC to handle an emergency case with a sigmoid-based safe passage embedded in the MPC constraints (SPMPC) with a specific triggering analysis algorithm on monitoring traffic emergencies. The presented PF-SPMPC algorithm was compiled in a comparative simulation study using MATLAB/Simulink and CarSim. The algorithm outperformed the latest PF-MPC approach to eliminate the severe tire oscillations and guarantee autonomous driving safety when handling the traffic emergency avoidance scenario.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Lin2022d,
title = {Building a speech recognition system with privacy identification information based on Google Voice for social robots},
author = {Pei‑Chun Lin and Benjamin Yankson and Vishal Chauhan and Manabu Tsukada},
doi = {10.1007/s11227-022-04487-3},
isbn = {1573-0484},
year = {2022},
date = {2022-04-12},
journal = {The Journal of Supercomputing},
abstract = {Currently, many smart speakers, even social robots, appear on the market to help people's lives become more convenient. Usually, people use smart speakers to check their daily schedule or control home appliances in their house. Many social robots also include smart speakers. They have the common property of being used in voice control machines. Regardless of where the smart speaker is installed and used, when people start a conversation with voice equipment, a security or privacy risk is exposed. Hence, we want to build a speech recognition (SR) that contains the privacy identification information (PII) system in this paper. We call this the SR-PII system. We used a Google Artificial-Intelligence-Yourself (AIY) Voice Kit released from Google to build a simple, smart dialog speaker and included our SR-PII system. In our experiments, we test SR accuracy and the reliability of privacy settings in three environments (quiet, noise, and playing music). We also examine the cloud response and speaker response times during our experiments. The results show that the speaker response is approximately 3.74 s in the cloud environment and approximately 9.04 s from the speaker. We also showed the response accuracy of the speaker, which successfully prevented personal information with the SR-PII system in three environments. The speaker has a response mean time of approximately 8.86 s with 93{%} mean accuracy in a quiet room, approximately 9.18 s with 89{%} mean accuracy in a noisy environment, and approximately 9.62 s with 90{%} mean accuracy in an environment that plays music. We conclude that the SR-PII system can secure private information and that the most important factor affecting the response speed of the speaker is the network connection status. We hope that people can, through our experiments, have some guidelines in building social robots and installing the SR-PII system to protect users’ personal identification information.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Tsukada2022,
title = {Misbehavior Detection Using Collective Perception under Privacy Considerations},
author = {Manabu Tsukada and Shimpei Arii and Hideya Ochiai and Hiroshi Esaki},
url = {https://arxiv.org/abs/2111.03461
https://youtu.be/UeHoSv5OAuc},
doi = {10.1109/CCNC49033.2022.9700564},
year = {2022},
date = {2022-01-08},
urldate = {2022-01-08},
booktitle = {2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC)},
address = {Online},
abstract = {In cooperative ITS, security and privacy protection are essential. Cooperative Awareness Message (CAM) is a basic V2V message standard, and misbehavior detection is critical for protection against attacking CAMs from the inside system, in addition to node authentication by Public Key Infrastructure (PKI). On the contrary, pseudonym IDs, which have been introduced to protect privacy from tracking, make it challenging to perform misbehavior detection. In this study, we improve the performance of misbehavior detection using observation data of other vehicles. This is referred to as collective perception message (CPM), which is becoming the new standard in European countries. We have experimented using realistic traffic scenarios and succeeded in reducing the rate of rejecting valid CAMs (false positive) by approximately 15 percentage points while maintaining the rate of correctly detecting attacks (true positive).},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@misc{Tsukada2021,
title = {[Invited talk] Cooperative Automated Driving Using Vehicle-to-Everything (V2X)},
author = {Manabu Tsukada},
url = {https://interlab.ait.ac.th/aintec2021/
https://youtu.be/WreVkQEGIKk
https://tlab.hongo.wide.ad.jp/papers/20211124-AINTEC2021-talk2.pdf
},
year = {2021},
date = {2021-12-16},
urldate = {2021-12-16},
address = {Online},
abstract = {Autonomous automatic driving is essentially a replacement for humans. It has the same limitations as human drivers because it uses onboard sensors and computers to drive based on localized information. Cooperative automatic driving is expected to achieve a level of safety and efficiency that has not been possible with human driving imitation by accurately perceiving a wide range of physical space through V2X communication. This talk will introduce the prospects of cooperative automated driving and the attempts of cooperative perception and cooperative planning implemented using Autoware, an automated driving software.},
howpublished = {Invited talk at the 16th Asian Internet Engineering Conference (AINTEC), Virtual Conference, 14-17 December 2021},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
@inproceedings{Nakagawa2021,
title = {WebRTC-based measurement tool for peer-to-peer applications and preliminary findings with real users},
author = {Kosuke Nakagawa and Manabu Tsukada and Keiichi Shima and Hiroshi Esaki},
url = {http://arxiv.org/abs/2112.02163
https://youtu.be/4XeCpuBLa7E},
doi = {10.1145/3497777.3498544},
year = {2021},
date = {2021-12-14},
urldate = {2021-12-14},
booktitle = {16th Asian Internet Engineering Conference (AINTEC)},
address = {Online},
abstract = {Direct peer-to-peer (P2P) communication is often used to minimize the end-to-end latency for real-time applications that require accurate synchronization, such as remote musical ensembles. However, there are few studies on the performance of P2P communication between home network environments, thus hindering the deployment of services that require synchronization. In this study, we developed a P2P performance measurement tool using the Web Real-Time Communication (WebRTC) statistics application programming interface. Using this tool, we can easily measure P2P performance between home network environments on a web browser without downloading client applications. We also verified the reliability of round-trip time (RTT) measurements using WebRTC and confirmed that our system could provide the necessary measurement accuracy for RTT and jitter measurements for real-time applications. In addition, we measured the performance of a full mesh topology connection with 10 users in an actual environment in Japan. Consequently, we found that only 66% of the peer connections had a latency of 30 ms or less, which is the minimum requirement for high synchronization applications, such as musical ensembles.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Cai2021,
title = {MAC address randomization tolerant crowd monitoring system using Wi-Fi packets},
author = {Yuyi Cai and Manabu Tsukada and Hideya Ochiai and Hiroshi Esaki},
url = {http://arxiv.org/abs/2112.02161
https://youtu.be/gVkdQ8LWDdI},
doi = {10.1145/3497777.3498547},
year = {2021},
date = {2021-12-14},
urldate = {2021-12-14},
booktitle = {16th Asian Internet Engineering Conference (AINTEC)},
address = {Online},
abstract = {Media access control (MAC) addresses inside Wi-Fi packets can be used for beneficial activities such as crowdedness estimation, marketing, and hazard maps. However, the MAC address randomization systems introduced around 2014 make all conventional MAC-address-based crowd monitoring systems count the same device more than once. Therefore, there is a need to create a new crowd monitoring system tolerant to MAC address randomization to estimate the number of devices accurately. In this paper, Vision and TrueSight, two new crowd monitoring algorithms that estimate the number of devices, are proposed to prove that MAC-address-based crowd monitoring is still possible. In addition to probe requests, Vision uses data packets and beacon packets to mitigate the influence of randomization. Moreover, TrueSight uses sequence numbers and hierarchical clustering to estimate the number of devices. The experimental results of this study show that even without installing any special software, Vision can gather 440 randomly generated MAC addresses into one group and count only once, and TrueSight can estimate the number of devices with an accuracy of more than 75% with an acceptable error range of 1.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Chen2021,
title = {Reinforcement Learning Based Optimal Camera Placement for Depth Observation of Indoor Scenes},
author = {Yichuan Chen and Manabu Tsukada and Hiroshi Esaki},
url = {https://arxiv.org/abs/2110.11106},
doi = {10.1109/ICNSC52481.2021.9702214},
year = {2021},
date = {2021-12-03},
urldate = {2021-12-03},
booktitle = {The 2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)},
address = {Xiamen, China},
abstract = {Exploring the most task-friendly camera setting---optimal camera placement (OCP) problem---in tasks that use multiple cameras is of great importance. However, few existing OCP solutions specialize in depth observation of indoor scenes, and most versatile solutions work offline. To this problem, an OCP online solution to depth observation of indoor scenes based on reinforcement learning is proposed in this paper. The proposed solution comprises a simulation environment that implements scene observation and reward estimation using shadow maps and an agent network containing a soft actor-critic (SAC)-based reinforcement learning backbone and a feature extractor to extract features from the observed point cloud layer-by-layer. Comparative experiments with two state-of-the-art optimization-based offline methods are conducted. The experimental results indicate that the proposed system outperforms seven out of ten test scenes in obtaining lower depth observation error. The total error in all test scenes is also less than 90% of the baseline ones. Therefore, the proposed system is more competent for depth camera placement in scenarios where there is no prior knowledge of the scenes or where a lower depth observation error is the main objective.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Hirata2021b,
title = {Roadside-assisted Cooperative Planning using Future Path Sharing for Autonomous Driving},
author = {Mai Hirata and Manabu Tsukada and Keisuke Okumura and Yasumasa Tamura and Hideya Ochiai and Xavier Défago},
url = {https://arxiv.org/abs/2108.04629
https://youtu.be/xaBIQC0SClE},
doi = {10.1109/VTC2021-Fall52928.2021.9625324},
year = {2021},
date = {2021-09-27},
urldate = {2021-09-27},
booktitle = {IEEE 94th Vehicular Technology Conference (VTC2021-Fall)},
address = {Online},
abstract = {Cooperative intelligent transportation systems (ITS) are used by autonomous vehicles to communicate with surrounding autonomous vehicles and roadside units (RSU). Current C-ITS applications focus primarily on real-time information sharing, such as cooperative perception. In addition to real-time information sharing, self-driving cars need to coordinate their action plans to achieve higher safety and efficiency. For this reason, this study defines a vehicle’s future action plan/path and designs a cooperative path-planning model at intersections using future path sharing based on the future path information of multiple vehicles. The notion is that when the RSU detects a potential conflict of vehicle paths or an acceleration opportunity according to the shared future paths, it will generate a coordinated path update that adjusts the speeds of the vehicles. We implemented the proposed method using the open-source Autoware autonomous driving software and evaluated it with the LGSVL autonomous vehicle simulator. We conducted simulation experiments with two vehicles at a blind intersection scenario, finding that each car can travel safely and more efficiently by planning a path that reflects the action plans of all vehicles involved. The time consumed by introducing the RSU is 23.0 % and 28.1 % shorter than that of the stand-alone autonomous driving case at the intersection.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Mizutani2021b,
title = {AutoMCM: Maneuver Coordination Service with Abstracted Functions for Autonomous Driving},
author = {Masaya Mizutani and Manabu Tsukada and Hiroshi Esaki},
url = {https://arxiv.org/abs/2107.06627
https://youtu.be/eC7L0R_1Ybo
https://youtu.be/s3l5zypizxQ
https://youtu.be/XBpZeT-apGE},
doi = {10.1109/ITSC48978.2021.9564556},
year = {2021},
date = {2021-09-19},
urldate = {2021-09-19},
booktitle = {24th IEEE International Conference on Intelligent Transportation (ITSC)},
address = {Indianapolis, IN, United States},
abstract = {A cooperative intelligent transport system (C-ITS) uses vehicle-to-everything (V2X) technology to make self-driving vehicles safer and more efficient. Current C-ITS applications have mainly focused on real-time information sharing, such as for cooperative perception. In addition to better real-time perception, self-driving vehicles need to achieve higher safety and efficiency by coordinating action plans. This study designs a maneuver coordination (MC) protocol that uses seven messages to cover various scenarios and an abstracted MC support service. We implement our proposal as AutoMCM by extending two open-source software tools: Autoware for autonomous driving and OpenC2X for C-ITS. The results show that our system effectively reduces the communication bandwidth by limiting message exchange in an event-driven manner. Furthermore, it shows that the vehicles run 15% faster when the vehicle speed is 30 km/h and 28% faster when the vehicle speed is 50 km/h using our scheme. Our system shows robustness against packet loss in experiments when the message timeout parameters are appropriately set.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@workshop{Mizutani2020,
title = {3D maps distribution of self-driving vehicles using roadside edges},
author = {Masaya Mizutani and Manabu Tsukada and Yuki Iida and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-02997520/document?.pdf
https://youtu.be/JBuX3JKDVjA},
doi = {10.1109/CANDARW51189.2020.00021},
year = {2020},
date = {2020-11-25},
urldate = {2020-11-25},
booktitle = {2020 Eighth International Symposium on Computing and Networking Workshops (CANDARW)},
pages = {40-45},
publisher = {IEEE},
address = {Okinawa, Japan},
abstract = {Three-dimensional (3D) maps have become a shared digital infrastructure for autonomous vehicles, especially in urban areas. Point Cloud Data (PCD) maps are used for scan matching to enable self-localization. Autonomous vehicles need to maintain PCD maps along with the destination that is often decided on demand and to keep the PCD map updated. In this paper, we propose a system that delivers PCD maps cached at roadside edges in real time. We implement the system in Autoware, open-source software for autonomous driving. Subsequently, we evaluate whether the autonomous vehicle can simultaneously download the PCD map from its edge and enable self-localization. Our results show that autonomous vehicles can perform self-localization while downloading the PCD map from the edge server. Additionally, we measure the download time with variable bandwidth and examine the bandwidth in which the self-localization normally operates. In our results, the download time of the PCD map at 100 Mbps was 698 ms at maximum, and the possibility of downloading PCD maps through 4G communication is shown.},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@inproceedings{Tsukada2020,
title = {AutoC2X: Open-source software to realize V2X cooperative perception among autonomous vehicles},
author = {Manabu Tsukada and Takaharu Oi and Akihide Ito and Mai Hirata and Hiroshi Esaki},
url = {https://github.com/esakilab/AutoC2X-AW
https://hal.archives-ouvertes.fr/hal-02942051/document?.pdf
https://youtu.be/kyv0sTyCIgU},
doi = {10.1109/VTC2020-Fall49728.2020.9348525},
year = {2020},
date = {2020-11-18},
urldate = {2020-11-18},
booktitle = {The 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall)},
address = {Victoria, B.C., Canada},
abstract = {The realization of vehicle-to-everything (V2X) communication enhances the capabilities of autonomous vehicles in terms of safety efficiency and comfort. In particular, sensor data sharing, known as cooperative perception, is a crucial technique to accommodate vulnerable road users in a cooperative intelligent transport system (ITS). In this regard, open-source software plays a significant role in prototyping, validation, and deployment. Specifically, in the developer community, Autoware is a popular open-source software for self-driving vehicles, and OpenC2X is an open-source experimental and prototyping platform for cooperative ITS. This paper reports on a system named AutoC2X to enable cooperative perception by using OpenC2X for Autoware-based autonomous vehicles. The developed system is evaluated by conducting field experiments involving real hardware. The results demonstrate that AutoC2X can deliver the cooperative perception message within 100 ms in the worst case. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Inokuchi2020b,
title = {co-Sound: An interactive medium with WebAR and spatial synchronization },
author = {Kazuma Inokuchi and Manabu Tsukada and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-02942505/document?.pdf
https://youtu.be/Bn1yPlbgqaA},
doi = {10.1007/978-3-030-65736-9_22},
year = {2020},
date = {2020-11-10},
booktitle = {The 19th IFIP International Conference on Entertainment Computing (ICEC) 2020},
pages = {255-263},
publisher = {Springer, Cham},
address = {Xi'an, China},
abstract = {An Internet-based media service platform can control recording processes and manage video and audio data interconnected by an IP network. Furthermore, the design and implementation of an object-based system for recording enable the flexible playback of the viewing contents. Augmented Reality (AR) is a three-dimensional video projection technology that allows us to interact with both elements in real space and digital space information. However, there are few examples of its use as a method for audio-visual media platforms. In this study, we propose co-Sound, which is an interactive audio-visual playback application for music events, using WebAR. co-Sound was designed as a multimodal interface that dynamically renders object-based AR in response to various actions from viewers on a web browser with low entry costs. Furthermore, by sharing AR objects among multiple devices in real time and bidirectionally, the relationship between users and contents was extended, and interaction among multiple users in the same AR space was possible. We implemented a prototype application, measured the performance of the AR spatial synchronization, and conducted a questionnaire-based evaluation. For subjective evaluation, 25 people experienced co-Sound and completed a questionnaire. We confirmed that the system was developed by object-based method with AR, achieved low-latency synchronization to accept operations from multiple users in real time, and the general acceptance of the system was very high.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Tsukada2020b,
title = {Networked Roadside Perception Units for Autonomous Driving},
author = {Manabu Tsukada and Takaharu Oi and Masahiro Kitazawa and Hiroshi Esaki
},
url = {https://www.mdpi.com/1424-8220/20/18/5320/pdf?.pdf
https://youtu.be/n7gD0L7NDEM},
doi = {10.3390/s20185320},
issn = {1424-8220},
year = {2020},
date = {2020-09-17},
urldate = {2020-09-17},
journal = {MDPI Sensors},
volume = {20},
number = {18},
abstract = {Vehicle-to-Everything (V2X) communication enhances the capability of autonomous driving through better safety, efficiency, and comfort. In particular, sensor data sharing, known as cooperative perception, is a crucial technique to accommodate vulnerable road users in a cooperative intelligent transport system (ITS). In this paper, we describe a roadside perception unit (RSPU) that combines sensors and roadside units (RSUs) for infrastructure-based cooperative perception. We propose a software called AutoC2X that we designed to realize cooperative perception for RSPUs and vehicles. We also propose the concept of networked RSPUs, which is the inter-connection of RSPUs along a road over a wired network, and helps realize broader cooperative perception. We evaluated the RSPU system and the networked RSPUs through a field test, numerical analysis, and simulation experiments. Field evaluation showed that, even in the worst case, our RSPU system can deliver messages to an autonomous vehicle within 100 ms. The simulation result shows that the proposed priority algorithm achieves a wide perception range with a high delivery ratio and low latency, especially under heavy road traffic conditions. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Kato2020,
title = {Web360^{2}: An Interactive Web Application for viewing 3D Audio-visual Contents},
author = {Shin Kato and Tomohiro Ikeda and Mitsuaki Kawamorita and Manabu Tsukada and Hiroshi Esaki},
url = {https://zenodo.org/record/3898664/files/SMCCIM_2020_paper_102.pdf
https://github.com/sdm-wg/web360square
https://youtu.be/qg7aGhzO2Nc},
doi = {10.5281/zenodo.3898664},
year = {2020},
date = {2020-06-25},
booktitle = {17th Sound and Music Computing Conference (SMC)},
pages = {32-39},
address = {Torino, Italy},
abstract = {The use of video streaming services is expanding, and currently accounts for the majority of downstream Internet traffic. With the availability of virtual reality (VR) services and 360-degree cameras for consumer use, 3D services are also gaining in popularity. In recent years, the technology supporting for 3D representation on the Web has advanced. Users can easily utilize this technology without installing dedicated applications. In this study, we design and implement a Web application, called “Web360$^2$,” which plays 360-degree video and object-based 3D sounds interactively on the Web. We also evaluated Web360$^2$ through a questionnaire survey.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@misc{Tsukada2020bb,
title = {Roadside cooperative perception for autonomous vehicles},
author = {Manabu Tsukada},
url = {https://tlab.hongo.wide.ad.jp/jfli-workshop-2020/},
year = {2020},
date = {2020-02-17},
abstract = {Cooperative Intelligent Transportation Systems (ITS) are systems where the vehicles, the roadside infrastructure, central control centres and other elements exchange information to achieve better road safety, traffic efficiency and comfort of the road users. These cooperative elements share the information by organising Vehicular Ad-hoc Network (VANET) or using the mobile network (e.g. 3G and LTE) and realises various cooperative ITS services. To promote wider deployment of such services, many stakeholders made consensus that the communication platform must be based on a common architecture and drive rapid international standardisation (e.g. ISO, ETSI, IEEE). In the near future, autonomous vehicles also benefit from such communication platforms by enforcing the perception, planing, and decision-making. However the most basic Vehicle-to-Vehicle (V2V) messages standardized in Europe, US and Japan suffer from same issues such as 1) unable to receive messages from the object without V2V transmitter, 2) message loss because of obstacle and wireless range, 3) vulnerable for malfunctioning and malicious node. For the solution, we propose an infrastructure-assisted V2V messaging system to support cooperative autonomous driving. We design the system based on the ITS Station architecture standardized in ISO/ETSI, working with any vehicle sensing technology. Moreover, we implement the prototype roadside system with a stereo vision for the vehicle sensing. The prototype system is evaluated in a field test in the campus of the University of Tokyo. The results show that the proposed system significantly improves the coverage of V2V messaging while the system overhead is limited. The proposal is being integrated to our personal mobility autonomous vehicle system based on an open source software. We are also planning the contribution to the international standardization of the technique.},
howpublished = {JFLI Workshop 2020 on Next Generation Networking},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
@inproceedings{Tsukada2019b,
title = {Cooperative awareness using roadside unit networks in mixed traffic},
author = {Manabu Tsukada and Masahiro Kitazawa and Takaharu Oi and Hideya Ochiai and Hiroshi Esaki
},
url = {https://hal.archives-ouvertes.fr/hal-02335068/?.pdf},
doi = {10.1109/VNC48660.2019.9062773},
year = {2019},
date = {2019-12-04},
booktitle = {2019 IEEE Vehicular Networking Conference (VNC)},
pages = {9-16},
abstract = {Vehicle-to-vehicle (V2V) messaging is an indispensable component of connected autonomous vehicle systems. Although V2V standards have been specified by the European Union, United States, and Japan, the deployment phase represents mixed traffic in which connected and legacy vehicles co-exist. To enhance cooperative awareness in this mixed traffic, we assessed the special roadside unit that we developed in our previous work that generates required V2V messages on behalf of sensed target vehicles. In this paper, we extend our earlier work to propose a system called “Grid Proxy Cooperative Awareness Message to broaden the cooperative awareness message dissemination area by connecting infrastructure using high-speed roadside networks. To minimize delay in message delivery, we designed the proposed system to use edge computing. The proposed scheme delivers cooperative messages to a wider area with a low delay and a high packet delivery ratio by prioritizing packets by their respective safety contributions. Our simulation results indicate that the proposed scheme efficiently delivers messages in heavy road traffic conditions modeled on real maps of Tokyo and Paris. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Kasuya2019,
title = {LiVRation: Remote VR live platform with interactive 3D audio-visual service},
author = {Takashi Kasuya and Manabu Tsukada and Yu Komohara and Shigeki Takasaka and Takuhiro Mizuno and Yoshitaka Nomura and Yuta Ueda and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-02136247/document?.pdf
https://youtu.be/4MElM4_t2Io},
doi = {10.1109/GEM.2019.8811549},
year = {2019},
date = {2019-06-19},
booktitle = {IEEE Games Entertainment & Media Conference (IEEE GEM) 2019},
pages = {1-7},
address = {Yale University, New Haven, CT, U.S.},
abstract = {Of late, various audio-visual services based on the internet are being deployed extensively. Among these, object- based audio-visual services are attracting more attention. In 2014, we had established the software defined media (SDM) consortium to investigate object-based and internet-based audio- visual services. Despite the increasing demand and popularity of live concert events, the placement of the microphone and camera limit the free-viewpoint watching of the contents of package media, such as DVDs. In this study, we design and implement an interactive 3D audio-visual service system called LiVRation, with a free-view-listen point. For subjective evaluation, 211 people were made to experience LiVRation and answer a questionnaire, subsequently. In addition, we demonstrated the system in the ”Billboard Live Hackasong 2017” hosted by Billboard Japan and received the first prize, based on the votes of the judges as well as the audience.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@workshop{Atarashi2018,
title = {The Software Defined Media Ontology for Music Events},
author = {Ray Atarashi and Takuro Sone and Yu Komohara and Manabu Tsukada and Takashi Kasuya and Hiraku Okumura and Masahiro Ikeda and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01879099/document?.pdf},
doi = {10.1145/3243907.3243915},
year = {2018},
date = {2018-10-08},
urldate = {2018-10-08},
booktitle = {Workshop on Semantic Applications for Audio and Music (SAAM) held in conjunction with ISWC 2018},
pages = {15-23},
address = {Monterey, California, USA.},
abstract = {With the advent of viewing services based on the Internet, the importance of object-based viewing services for interpreting objects existing in space and utilizing them as the content is increasing. Since 2014, the Software Defined Media Consortium has been researching object-based media and Internet-based viewing spaces. This paper defines a framework in event participants and professional recorders each freely share recorded data, and a third party can create an application based on the data. This study aims to provide an SDM ontology-based contents management mechanism with a detailed description of the object-based audio and video data and the recording environment. The data can be shared via the Internet and is highly reusable. We implemented this management mechanism and have developed and validated applications that are capable of interactively playing 3D content from any viewpoints freely.},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@article{Azuma2018,
title = {A Method of Misbehavior Detection with Mutual Vehicle Position Monitoring},
author = {Shuntaro Azuma and Manabu Tsukada and Kenya Sato},
url = {https://hal.archives-ouvertes.fr/hal-01879098/document?.pdf},
year = {2018},
date = {2018-06-10},
journal = {IntTech18v11n12, International Journal On Advances in Internet Technology},
volume = {11},
number = {1&2},
pages = {82-91},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Kitazawa2018,
title = {Wide transmission of Proxy Cooperative Awareness Message},
author = {Masahiro Kitazawa and Manabu Tsukada and Hideya Ochiai and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01879100/document?.pdf},
year = {2018},
date = {2018-06-10},
booktitle = {The Seventh International Conference on Advances in Vehicular Systems, Technologies and Applications (VEHICULAR 2018)},
pages = {54-59},
address = {Venice, Italy},
note = {Best Paper Award},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Azuma2018b,
title = {Improvement of False Positives in Misbehavoir Detection},
author = {Shuntaro Azuma and Manabu Tsukada and Kennya Sato},
url = {https://hal.archives-ouvertes.fr/hal-01879101/document?.pdf},
year = {2018},
date = {2018-06-10},
booktitle = {The Seventh International Conference on Advances in Vehicular Systems, Technologies and Applications (VEHICULAR 2018)},
pages = {78-83},
address = {Venice, Italy},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tsukada2017b,
title = {Roadside-Assisted V2V Messaging for Connected Autonomous Vehicle},
author = {Manabu Tsukada},
url = {https://hal.archives-ouvertes.fr/hal-01558066v2/document?.pdf},
year = {2017},
date = {2017-07-20},
booktitle = {The Thirteenth International Conference on Wireless and Mobile Communications (ICWMC 2017)},
pages = {89-94},
address = {Nice, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Azuma2017,
title = {A Method of Detecting Camouflage Data with Mutual Vehicle Position Monitoring},
author = {Shuntaro Azuma and Manabu Tsukada and Teruaki Nomura and Kenya Sato},
url = {https://hal.archives-ouvertes.fr/hal-01879103/document?.pdf},
year = {2017},
date = {2017-07-20},
booktitle = {The Sixth International Conference on Advances in Vehicular Systems, Technologies and Applications (VEHICULAR 2017)},
pages = {48-53},
address = {Nice, France},
note = {Best Paper Award},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tao2017b,
title = {Positioning and Perception in cooperative ITS application simulator},
author = {Ye Tao and Manabu Tsukada and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01879102/document?.pdf},
year = {2017},
date = {2017-07-20},
booktitle = {The Sixth International Conference on Advances in Vehicular Systems, Technologies and Applications (VEHICULAR 2017)},
pages = {54-59},
address = {Nice, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Kitazawa2017,
title = {Remote Proxy V2V Messaging using IPv6 and GeoNetworking},
author = {Masahiro Kitazawa and Manabu Tsukada and Kai Morino and Hideya Ochiai and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01578410/document?.pdf},
year = {2017},
date = {2017-07-10},
booktitle = {The Sixth International Conference on Advances in Vehicular Systems, Technologies and Applications (VEHICULAR 2017)},
pages = {74-80},
address = {Nice, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tsukada2017c,
title = {Software Defined Media: Virtualization of Audio-Visual Services},
author = {Manabu Tsukada and Keiko Ogawa and Masahiro Ikeda and Takuro Sone and Kenta Niwa and Shoichiro Saito and Takashi Kasuya and Hideki Sunahara and Hiroshi Esaki},
url = {https://arxiv.org/pdf/1702.07452.pdf},
doi = {10.1109/ICC.2017.7996610},
isbn = {1938-1883},
year = {2017},
date = {2017-05-21},
booktitle = {IEEE International Conference on Communications (ICC2017)},
pages = {1-7},
address = {Paris, France},
abstract = {Internet-native audio-visual services are witnessing rapid development. Among these services, object-based audio-visual services are gaining importance. In 2014, we established the Software Defined Media (SDM) consortium to target new research areas and markets involving object-based digital media and Internet-by-design audio-visual environments. In this paper, we introduce the SDM architecture that virtualizes networked audio-visual services along with the development of smart buildings and smart cities using Internet of Things (IoT) devices and smart building facilities. Moreover, we design the SDM architecture as a layered architecture to promote the development of innovative applications on the basis of rapid advancements in software-defined networking (SDN). Then, we implement a prototype system based on the architecture, present the system at an exhibition, and provide it as an SDM API to application developers at hackathons. Various types of applications are developed using the API at these events. An evaluation of SDM API access shows that the prototype SDM platform effectively provides 3D audio reproducibility and interactiveness for SDM applications.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Tao2017,
title = {Reliable Overlay Networking on ETSI GeoNetworking Standards},
author = {Ye Tao and Xin Li and Manabu Tsukada and Hiroshi Esaki},
url = {http://rdcu.be/qyX5},
doi = {https://doi.org/10.1007/s13177-017-0141-7},
isbn = {1348-8503},
year = {2017},
date = {2017-04-20},
journal = {International Journal of Intelligent Transportation Systems Research},
volume = {16},
number = {2},
pages = {98-111},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Kitazato2016,
title = {Proxy Cooperative Awareness Message: An Infrastructure-Assisted V2V Messaging},
author = {Tomoya Kitazato and Manabu Tsukada and Hideya Ochiai and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01341850/document?.pdf},
doi = {10.1109/ICMU.2016.7742092},
year = {2016},
date = {2016-10-04},
booktitle = {The Ninth International Conference on Mobile Computing and Ubiquitous Networking (ICMU2016)},
address = {DFKI Kaiserslautern, Kaiserslautern, Germany},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Ikeda2016,
title = {New Recording Application for Software Defined Media},
author = {Masahiro Ikeda and Takuro Sone and Kenta Niwa and Shoichiro Saito and Manabu Tsukada and Hiroshi Esaki},
url = {http://www.aes.org/e-lib/browse.cfm?elib=18414},
year = {2016},
date = {2016-09-10},
booktitle = {Audio Engineering Society Convention Paper, 141st AES Convention},
address = {Los Angeles, USA},
abstract = {In recent years, hardware-based systems are becoming software-based and networked. From IP based media networks, the notion of Software Defined Media (SDM) has arisen. SDM is an architectural approach to media as a service by virtualization and abstraction of networked infrastructure. With this approach, it would be possible to provide more flexible and versatile systems. To test this concept, a baroque orchestra was recorded by various methods with 82 channels of microphones in total. All the data was organized based on the object-based concept and we applied advanced signal processing to the data based on array signal processing technology to produce a content matching various purposes of possible applications. Through this study, the value of SDM concept is verified.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inbook{Sato2016,
title = {Probe Vehicle Information Systems},
author = {Masaaki Sato and Manabu Tsukada and Hiroshi Ito},
url = {https://www.crcpress.com/Intelligent-Transportation-Systems-From-Good-Practices-to-Standards/Pagano/p/book/9781498721868},
doi = {10.1201/9781315370866-9},
isbn = {9781498721868},
year = {2016},
date = {2016-08-20},
pages = {151-170},
publisher = {Intelligent Transportation Systems: From Good Practices to Standards, CRC Press Book},
chapter = {8},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@inproceedings{Tao2016,
title = {DUPE: Duplicated Unicast Packet Encapsulation in Position-Based Routing VANET},
author = {Ye Tao and Xin Li and Manabu Tsukada and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01321553/document?.pdf},
doi = {10.1109/WMNC.2016.7543979},
year = {2016},
date = {2016-07-20},
booktitle = {9th IFIP Wireless and Mobile Networking Conference (WMNC 2016)},
address = {Colmar, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Ikegami2015,
title = {Electric Current Based Power Line Communication for Plug-Load Device Auto Identification},
author = {Hiroyuki Ikegami and Manabu Tsukada and Hideya Ochiai and Hideaki Nii and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01317105/document?.pdf},
doi = {10.1109/SmartGridComm.2015.7436287},
year = {2015},
date = {2015-09-20},
booktitle = {IEEE International Conference on Smart Grid Communications (SmartGridComm 2015)},
address = {Miami, United States},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tao2015,
title = {Reproducing and Extending Real Testbed Evaluation of GeoNetworking Implementation in Simulated Networks},
author = {Ye Tao and Manabu Tsukada and Xin Li and Masatoshi Kakiuchi and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01317104/document?.pdf},
doi = {10.1145/2775088.2775092},
year = {2015},
date = {2015-06-20},
booktitle = {The 10th International Conference on Future Internet Technologies (CFI 2015)},
address = {Seoul, Korea},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Tsukada2014,
title = {On the Experimental Evaluation of Vehicular Networks: Issues, Requirements and Methodology Applied to a Real Use Case},
author = {Manabu Tsukada and Jos{'e} Santa and Satoshi Matsuura and Thierry Ernst and Kazutoshi Fujikawa},
url = {https://hal.inria.fr/hal-01095282/document?.pdf
https://youtu.be/NamJUd-_0jw},
doi = {http://dx.doi.org/10.4108/inis.1.1.e4},
isbn = {2410-0218},
year = {2014},
date = {2014-12-01},
journal = {EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
volume = {1},
number = {1},
pages = {1-14},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Li2014,
title = {MultiVehicle Cooperative Local Mapping: A Methodology Based on Occupancy Grid Map Merging},
author = {Hao Li and Manabu Tsukada and Fawzi Nashashibi and Michel Parent},
url = {https://hal.inria.fr/hal-01107534/file/LI_Nashashibi_Draft_Juillet2013.pdf},
doi = {10.1109/TITS.2014.2309639},
isbn = {1524-9050},
year = {2014},
date = {2014-10-10},
journal = {IEEE Transactions on Intelligent Transportation Systems},
volume = {15},
number = {5},
pages = {2089-2100},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Tsukada2014b,
title = {AnaVANET: an experiment and visualization tool for vehicular networks},
author = {Manabu Tsukada and José Santa and Satoshi Matsuura and Thierry Ernst and Kazutoshi Fujikawa},
url = {https://hal.inria.fr/hal-00983479/document?.pdf
https://youtu.be/NamJUd-_0jw},
doi = {10.1007/978-3-319-13326-3_13},
year = {2014},
date = {2014-05-20},
booktitle = {9th International Conference on Testbeds and Research Infrastructures for the Development of Networks & Communities (TRIDENTCOM 2014)},
address = {Guangzhou, China},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@workshop{Shagdar2012,
title = {Experimentation Towards IPv6 over IEEE 802.11p with ITS Station Architecture},
author = {Oyunchimeg Shagdar and Manabu Tsukada and Kakiuchi Masatoshi and Thouraya Toukabri and Thierry Ernst},
url = {https://hal.inria.fr/hal-00702923/document?.pdf},
year = {2012},
date = {2012-06-20},
urldate = {2012-06-20},
booktitle = {International Workshop on IPv6-based Vehicular Networks (Vehi6 2012) colocated with IEEE Intelligent Vehicles Symposium},
address = {Madrid, Spain},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@article{Lee2012,
title = {MNPP: Mobile Network Prefix Provisioning for Enabling Route Optimization in Geographic Vehicular Networks},
author = {Jong-Hyouk Lee and Manabu Tsukada and Thierry Ernst},
url = {http://www.oldcitypublishing.com/journals/ahswn-home/ahswn-issue-contents/ahswn-volume-15-number-1-2-2012/},
issn = {1551-9899},
year = {2012},
date = {2012-06-13},
journal = {AHSWN - Ad Hoc & Sensor Wireless Networks},
volume = {15},
number = {1},
pages = {5-19},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Noguchi2012,
title = {Design and Field evaluation of geographical location-aware service discovery on IPv6 GeoNetworking for VANET},
author = {Satoru Noguchi and Manabu Tsukada and Thierry Ernst and Astuo Inomata and Kazutoshi Fujikawa
},
url = {https://hal.inria.fr/hal-00784409/document?.pdf},
doi = {10.1186/1687-1499-2012-29},
isbn = {1687-1499},
year = {2012},
date = {2012-02-20},
journal = {special issue (SI) of "Network Routing and Communication Algorithm for Intelligent Transportation Systems" in EURASIP Journal on Wireless Communications and Networking},
pages = {1-16},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Noguchi2011,
title = {Location-aware service discovery on IPv6 GeoNetworking for VANET},
author = {Satoru Noguchi and Manabu Tsukada and Thierry Ernst and Atsuo Inomata and Kazutoshi Fujikawa},
url = {https://hal.inria.fr/inria-00625796/document?.pdf},
doi = {10.1109/ITST.2011.6060058},
year = {2011},
date = {2011-08-20},
booktitle = {11th International Conference on Intelligent Transport System Telecommunications (ITST 2011)},
address = {Saint-Petersburg, Russia},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Toukabri2011,
title = {Experimental evaluation of an open source implementation of IPv6 GeoNetworking in VANETs},
author = {Thouraya Toukabri and Manabu Tsukada and Thierry Ernst and Lamjed Bettaieb},
url = {https://hal.inria.fr/inria-00625789/document?.pdf},
doi = {10.1109/ITST.2011.6060060},
year = {2011},
date = {2011-08-20},
booktitle = {11th International Conference on Intelligent Transport System Telecommunications (ITST 2011)},
address = {Saint-Petersburg, Russia},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Noguchi2011b,
title = {Real-vehicle integration of driver support application with IPv6 GeoNetworking},
author = {Satoru Noguchi and Manabu Tsukada and Ines Ben Jemaa and Thierry Ernst},
url = {https://hal.inria.fr/inria-00567852/document?.pdf},
doi = {10.1109/VETECS.2011.5956756},
year = {2011},
date = {2011-05-20},
booktitle = {2011 IEEE 73rd Vehicular Technology Conference (VTC2011-Spring)},
address = {Budapest, Hungary},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Jemaa2010,
title = {Validation and evaluation of NEMO in VANET using geographic routing},
author = {Ines Ben Jemaa and Manabu Tsukada and Hamid Menouar and Thierry Ernst},
url = {https://hal.inria.fr/inria-00567786/document?.pdf},
year = {2010},
date = {2010-11-20},
booktitle = {10th International Conference on Intelligent Transport System Telecommunications (ITST 2010)},
address = {Kyoto, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Tsukada2010,
title = {Design and Experimental Evaluation of a Vehicular Network Based on NEMO and MANET},
author = {Manabu Tsukada and José Santa and Olivier Mehani and Yacine Khaled and Thierry Ernst},
url = {https://hal.inria.fr/hal-00784433/document?.pdf
https://youtu.be/LtFrE8Ezho0},
doi = {10.1155/2010/656407},
isbn = {1687-6180},
year = {2010},
date = {2010-09-10},
journal = {The special issue for Vehicular Ad Hoc Networks, EURASIP Journal on Advances in Signal Processing},
volume = {2010},
pages = {1-16},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Tsukada2010b,
title = {Experimental Evaluation for IPv6 over VANET Geographic routing},
author = {Manabu Tsukada and Ines Ben Jemaa and Hamid Menouar and Wenhui Zhang and Maria Goleva and Thierry Ernst},
url = {https://hal.inria.fr/inria-00505921/document?.pdf
https://youtu.be/MFo12Nxik94},
doi = {10.1145/1815396.1815565},
year = {2010},
date = {2010-06-10},
booktitle = {6th International Wireless Communications and Mobile Computing Conference, IWCMC 2010},
address = {Caen, France},
note = {Best Paper Award},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inbook{Khaled2010,
title = {The Role of Communication Technologies in Vehicular Applications},
author = {Yacine Khaled and Manabu Tsukada and José Santa and Thierry Ernst},
url = {https://www.igi-global.com/book/advances-vehicular-hoc-networks/37323},
doi = {10.4018/978-1-61520-913-2},
isbn = {9781615209132},
year = {2010},
date = {2010-04-20},
pages = {37-57},
publisher = {Advances in Vehicular Ad-Hoc Networks: Developments and Challenges, IGI Global},
chapter = {3},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@inproceedings{Khaled2009b,
title = {Geographical information extension for IPv6: application to VANET},
author = {Yacine Khaled and Manabu Tsukada and Thierry Ernst},
url = {https://hal.inria.fr/inria-00567786/document?.pdf},
doi = {10.1109/ITST.2009.5399339},
year = {2009},
date = {2009-10-20},
booktitle = {9th International Conference on Intelligent Transport System Telecommunications (ITST 2009)},
address = {Lille, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Khaled2009c,
title = {Application of IPv6 multicast to VANET},
author = {Yacine Khaled and Ines Ben Jemaa and Manabu Tsukada and Thierry Ernst},
url = {https://ieeexplore.ieee.org/document/5399356/},
doi = {10.1109/ITST.2009.5399356},
year = {2009},
date = {2009-10-20},
booktitle = {9th International Conference on Intelligent Transport System Telecommunications (ITST 2009)},
address = {Lille, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Santa2009,
title = {Assessment of VANET multi-hop routing over an experimental platform},
author = {José Santa and Manabu Tsukada and Thierry Ernst and Olivier Mehani and Antonio F. Gómez-Skarmeta},
url = {https://hal.inria.fr/inria-00625837/document?.pdf},
doi = {10.1504/IJIPT.2009.028655},
issn = {1743-8209},
year = {2009},
date = {2009-09-30},
journal = {International Journal of Internet Protocol Technology},
volume = {4},
number = {3},
pages = {158-172},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Khaled2009,
title = {A usage oriented analysis of vehicular networks: from technologies to applications},
author = {Yacine Khaled and Manabu Tsukada and José Santa and JinHyeock Choi and Thierry Ernst},
url = {https://pdfs.semanticscholar.org/dea9/8e19ab72a60e2f5783066c5f7912efca3b64.pdf},
doi = {10.4304/jcm.4.5.357-368},
issn = {1796-2021},
year = {2009},
date = {2009-06-10},
journal = {Journal of Communications (JCM), Academy Publisher, Special Issue on Challenges in Future Vehicular AD HOC Networks, vol. 4, no. 5, pp. 357-368, May 2009},
volume = {4},
number = {5},
pages = {357-368},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@workshop{Santa2009b,
title = {Experimental Analysis of Multi-hop Routing in Vehicular Ad-hoc Networks},
author = {José Santa and Manabu Tsukada and Thierry Ernst and Antonio F. Gómez-Skarmeta},
url = {https://hal.inria.fr/inria-00625645/document?.pdf},
doi = {10.1109/TRIDENTCOM.2009.4976248},
year = {2009},
date = {2009-04-06},
urldate = {2009-04-06},
booktitle = {2nd Workshop on Experimental Evaluation and Deployment Experiences on Vehicular networks (WEEDEV 2009) in conjunction with TRIDENTCOM 2009},
journal = {2nd Workshop on Experimental Evaluation and Deployment Experiences on Vehicular networks (WEEDEV 2009) in conjunction with TRIDENTCOM 2009},
address = {Washington D.C., USA},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@inproceedings{Khaled2009d,
title = {On the Design of efficient Vehicular Applications},
author = {Yacine Khaled and Manabu Tsukada and José Santa and Thierry Ernst.},
url = {https://hal.inria.fr/inria-00355878/document?.pdf},
doi = {10.1109/VETECS.2009.5073727},
year = {2009},
date = {2009-04-06},
booktitle = {2009 IEEE 69th Vehicular Technology Conference (VTC2009-Spring)},
address = {Barcelona, Spain},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Choi2008,
title = {IPv6 support for VANET with geographical routing},
author = {JinHyeock Choi and Yacine Khaled and Manabu Tsukada and Thierry Ernst},
url = {https://hal.inria.fr/inria-00336450/document?.pdf},
doi = {10.1109/ITST.2008.4740261},
year = {2008},
date = {2008-10-22},
booktitle = {8th International Conference on Intelligent Transport System Telecommunications (ITST 2008)},
journal = {8th International Conference on Intelligent Transport System Telecommunications (ITST 2008)},
address = {Phuket, Thailand},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Montavont2008,
title = {Anemone: A ready-to-go testbed for IPv6 compliant Intelligent Transport Systems},
author = {Nicolas Montavont and Antoine Boutet and Tanguy Ropitault and Manabu Tsukada and Thierry Ernst and Jari Korva and Cesar Viho and Laszlo Bokor},
url = {https://ieeexplore.ieee.org/document/4740262/},
doi = {10.1109/ITST.2008.4740262},
year = {2008},
date = {2008-10-22},
booktitle = {8th International Conference on Intelligent Transport System Telecommunications (ITST 2008)},
address = {Phuket, Thailand},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@workshop{Tsukada2008,
title = {Simultaneous Usage of NEMO and MANET for Vehicular Communication},
author = {Manabu Tsukada and Olivier Mehani and Thierry Ernst},
url = {https://hal.inria.fr/inria-00265652/document?.pdf
https://youtu.be/LtFrE8Ezho0},
doi = {10.4108/weedev.2008.3118},
year = {2008},
date = {2008-03-18},
urldate = {2008-03-18},
booktitle = {1st Workshop on Experimental Evaluation and Deployment Experiences on Vehicular networks (WEEDEV 2008) conjunction with TRIDENTCOM 2008},
address = {Innsbruck, Austria},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@inproceedings{Dhraief2007,
title = {E-Bicycle Demonstration on the Tour De France},
author = {Amine Dhraief and Nicolas Montavont and Romain Kuntz and Manabu Tsukada},
doi = {10.1109/ICCGI.2007.23},
year = {2007},
date = {2007-03-04},
booktitle = {International Multi-Conference on Computing in the Global Information Technology (ICCGI '07)},
address = {Guadeloupe, French Caribbean},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@workshop{Tsukada2007,
title = {Vehicle Communication Experiment Environment With MANET And NEMO},
author = {Manabu Tsukada and Thierry Ernst},
doi = {doi:10.1109/SAINT-W.2007.104},
year = {2007},
date = {2007-01-15},
urldate = {2007-01-15},
booktitle = {The 2007 International Symposium on Applications and the Internet, Workshop on Network Mobility (SAINT WONEMO 2007)},
address = {Hiroshima, Japan},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@inproceedings{Tsukada2005,
title = {Dynamic Management of Multiple Mobile Routers},
author = {Manabu Tsukada and Thierry Ernst and Ryuji Wakikawa and Koshiro Mitsuya},
url = {https://www.nautilus6.org/doc/paper/20051116-ICON-NEMO-MMRM-ManabuT.pdf
https://youtu.be/fcKOUsYC6ro},
doi = {10.1109/ICON.2005.1635682},
year = {2005},
date = {2005-11-20},
booktitle = {IEEE Malaysia International Conference on Communications and IEEE International Conference on Networks (MICC & ICON 2005)},
volume = {2},
pages = {1108-1113},
address = {Kuala Lumpur, Malaysia},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{ITO2025108157,
title = {A multipath redundancy communication framework for enhancing 5G mobile communication quality},
author = {Koki Ito and Jin Nakazato and Romain Fontugne and Manabu Tsukada and Esaki Hiroshi},
url = {https://www.sciencedirect.com/science/article/pii/S0140366425001148},
doi = {https://doi.org/10.1016/j.comcom.2025.108157},
issn = {0140-3664},
year = {2025},
date = {2025-04-23},
urldate = {2025-04-23},
journal = {Computer Communications},
pages = {108157},
abstract = {As networks increasingly become the backbone of modern society, the demands placed on them by various applications have become more complex. In particular, the demand for high-capacity, low-latency services such as real-time streaming is increasing every year. Although 5G has been deployed to meet these needs, its effectiveness can vary significantly by location and time, and sometimes falls short of requirements. Traditionally, much of the research to improve communication stability has focused on TCP-based systems, which do not translate well to real-time UDP streaming applications. To address the above challenges, we propose a multipath redundant communication framework designed to improve the quality of real-time media streaming. This framework has been tested using multipath redundant communication over two mobile networks with a moving vehicle in an urban environment. Using a real-time streaming application based on WebRTC, our framework demonstrates a significant reduction in packet loss and an increase in bitrate, outperforming existing multipath redundant communication systems without interfering with the application’s congestion control mechanisms.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Asad2024b,
title = {Federated Learning for Secure and Efficient Vehicular Communications in Open RAN},
author = {Muhammad Asad and Saima Shaukat and Jin Nakazato and Ehsan Javanmardi and Manabu Tsukada},
url = {https://rdcu.be/d7RSW},
doi = {10.1007/s10586-024-04932-3},
issn = {1386-7857},
year = {2025},
date = {2025-01-28},
urldate = {2024-11-25},
journal = {Cluster Computing},
volume = {28},
number = {211},
abstract = {This paper presents a comprehensive exploration of federated learning applied to vehicular communications within the context of Open RAN. Through an in-depth review of existing literature and analysis of fundamental concepts, critical challenges are identified within the current methodologies employed in this sphere. A novel framework is proposed to address these shortcomings, fundamentally based on federated learning principles. This framework aims to enhance security and efficiency in vehicular communications, leveraging the flexibility of Open RAN architecture. The paper further delves into a rigorous justification of the proposed solution, highlighting its potential impact and the improvements it could bring to vehicular communications. Ultimately, this study provides a roadmap for future research in applying federated learning for more secure and efficient vehicular communications in Open RAN, opening up new avenues for exploration in this exciting interdisciplinary domain.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Zhou2024,
title = {Cellular Connected UAV Anti-Interference Path Planning Based on PDS-DDPG and TOPEM},
author = {Quanxi Zhou and Yongjing Wang and Ruiyu Shen and Jin Nakazato and Manabu Tsukada and Zhenyu Guan},
doi = {10.1109/JMASS.2024.3490762},
issn = {2576-3164},
year = {2024},
date = {2024-11-04},
urldate = {2024-11-04},
journal = {IEEE Journal on Miniaturization for Air and Space Systems},
abstract = {Due to the randomness of channel fading, communication devices, and malicious interference sources, unmanned aerial vehicles (UAVs) face a complex and ever-changing task scenario, which poses significant communication security challenges, such as transmission outages. Fortunately, these communication security challenges can be transformed into path planning problems that minimize the weighted sum of UAV mission time and transmission outage time. In order to design the complex communication environment faced by UAVs in actual scenarios, we propose a system model, including building distribution, communication channel, and antenna design in this paper. Besides, we introduce other UAVs with fixed flight paths and ground interference resources with random locations to ensure mission UAVs have better anti-interference ability. However, it is challenging for classical search algorithms and heuristic algorithms to cope with the complex path problems mentioned above. In this paper, we propose an improved deep deterministic policy gradient (DDPG) algorithm with better performance compared with basic DDPG and DDQN algorithms. Specifically, a post-decision state (PDS) mechanism has been introduced to accelerate the convergence rate and enhance the stability of the training process. In addition, a transmission outage probability experience memory (TOPEM) has been designed to quickly generate wireless communication quality maps and provide temporary experience for the post-decision process, resulting in better training results. Simulation experiments have proven that, compared to basic DDPG, the improved algorithm increases training speed by at least 50%, significantly improves convergence rate, and reduces the episode required for convergence to 20%. It can also help UAVs choose better paths than basic DDPG and DDQN algorithms.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {A Survey on Recent Advancements in Autonomous Driving Using Deep Reinforcement Learning: Applications, Challenges, and Solutions},
author = {Rui Zhao and Yun Li and Yuze Fan and Fei Gao and Manabu Tsukada and Zhenhai Gao},
doi = {10.1109/TITS.2024.3452480},
isbn = {1524-9050},
year = {2024},
date = {2024-09-18},
urldate = {2024-09-18},
journal = {IEEE Transactions on Intelligent Transportation Systems},
volume = {25},
issue = {2},
pages = {19365 - 19398},
abstract = {Autonomous driving (AD) endows vehicles with the capability to drive partly or entirely without human intervention. AD agents generate driving policies based on online perception results, which are crucial to the realization of safe, efficient, and comfortable driving behaviors, particularly in high-dimensional and stochastic traffic scenarios. Currently, deep reinforcement learning (DRL) techniques to derive and validate AD policies have witnessed vast research efforts and have shown rapid development in recent years. However, a comprehensive interpretation and evaluation of their strengths and limitations concerning the full-stack AD tasks remain uncharted. This paper presents a survey of this body of work, which is conducted at three levels. First, it analyzes the multi-level AD task characteristics and delves deeply into the current DRL methodologies primarily employed in AD. Second, a taxonomy of the literature studies is constructed from the system perspective, identifying six modes of DRL model integration into an AD architecture that span the entire spectrum of AD policy processes, from perception understanding and decision-making to motion control, as well as verification and validation. Each literature review comprehensively encompasses the main elements of designing such a system, including modeling partially observable environments, state and action spaces, reward structuring, and the design and training methodologies of neural network models. Finally, an in-depth foresight is conducted on how the eight critical issues of AD application development are addressed by the DRL models tailored for real-world AD challenges.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Asabe2024,
title = {Enhancing Reliability in Infrastructure-based Collective Perception: A Dual-Channel Hybrid Delivery Approach with Real-Time Monitoring},
author = {Yu Asabe and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
doi = {10.1109/OJVT.2024.3443877},
issn = {2644-1330},
year = {2024},
date = {2024-08-30},
urldate = {2024-08-30},
journal = {IEEE Open Journal of Vehicular Technology},
volume = {5},
pages = {1124-1138},
abstract = {Standalone autonomous vehicles primarily rely on their onboard sensors and may have blind spots or limited situational awareness in complex or dynamic traffic scenarios, leading to difficulties in making safe decisions. Collective perception enables connected autonomous vehicles (CAVs) to overcome the limitations of standalone autonomous vehicles by sharing sensory information with nearby road users. However, unfavorable conditions of the wireless communication medium it uses can lead to limited reliability and reduced quality of service. In this paper, we propose methods for increasing the reliability of collective perception through real-time packet delivery rate monitoring and a dual-channel hybrid delivery approach. We have implemented AutowareV2X, a vehicle-to-everything (V2X) communication module integrated into the autonomous driving (AD) software Autoware. AutowareV2X provides connectivity to the AD stack, enabling end-to-end (E2E) experimentation and evaluation of CAVs. The Collective Perception Service (CPS) was also implemented, allowing the transmission of Collective Perception Messages (CPMs). Our proposed methods using AutowareV2X were evaluated using actual hardware and vehicles in reallife field tests. Results have indicated that the E2E network latency of the perception information sent is around 30 ms, and the AD software can use shared object data to conduct collision avoidance maneuvers. The dual-channel delivery of CPMs enabled the CAV to dynamically select the best CPM from CPMs received from different links, depending on the freshness of their information. This enabled the reliable transmission of CPMs even when there was significant packet loss on one of the transmitting channels.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Lin2024b,
title = {Clothoid Curve-based Emergency Stopping Path-Planning with Adaptive Potential Field for Autonomous Vehicles},
author = {Pengfei Lin and Ehsan Javanmardi and Manabu Tsukada},
doi = {10.1109/TVT.2024.3380745},
issn = {0018-9545},
year = {2024},
date = {2024-07-24},
urldate = {2024-03-22},
journal = {IEEE Transactions on Vehicular Technology},
volume = {73},
issue = {7},
pages = {9747-9762},
abstract = {Potential Field-based path planning methods are widely embraced in the context of autonomous vehicles due to their real-time efficiency and simplicity. While the potential field effectively enforces a rigid road boundary to keep the vehicle within the confines of the road, it can lead to the “blind alley” problem caused by local minima in specific high- speed scenarios, resulting in indecision, erratic behavior, or even accidents. Therefore, the objective of this research is to anticipate and address the aforementioned problem in order to proactively avoid potential collisions. We have also found that existing methods do not offer a root cause analysis or practical solutions for this issue, which limits the practicality of the potential field in handling complicated traffic situations. In this paper, we propose an Emergency-Stopping Path Planning (ESPP) approach that incorporates an adaptive potential field with the clothoid curve. First, we design an emergency triggering estimation to detect the ”blind alley” problem. Second, we regionalize the driving scene to search for the optimal breach point on the road PF and the final stopping point for the vehicle by considering the motion range of the obstacle. Finally, we use the optimized clothoid curve to fit these calculated points under vehicle dynamics constraints to generate a smooth emergency avoidance path. The proposed ESPP method was evaluated by conducting the co-simulation between MATLAB/Simulink and CarSim Simulator in a freeway scene. The simulation results reveal that the proposed method shows increased performance in emergency collision avoidance and renders the vehicle safer, in which the duration of wheel slip is 61.9% shorter, and the maximum steering angle amplitude is 76.9% lower than other potential field-based methods.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{electronics13112037,
title = {A State-Interactive MAC Layer TDMA Protocol Based on Smart Antennas},
author = {Donghui Li and Jin Nakazato and Manabu Tsukada},
url = {https://www.mdpi.com/2079-9292/13/11/2037},
doi = {10.3390/electronics13112037},
issn = {2079-9292},
year = {2024},
date = {2024-05-23},
urldate = {2024-01-01},
journal = {Electronics},
volume = {13},
number = {11},
abstract = {Mobile ad hoc networks are self-organizing networks that do not rely on fixed infrastructure. Smart antennas employ advanced beamforming technology, enabling ultra-long-range directional transmission in wireless networks, which leads to lower power consumption and better utilization of spatial resources. The media access control (MAC) protocol design using smart antennas can lead to efficient usage of channel resources. However, during ultra-long-distance transmissions, there may be significant transport delays. In addition, when using the time division multiple access (TDMA) schemes, it can be difficult to manage conflicts arising from adjacent time slot advancement caused by latency compensation in ultra-long-range propagation. Directional transmission and reception can also cause interference between links that reuse the same time slot. This paper proposes a new distributed dynamic TDMA protocol called State Interaction-based Slot Allocation Protocol (SISAP) to address these issues. This protocol is based on slot states and includes TDMA frame structure, slot allocation process, interference self-avoidance strategy, and slot allocation algorithms. According to the simulation results, the MAC layer design scheme suggested in this paper can achieve ultra-long-distance transmission without conflicts. Additionally, it can reduce the interference between links while space multiplexing. Furthermore, the system exhibits remarkable performance in various network aspects, such as throughput and link delay.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Asad2024,
title = {Secure and Efficient Blockchain-based Federated Learning Approach For VANETs},
author = {Muhammad Asad and Saima Shaukat and Ehsan Javanmardi and Jin Nakazato and Naren Bao and Manabu Tsukada},
doi = {10.1109/JIOT.2023.3322221},
issn = {2327-4662},
year = {2024},
date = {2024-03-01},
urldate = {2023-10-05},
journal = {IEEE Internet of Things Journal},
volume = {11},
issue = {5},
pages = {9047-9055},
abstract = {The rapid increase in the number of connected vehicles on roads has made Vehicular Ad-hoc Networks (VANETs) an attractive target for malicious actors. As a result, VANETs require secure data transmission to maintain the network’s integrity. Federated Learning (FL) has been proposed as a secure data-sharing method for VANETs, but it is limited in its ability to protect sensitive data. This paper proposes integrating Blockchain technology into FL to provide an additional layer of security for VANETs. In particular, we propose a Secure and Efficient Blockchain-based FL (SEBFL) approach to ensure communication efficiency and data privacy in VANETs. To this end, we use the FL model for VANETs, where computation tasks are decomposed from a base station to individual vehicles. This effectively reduces the congestion delay and communication overhead. Integrating blockchain with the FL model provides a reliable and secure data communication system between vehicles, roadside units, and a cloud server. Additionally, we use a Homomorphic Encryption System (HES) that effectively preserves the confidentiality and credibility of vehicles. Besides, the proposed SEBFL leverages the asynchronous FL model, minimizing the long delay while avoiding possible threats and attacks using HES. The experiment results show the proposed SEBFL achieves 0.87% accuracy while a model inversion attack and 0.86% accuracy while a membership inference attack.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Location-Aided Fast Beam Tracking Algorithm for Millimeter-Wave V2I},
author = {Sojin Ozawa and Tokio Ikuta and Yuki Sasaki and Ryo Iwaki and Jin Nakazato and Manabu Tsukada and Hideya So and Kazuki Maruta},
doi = {10.23919/comex.2024XBL0001},
year = {2024},
date = {2024-02-15},
urldate = {2024-02-15},
journal = {IEICE Communications Express (ComEX)},
abstract = {This article proposes a millimeter-wave fast beam tracking algorithm for moving vehicles, considering a geometry of road environment. Focusing on the fact that vehicle movement is constrained on roads, horizontal and vertical beam directions are determined based on obtainable driving direction and road shape. In addition, we perform a two-pattern beam selection for the vehicle’s forward and rearward directions to esti- mate the beam tracking speed. By conducting simulations using SUMO, which emulates vehicle movement on various roads, we verified the effective operation of the proposed scheme and confirmed its superiority over the existing beam sweeping approach.},
note = {ComEX Top Downloaded Letter Award},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Tao2023b,
title = {Zero-Knowledge Proof of Traffic: A Deterministic and Privacy-Preserving Cross Verification Mechanism for Cooperative Perception Data},
author = {Ye Tao and Ehsan Javanmardi and Pengfei Lin and Yuze Jiang and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
url = {https://arxiv.org/abs/2312.07948},
doi = {10.1109/ACCESS.2023.3343405},
issn = {2169-3536},
year = {2023},
date = {2023-12-17},
urldate = {2023-12-17},
journal = {IEEE Access},
volume = {11},
pages = {142846-142861},
abstract = {Cooperative perception is crucial for connected automated vehicles in intelligent transportation systems (ITSs); however, ensuring the authenticity of perception data remains a challenge as the vehicles cannot verify events that they do not witness independently. Various studies have been conducted on establishing the authenticity of data, such as trust-based statistical methods and plausibility-based methods. However, these methods are limited as they require prior knowledge such as previous sender behaviors or predefined rules to evaluate the authenticity. To overcome this limitation, this study proposes a novel approach called zero-knowledge Proof of Traffic (zk-PoT), which involves generating cryptographic proofs to the traffic observations. Multiple independent proofs regarding the same vehicle can be deterministically cross-verified by any receivers without relying on ground truth, probabilistic, or plausibility evaluations. Additionally, no private information is compromised during the entire procedure. A full on-board unit software stack that reflects the behavior of zk-PoT is implemented within a specifically designed simulator called Flowsim. A comprehensive experimental analysis is then conducted using synthesized city-scale simulations, which demonstrates that zk-PoT’s cross-verification ratio ranges between 80 % to 96 %, and 90 % of the verification is achieved in 5 s, with a protocol overhead of approximately 25 %. Furthermore, the analyses of various attacks indicate that most of the attacks could be prevented, and some, such as collusion attacks, can be mitigated. The proposed approach can be incorporated into existing works, including the European Telecommunications Standards Institute (ETSI) and the International Organization for Standardization (ISO) ITS standards, without disrupting the backward compatibility.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Nakazato2023,
title = {WebRTC over 5G: A Study of Remote Collaboration QoS in Mobile Environment},
author = {Jin Nakazato and Kousuke Nakagawa and Koki Itoh and Romain Fontugne and Manabu Tsukada and Hiroshi Esaki},
url = {https://link.springer.com/content/pdf/10.1007/s10922-023-09778-5.pdf},
doi = {10.1007/s10922-023-09778-5},
issn = {1573-7705},
year = {2023},
date = {2023-10-24},
urldate = {2023-10-24},
journal = {Journal of Network and Systems Management},
volume = {32},
issue = {1},
abstract = {The increasing demand for remote collaboration and remote working has become crucial to daily life owing to the Covid-19 pandemic and the development of internet-based video distribution services. Furthermore, low-latency remote collaboration, such as teleoperation and support applications designed for in-vehicle environments, has gained considerable attention. The 5G technology is considered as a key infrastructure for remote collaboration. This study aimed to evaluate the actual 5G capability to achieve high quality of service (QoS) for remote collaboration. We designed and implemented a measurement tool to monitor the QoS of remote collaboration under real-world 5G conditions. We performed measurements encompassing the various 5G frequency bands. During these experiments, we employed various tools to obtain detailed mobile signal conditions to analyze the relationship between various environmental factors (e.g. signal quality, band, handoff, geographic conditions, and mobility) and the QoS performance of remote collaboration in a real-world 5G environment. This study elucidated the correlation between the WebRTC performance and various environmental factors as well as the performance improvement potential by leveraging the communication technologies of multiple mobile carriers. The collected data has been made publicly available to foster research on QoS and 5G.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Chauhan2023c,
title = {Fostering Fuzzy Logic in Enhancing Pedestrian Safety: Harnessing Smart Pole Interaction Unit for Autonomous Vehicle-to-Pedestrian Communication and Decision Optimization},
author = {Vishal Chauhan and Chia-Ming Chang and Ehsan Javanmardi and Jin Nakazato and Pengfei Lin and Takeo Igarashi and Manabu Tsukada},
url = {https://www.mdpi.com/2079-9292/12/20/4207},
doi = {10.3390/electronics12204207},
issn = {2079-9292},
year = {2023},
date = {2023-10-11},
urldate = {2023-10-11},
journal = {Electronics},
volume = {12},
number = {20},
abstract = {In autonomous vehicles (AVs), ensuring pedestrian safety within intricate and dynamic settings, particularly at crosswalks, has gained substantial attention. While AVs perform admirably in standard road conditions, their integration into unique environments like shared spaces devoid of traditional traffic infrastructure control presents complex challenges. These challenges involve issues of right-of-way negotiation and accessibility, particularly in “naked streets”. This research delves into an innovative smart pole interaction unit (SPIU) with an external human–machine interface (eHMI). Utilizing virtual reality (VR) technology to evaluate the SPIU efficacy, this study investigates its capacity to enhance interactions between vehicles and pedestrians at crosswalks. The SPIU is designed to communicate the vehicles’ real-time intentions well before arriving at the crosswalk. The study findings demonstrate that the SPIU significantly improves secure decision making for pedestrian passing and stops in shared spaces. Integrating an SPIU with an eHMI in vehicles leads to a substantial 21% reduction in response time, greatly enhancing the efficiency of pedestrian stops. Notable enhancements are observed in unidirectional (one-way) and bidirectional (two-way) scenarios, highlighting the positive impact of the SPIU on interaction dynamics. This work contributes to AV–pedestrian interaction and underscores the potential of fuzzy-logic-driven solutions in addressing complex and ambiguous pedestrian behaviors.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Asad2023b,
title = {Limitations and Future Aspects of Communication Costs in Federated Learning: A Survey},
author = {Muhammad Asad and Saima Shaukat and Dou Hu and Zekun Wang and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada},
doi = {10.3390/s23177358},
issn = {1424-8220},
year = {2023},
date = {2023-08-23},
urldate = {2023-08-23},
journal = {Sensors},
volume = {23},
number = {17},
abstract = {This paper explores the potential for communication-efficient federated learning (FL) in modern distributed systems. FL is an emerging distributed machine learning technique that allows for distributed training of a single machine learning model across multiple geographically distributed clients. This paper surveys the various approaches to communication-efficient FL, including model updates, compression techniques, resource management for edge and cloud, and client selection. We also review the various optimization techniques associated with communication-efficient FL, such as compression schemes and structured updates. Finally, we highlight the current research challenges and discuss the potential future directions for communication-efficient FL.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Sone2023,
title = {An Ontology for Spatio-Temporal Media Management and an Interactive Application},
author = {Takuro Sone and Shin Kato and Ray Atarashi and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
url = {https://github.com/sdm-wg/web360square-vue
https://tlab.hongo.wide.ad.jp/sdmo/},
doi = {10.3390/fi15070225},
issn = {1999-5903},
year = {2023},
date = {2023-06-23},
urldate = {2023-06-23},
journal = {Future Internet},
volume = {15},
number = {225},
issue = {7},
abstract = {In addition to traditional viewing media, metadata that record the physical space from multiple perspectives will become extremely important in realizing interactive applications such as Virtual Reality(VR), Augmented Reality(AR). This paper proposes the Software Defined Media (SDM) Ontology designed to describe spatio-temporal media and the systems that handle them comprehensively. Spatio-temporal media refers to video, audio, and various sensor values recorded together with time and location information. The SDM Ontology can flexibly and precisely represent spatio-temporal media, equipment, and functions that record, process, edit, and play them and related semantic information. In addition, we recorded classical and jazz concerts using many video cameras and audio microphones, and then processed and edited the video and audio data with related metadata. Then, we created a dataset using the SDM Ontology and published it as linked open data(LOD). Furthermore, we developed "Web360^2" an application that enables users to interactively view and experience 360-degree video and spatial acoustic sounds by referring to this dataset. We conducted a subjective evaluation by using a user questionnaire. Web360^2 is a data-driven web application that obtains video and audio data and related metadata by querying the Dataset.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Asad2023,
title = {A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems},
author = {Muhammad Asad and Saima Shaukat and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada},
doi = {10.3390/app13106201},
issn = {2076-3417},
year = {2023},
date = {2023-05-18},
urldate = {2023-05-18},
journal = {Applied Sciences },
abstract = {Big data is a rapidly growing field, and new developments are constantly emerging to address various challenges. One such development is the use of federated learning for recommendation systems (FRSs). An FRS provides a way to protect user privacy by training recommendation models using intermediate parameters instead of real user data. This approach allows for cooperation between data platforms while still complying with privacy regulations. In this paper, we explored the current state of research on FRSs, highlighting existing research issues and possible solutions. Specifically, we looked at how FRSs can be used to protect user privacy while still allowing organizations to benefit from the data they share. Additionally, we examined potential applications of FRSs in the context of big data, exploring how these systems can be used to facilitate secure data sharing and collaboration. Finally, we discuss the challenges associated with developing and deploying FRSs in the real world and how these challenges can be addressed.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Lin2022e,
title = {Safety Tunnel-Based Model Predictive Path-Planning Controller with Potential Functions for Emergency Navigation},
author = {Pengfei Lin and Ying Shuai Quan and Jin Ho Yang and Chung Choo Chung and Manabu Tsukada},
doi = {10.1109/TITS.2022.3229699},
issn = {1524-9050},
year = {2023},
date = {2023-04-01},
urldate = {2023-04-01},
journal = {IEEE Transactions on Intelligent Transportation Systems},
volume = {24},
issue = {4},
pages = {3974 - 3985},
abstract = {The potential functions (PFs) have generally shown good performances in real-time path planning with computation efficiency conforming to the requirements of lower control systems in autonomous driving. However, several inherent limitations exist in using the PFs, including a local minimum in specific scenarios and no passage between closely spaced obstacles. Recent studies have focused on conventional scenarios where PFs are assumed to work normally, without malfunctioning, occurring during perilous situations. Therefore, we propose a specific safety tunnel (ST)-based model predictive controller (MPC) combined with PFs (PF-STMPC) to handle path-planning in extreme-emergency traffic scenarios (e.g., emergency braking and lane-changing obstacles). To further guarantee driving safety, we improve PFs with the responsibility-sensitive safety (RSS) model that accurately calculates the minimum safe longitudinal and lateral distances. Furthermore, a sigmoid-based ST is designed for emergency navigation if the PFs fail to plan a safe path due to the aforementioned inherent limitations, enabling the controller with planning functionality if necessary. The ST is embedded in the MPC-based tracking controller as a safe constraint sensitive to surrounding environments (e.g., road structure and obstacles). The proposed PF-STMPC was co-simulated using MATLAB/Simulink and CarSim Simulator under the constant speed condition. Compared with the state-of-the-art method, the proposed method demonstrated better performance in finding a safe path and eliminating severe yawing of the ego-vehicle (82.8% less in sideslip yawing amplitude and 57.7% shorter in the oscillation period of yaw angle) when facing traffic emergencies.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Asad2022,
title = {SHFL: K-Anonymity-based Secure Hierarchical Federated Learning Framework for Smart Healthcare Systems},
author = {Muhammad Asad and Muhammad Aslam and Syeda Fizzah Jilani and Saima Shoukat and Manabu Tsukada},
url = {https://www.mdpi.com/1999-5903/14/11/338},
doi = {10.3390/fi14110338},
isbn = {1999-5903},
year = {2022},
date = {2022-11-18},
urldate = {2022-11-18},
journal = {Future Internet},
volume = {14},
number = {11},
abstract = {Dynamic and smart infrastructures of the Internet of Things (IoT) allow the development of smart healthcare systems. These smart healthcare systems are equipped with mobile health and embedded healthcare sensors to provide a broad range of healthcare applications. These IoT applications provide the key availability of clients’ health information. However, the boost in the number of mobile devices and social networks intends to share the locations without the clients’ concern. In this regard, Federated Learning (FL) is an emerging paradigm of decentralized machine learning that guarantees to train a shared global model without compromising client data privacy. To this end, in this paper, we propose a K-Anonymity-based Secure Hierarchical Federated Learning (SHFL) framework for smart healthcare systems. In the proposed hierarchical FL approach, a centralized server communicates with multiple directly and indirectly connected devices hierarchically. In particular, the proposed SHFL formulates the location-based services (LBS)-hierarchical clusters to execute distributed FL. Besides, the proposed SHFL utilizes the K-Anonymity method to hide the location of the clusters’ devices. In the end, we evaluate the performance of the proposed SHFL by configuring the different hierarchical networks with multiple model architectures and datasets. The experiments validate that the proposed SHFL provides a suitable generalization to enable network scalability of accurate healthcare systems without compromising data and location privacy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Masuda2022,
title = {Feature-based Vehicle Identification Framework for Optimization of Collective Perception Messages in Vehicular Networks},
author = {Hidetaka Masuda and Oussama El Marai and Manabu Tsukada and Tarik Taleb and Hiroshi Esaki},
doi = {10.1109/TVT.2022.3211852},
isbn = {0018-9545},
year = {2022},
date = {2022-10-04},
urldate = {2022-10-04},
journal = {IEEE Transactions on Vehicular Technology},
volume = {72},
issue = {2},
pages = {2120-2129},
abstract = {The world is moving towards a fully connected digital world, where objects produce and consume data, at a sultry pace. Autonomous vehicles will play a key role in bolstering the digitization of the world. These connected vehicles must communicate timely data with their surrounding objects and road participants to fully and accurately understand their environments and eventually operate smoothly. As a result, the hugely exchanged data would scramble the network traffic that, at a given point, would no longer increase the awareness level of the vehicle. In this paper, we propose a vision-based approach to identify connected vehicles and use it to optimize the exchange of collective perception messages (CPMs), in terms of both the CPM generation frequency and the number of generated CPMs. To validate our proposed approach, we created a Cartery framework that integrates SUMO, Carla, and OMNeT++. We also compared our solution with both baselines and European Telecommunications Standards Institute solutions, considering three main KPIs: the channel busy ratio, environmental awareness, and the CPM generation frequency. Simulation results show that our proposed solution exhibits the best trade-off between the network load and situational awareness.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Lin2022,
title = {Model Predictive Path-Planning Controller with Potential Function for Emergency Collision Avoidance on Highway Driving},
author = {Pengfei Lin and Manabu Tsukada},
doi = {10.1109/LRA.2022.3152693},
isbn = {2377-3766},
year = {2022},
date = {2022-04-22},
urldate = {2022-04-22},
journal = {Robotics and Automation Letters (RA-L) with IEEE International Conference on Robotics and Automation (ICRA) option},
volume = {7},
issue = {2},
pages = {4662-4669},
abstract = {Existing potential functions (PFs) utilized in autonomous vehicles mainly focus on solving the path-planning problems in some conventional driving scenarios; thus, their performance may not be satisfactory in the context of emergency obstacle avoidance. Therefore, we propose a novel model predictive path-planning controller (MPPC) combined with PFs to handle complex traffic scenarios (e.g., emergency avoidance when a sudden accident occurs). Specifically, to enhance the safety of the PFs, we developed an MPPC to handle an emergency case with a sigmoid-based safe passage embedded in the MPC constraints (SPMPC) with a specific triggering analysis algorithm on monitoring traffic emergencies. The presented PF-SPMPC algorithm was compiled in a comparative simulation study using MATLAB/Simulink and CarSim. The algorithm outperformed the latest PF-MPC approach to eliminate the severe tire oscillations and guarantee autonomous driving safety when handling the traffic emergency avoidance scenario.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Lin2022d,
title = {Building a speech recognition system with privacy identification information based on Google Voice for social robots},
author = {Pei‑Chun Lin and Benjamin Yankson and Vishal Chauhan and Manabu Tsukada},
doi = {10.1007/s11227-022-04487-3},
isbn = {1573-0484},
year = {2022},
date = {2022-04-12},
journal = {The Journal of Supercomputing},
abstract = {Currently, many smart speakers, even social robots, appear on the market to help people's lives become more convenient. Usually, people use smart speakers to check their daily schedule or control home appliances in their house. Many social robots also include smart speakers. They have the common property of being used in voice control machines. Regardless of where the smart speaker is installed and used, when people start a conversation with voice equipment, a security or privacy risk is exposed. Hence, we want to build a speech recognition (SR) that contains the privacy identification information (PII) system in this paper. We call this the SR-PII system. We used a Google Artificial-Intelligence-Yourself (AIY) Voice Kit released from Google to build a simple, smart dialog speaker and included our SR-PII system. In our experiments, we test SR accuracy and the reliability of privacy settings in three environments (quiet, noise, and playing music). We also examine the cloud response and speaker response times during our experiments. The results show that the speaker response is approximately 3.74 s in the cloud environment and approximately 9.04 s from the speaker. We also showed the response accuracy of the speaker, which successfully prevented personal information with the SR-PII system in three environments. The speaker has a response mean time of approximately 8.86 s with 93{%} mean accuracy in a quiet room, approximately 9.18 s with 89{%} mean accuracy in a noisy environment, and approximately 9.62 s with 90{%} mean accuracy in an environment that plays music. We conclude that the SR-PII system can secure private information and that the most important factor affecting the response speed of the speaker is the network connection status. We hope that people can, through our experiments, have some guidelines in building social robots and installing the SR-PII system to protect users’ personal identification information.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Tsukada2020b,
title = {Networked Roadside Perception Units for Autonomous Driving},
author = {Manabu Tsukada and Takaharu Oi and Masahiro Kitazawa and Hiroshi Esaki
},
url = {https://www.mdpi.com/1424-8220/20/18/5320/pdf?.pdf
https://youtu.be/n7gD0L7NDEM},
doi = {10.3390/s20185320},
issn = {1424-8220},
year = {2020},
date = {2020-09-17},
urldate = {2020-09-17},
journal = {MDPI Sensors},
volume = {20},
number = {18},
abstract = {Vehicle-to-Everything (V2X) communication enhances the capability of autonomous driving through better safety, efficiency, and comfort. In particular, sensor data sharing, known as cooperative perception, is a crucial technique to accommodate vulnerable road users in a cooperative intelligent transport system (ITS). In this paper, we describe a roadside perception unit (RSPU) that combines sensors and roadside units (RSUs) for infrastructure-based cooperative perception. We propose a software called AutoC2X that we designed to realize cooperative perception for RSPUs and vehicles. We also propose the concept of networked RSPUs, which is the inter-connection of RSPUs along a road over a wired network, and helps realize broader cooperative perception. We evaluated the RSPU system and the networked RSPUs through a field test, numerical analysis, and simulation experiments. Field evaluation showed that, even in the worst case, our RSPU system can deliver messages to an autonomous vehicle within 100 ms. The simulation result shows that the proposed priority algorithm achieves a wide perception range with a high delivery ratio and low latency, especially under heavy road traffic conditions. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Azuma2018,
title = {A Method of Misbehavior Detection with Mutual Vehicle Position Monitoring},
author = {Shuntaro Azuma and Manabu Tsukada and Kenya Sato},
url = {https://hal.archives-ouvertes.fr/hal-01879098/document?.pdf},
year = {2018},
date = {2018-06-10},
journal = {IntTech18v11n12, International Journal On Advances in Internet Technology},
volume = {11},
number = {1&2},
pages = {82-91},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Tao2017,
title = {Reliable Overlay Networking on ETSI GeoNetworking Standards},
author = {Ye Tao and Xin Li and Manabu Tsukada and Hiroshi Esaki},
url = {http://rdcu.be/qyX5},
doi = {https://doi.org/10.1007/s13177-017-0141-7},
isbn = {1348-8503},
year = {2017},
date = {2017-04-20},
journal = {International Journal of Intelligent Transportation Systems Research},
volume = {16},
number = {2},
pages = {98-111},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Tsukada2014,
title = {On the Experimental Evaluation of Vehicular Networks: Issues, Requirements and Methodology Applied to a Real Use Case},
author = {Manabu Tsukada and Jos{'e} Santa and Satoshi Matsuura and Thierry Ernst and Kazutoshi Fujikawa},
url = {https://hal.inria.fr/hal-01095282/document?.pdf
https://youtu.be/NamJUd-_0jw},
doi = {http://dx.doi.org/10.4108/inis.1.1.e4},
isbn = {2410-0218},
year = {2014},
date = {2014-12-01},
journal = {EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
volume = {1},
number = {1},
pages = {1-14},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Li2014,
title = {MultiVehicle Cooperative Local Mapping: A Methodology Based on Occupancy Grid Map Merging},
author = {Hao Li and Manabu Tsukada and Fawzi Nashashibi and Michel Parent},
url = {https://hal.inria.fr/hal-01107534/file/LI_Nashashibi_Draft_Juillet2013.pdf},
doi = {10.1109/TITS.2014.2309639},
isbn = {1524-9050},
year = {2014},
date = {2014-10-10},
journal = {IEEE Transactions on Intelligent Transportation Systems},
volume = {15},
number = {5},
pages = {2089-2100},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Lee2012,
title = {MNPP: Mobile Network Prefix Provisioning for Enabling Route Optimization in Geographic Vehicular Networks},
author = {Jong-Hyouk Lee and Manabu Tsukada and Thierry Ernst},
url = {http://www.oldcitypublishing.com/journals/ahswn-home/ahswn-issue-contents/ahswn-volume-15-number-1-2-2012/},
issn = {1551-9899},
year = {2012},
date = {2012-06-13},
journal = {AHSWN - Ad Hoc & Sensor Wireless Networks},
volume = {15},
number = {1},
pages = {5-19},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Noguchi2012,
title = {Design and Field evaluation of geographical location-aware service discovery on IPv6 GeoNetworking for VANET},
author = {Satoru Noguchi and Manabu Tsukada and Thierry Ernst and Astuo Inomata and Kazutoshi Fujikawa
},
url = {https://hal.inria.fr/hal-00784409/document?.pdf},
doi = {10.1186/1687-1499-2012-29},
isbn = {1687-1499},
year = {2012},
date = {2012-02-20},
journal = {special issue (SI) of "Network Routing and Communication Algorithm for Intelligent Transportation Systems" in EURASIP Journal on Wireless Communications and Networking},
pages = {1-16},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Tsukada2010,
title = {Design and Experimental Evaluation of a Vehicular Network Based on NEMO and MANET},
author = {Manabu Tsukada and José Santa and Olivier Mehani and Yacine Khaled and Thierry Ernst},
url = {https://hal.inria.fr/hal-00784433/document?.pdf
https://youtu.be/LtFrE8Ezho0},
doi = {10.1155/2010/656407},
isbn = {1687-6180},
year = {2010},
date = {2010-09-10},
journal = {The special issue for Vehicular Ad Hoc Networks, EURASIP Journal on Advances in Signal Processing},
volume = {2010},
pages = {1-16},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Santa2009,
title = {Assessment of VANET multi-hop routing over an experimental platform},
author = {José Santa and Manabu Tsukada and Thierry Ernst and Olivier Mehani and Antonio F. Gómez-Skarmeta},
url = {https://hal.inria.fr/inria-00625837/document?.pdf},
doi = {10.1504/IJIPT.2009.028655},
issn = {1743-8209},
year = {2009},
date = {2009-09-30},
journal = {International Journal of Internet Protocol Technology},
volume = {4},
number = {3},
pages = {158-172},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Khaled2009,
title = {A usage oriented analysis of vehicular networks: from technologies to applications},
author = {Yacine Khaled and Manabu Tsukada and José Santa and JinHyeock Choi and Thierry Ernst},
url = {https://pdfs.semanticscholar.org/dea9/8e19ab72a60e2f5783066c5f7912efca3b64.pdf},
doi = {10.4304/jcm.4.5.357-368},
issn = {1796-2021},
year = {2009},
date = {2009-06-10},
journal = {Journal of Communications (JCM), Academy Publisher, Special Issue on Challenges in Future Vehicular AD HOC Networks, vol. 4, no. 5, pp. 357-368, May 2009},
volume = {4},
number = {5},
pages = {357-368},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inbook{Sato2016,
title = {Probe Vehicle Information Systems},
author = {Masaaki Sato and Manabu Tsukada and Hiroshi Ito},
url = {https://www.crcpress.com/Intelligent-Transportation-Systems-From-Good-Practices-to-Standards/Pagano/p/book/9781498721868},
doi = {10.1201/9781315370866-9},
isbn = {9781498721868},
year = {2016},
date = {2016-08-20},
pages = {151-170},
publisher = {Intelligent Transportation Systems: From Good Practices to Standards, CRC Press Book},
chapter = {8},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@inbook{Khaled2010,
title = {The Role of Communication Technologies in Vehicular Applications},
author = {Yacine Khaled and Manabu Tsukada and José Santa and Thierry Ernst},
url = {https://www.igi-global.com/book/advances-vehicular-hoc-networks/37323},
doi = {10.4018/978-1-61520-913-2},
isbn = {9781615209132},
year = {2010},
date = {2010-04-20},
pages = {37-57},
publisher = {Advances in Vehicular Ad-Hoc Networks: Developments and Challenges, IGI Global},
chapter = {3},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@inproceedings{Li2025b,
title = {State-Guided Spatial Cross-Attention for Enhanced End-to-End Autonomous Driving},
author = {Dongyang Li and Ehsan Javanmardi and Manabu Tsukada},
year = {2025},
date = {2025-09-30},
urldate = {2025-09-30},
booktitle = {IEEE International Automated Vehicle Validation Conference (IAVVC 2025)},
address = {Baden-Baden, Germany},
abstract = {Handling near-accident scenarios is a significant challenge for end-to-end autonomous driving (E2E-AD), as these situations often involve sudden environmental changes, complex interactions with other road users, and high-risk decision-making under uncertainty. Unlike routine driving tasks, near-accident scenarios require rapid and precise responses based on external perception and internal vehicle dynamics. Successfully navigating such situations demands not only a comprehensive understanding of the surrounding environment but also an accurate assessment of the ego vehicle's state, including speed, acceleration, and steering angle, to ensure safe and reliable control. However, conventional E2E-AD models struggle to handle these safety-critical situations effectively. Standard approaches primarily rely on raw sensor inputs to learn driving policies, often overlooking the crucial role of vehicle state information in decision-making. Since many near-accident scenarios involve conditions where the same environmental observation could require vastly different responses depending on the ego vehicle's motion state-such as whether the vehicle is braking, accelerating, or experiencing traction loss-ignoring these internal dynamics can lead to unsafe or suboptimal actions. Furthermore, E2E-AD models typically learn a direct mapping from sensory inputs to control outputs, making it difficult to generalize to highly dynamic and unpredictable interactions, such as emergency evasive maneuvers or sudden braking events. To address these challenges, we propose a state-guided cross-attention mechanism that explicitly models the interaction between the ego vehicle's states and its perception of the environment. By incorporating vehicle state information into the decision-making process, our approach ensures that the model can dynamically adjust its attention to critical sensory inputs based on real-time driving conditions. This allows the autonomous system to make more context-aware decisions, improving its ability to respond effectively to complex and safety-critical scenarios.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Li2025c,
title = {PrefDrive: Enhancing Autonomous Driving through Preference-Guided Large Language Models},
author = {Yun Li and Ehsan Javanmardi and Simon Thompson and Kai Katsumata and Alex Orsholits and Manabu Tsukada},
url = {https://github.com/LiYun0607/PrefDrive/
https://huggingface.co/liyun0607/PrefDrive
https://huggingface.co/datasets/liyun0607/PrefDrive},
year = {2025},
date = {2025-06-22},
urldate = {2025-06-22},
booktitle = {36th IEEE Intelligent Vehicles Symposium (IV2025)},
address = {Cluj-Napoca, Romania},
abstract = {This paper presents PrefDrive, a novel framework that integrates driving preferences into autonomous driving models through large language models (LLMs). While recent advances in LLMs have shown promise in autonomous driving, existing approaches often struggle to align with specific driving behaviors (e.g., maintaining safe distances, smooth acceleration patterns) and operational requirements (e.g., traffic rule compliance, route adherence). We address this challenge by developing a preference learning framework that combines multimodal perception with natural language understanding. Our approach leverages Direct Preference Optimization (DPO) to fine-tune LLMs efficiently on consumer-grade hardware, making advanced autonomous driving research more accessible to the broader research community. We introduce a comprehensive dataset of 74,040 sequences, carefully annotated with driving preferences and driving decisions, which, along with our trained model checkpoints, will be made publicly available to facilitate future research. Through extensive experiments in the CARLA simulator, we demonstrate that our preference-guided approach significantly improves driving performance across multiple metrics, including distance maintenance and trajectory smoothness. Results show up to 28.1% reduction in traffic rule violations and 8.5% improvement in navigation task completion while maintaining appropriate distances from obstacles. The framework demonstrates robust performance across different urban environments, showcasing the effectiveness of preference learning in autonomous driving applications. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Jiang2025,
title = {Towards Efficient Roadside LiDAR Deployment: A Fast Surrogate Metric Based on Entropy-Guided Visibility},
author = {Yuze Jiang and Ehsan Javanmardi and Manabu Tsukada and Hiroshi Esaki},
url = {https://arxiv.org/abs/2504.06772},
year = {2025},
date = {2025-06-22},
urldate = {2025-06-22},
booktitle = {36th IEEE Intelligent Vehicles Symposium (IV2025)},
address = {Cluj-Napoca, Romania},
abstract = {The deployment of roadside LiDAR sensors plays a crucial
role in the development of Cooperative Intelligent
Transport Systems (C-ITS). However, the high cost of LiDAR
sensors necessitates efficient placement strategies to
maximize detection performance. Traditional roadside LiDAR
deployment methods rely on expert insight, making them
time-consuming. Automating this process, however, demands
extensive computation, as it requires not only visibility
evaluation but also assessing detection performance across
different LiDAR placements. To address this challenge, we
propose a fast surrogate metric, the Entropy-Guided
Visibility Score (EGVS), based on information gain to
evaluate object detection performance in roadside LiDAR
configurations. EGVS leverages Traffic Probabilistic
Occupancy Grids (TPOG) to prioritize critical areas and
employs entropy-based calculations to quantify the
information captured by LiDAR beams. This eliminates the
need for direct detection performance evaluation, which
typically requires extensive labeling and computational
resources. By integrating EGVS into the optimization
process, we significantly accelerate the search for optimal
LiDAR configurations. Experimental results using the AWSIM
simulator demonstrate that EGVS strongly correlates with
Average Precision (AP) scores and effectively predicts
object detection performance. This approach offers a
computationally efficient solution for roadside LiDAR
deployment, facilitating scalable smart infrastructure
development. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Rao2025,
title = {A LEO satellite routing method based on incremental evolutionary graph reinforcement learning},
author = {Zheheng Rao and Zhenyu Zhu and Wei Yang Bryan Lim and Ye Yao and Yanyan Xu and Manabu Tsukada and Yanyu Cheng},
year = {2025},
date = {2025-06-08},
booktitle = {IEEE International Conference on Communications (ICC 2025)},
address = {Montreal, Canada},
abstract = {Only the chairs can edit With the advent of sixth-generation (6G) technologies and growing communication demands, Low Earth Orbit (LEO) satellite networks have become essential in modern communications. However, due to the dynamic topology and complex network state of LEO environments, existing routing methods often fail to make effective decisions, limiting transmission performance. This paper proposes a novel routing method, DGA-IES. To address network state perception challenges, we introduce a topological learning model using deep graph attention (DGA), which captures complex inter-satellite connectivity and resource states. Additionally, by integrating incremental evolution strategies (IES) into deep reinforcement learning (DRL), we replace sequential interactive proximal policy optimization (PPO) with global parallel ES, achieving efficient routing convergence in the highly dynamic LEO environment. Experimental results demonstrate that our DGA-IES approach enhances LEO network load balancing by reducing end-to-end (E2E) network latency, decreasing packet loss and improving throughput compared with the benchmark approaches.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Orsholits2025,
title = {Context-Rich Interactions in Mixed Reality through Edge AI Co-Processing},
author = {Alex Orsholits and Manabu Tsukada},
url = {https://link.springer.com/chapter/10.1007/978-3-031-87772-8_3},
doi = {10.1007/978-3-031-87772-8_3},
isbn = {978-3-031-87771-1},
year = {2025},
date = {2025-04-09},
urldate = {2025-04-09},
booktitle = {The 39-th International Conference on Advanced Information Networking and Applications (AINA 2025)},
address = {Barcelona, Spain},
abstract = {Spatial computing is evolving towards leveraging data streaming for computationally demanding applications, facilitating a shift to lightweight, untethered, and standalone devices. These devices are therefore ideal candidates for co-processing, where real-time context understanding and low-latency data streaming are fundamental for seamless, general-purpose Mixed Reality (MR) experiences. This paper demonstrates and evaluates a scalable approach to augmented contextual understanding in MR by implementing multi-modal edge AI co-processing through a Hailo-8 AI accelerator, a low-power ARM-based single board computer (SBC), and the Magic Leap 2 AR headset. The proposed system utilises the native WebRTC streaming capabilities of the Magic Leap 2 to continuously stream camera data to the edge co-processor, where a collection of vision AI models-object detection, pose estimation, face recognition, and depth estimation-are executed. The resulting inferences are then streamed back to the headset for spatial re-projection and transmitted to cloud-based systems for further integration with large-scale AI models, such as LLMs and VLMs. This seamless integration enhances real-time contextual understanding in MR while facilitating advanced multi-modal, multi-device collaboration, supporting richer, scalable spatial cognition across distributed systems.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Zhu2025,
title = {A Distributed Content Subscription Mechanism with Revision Discovery to Decouple Content Sharing Platform and Creator ID},
author = {Zhihai Zhu and Ye Tao and Manabu Tsukada and Hiroshi Esaki},
year = {2025},
date = {2025-02-18},
urldate = {2025-02-18},
booktitle = {International Conference on Artificial Intelligence in Information and Communication (ICAIIC 2025) },
address = {Fukuoka, Japan},
abstract = {Only the chairs can edit This paper proposes a distributed content subscription mechanism that enables content creators to share updates with their audience while maintaining platform independence and anonymity. The mechanism extends the Kademlia distributed hash table (DHT) protocol by incorporating revision numbers and republication timestamps into the DHT key computation, allowing subscribers to discover content updates through heuristic revision queries. It leverages public key cryptography for creator identification and content authenticity, while integrating with established peer-to-peer protocols like BitTorrent for efficient content distribution. Preliminary testing with 200 simulated nodes demonstrates the mechanism's ability to maintain content availability and update discovery even when content creators are offline. This approach particularly benefits creators operating under strict content controls or surveillance, offering them greater creative freedom and distribution autonomy compared to existing centralized and decentralized solutions.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Yamane2025,
title = {Low Latency Redundant Network Architecture for Enhanced 5G Mobile Communication Quality},
author = {Nayuta Yamane and Jin Nakazato and Koki Ito and Manabu Mikami and Takahiro Tsuchiya and Manabu Tsukada and Hiroshi Esaki},
year = {2025},
date = {2025-01-14},
booktitle = {39th International Conference on Information Networking (ICOIN) 2025},
address = {Chiang Mai, Thailand},
abstract = {With growing demand for high-capacity, low-latency communication driven by the increasing use of video streaming and real-time data applications, efficient data transfer methods have become essential. While 5G technology provides enhanced data transfer capabilities, its directional nature can lead to stability issues, including connection interruptions, frequent handoffs, data loss, and increased latency. To address these challenges, we propose a system architecture with a redundant configuration that separates LTE and 5G-SA within a single MNO, alongside a method to reduce overhead. Initial verification in a stationary environment demonstrated the reduction of latency by 38.1 ms compared to the conventional method, underscoring the potential of our approach for stable and efficient data transmission.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Lin2024c,
title = {A Rule-Compliance Path Planner for Lane-Merge Scenarios Based on Responsibility-Sensitive Safety},
author = {Pengfei Lin and Ehsan Javanmardi and Yuze Jiang and Manabu Tsukada},
doi = {10.1109/ICARCV63323.2024.10821557},
year = {2024},
date = {2024-12-12},
urldate = {2024-12-12},
booktitle = {2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV)},
address = {Dubai, UAE},
abstract = {Lane merging is one of the critical tasks for selfdriving cars, and how to perform lane-merge maneuvers effectively and safely has become one of the important standards
in measuring the capability of autonomous driving systems.
However, due to the ambiguity in driving intentions and
right-of-way issues, the lane merging process in autonomous
driving remains deficient in terms of maintaining or ceding
the right-of-way and attributing liability, which could result
in protracted durations for merging and problems such as
trajectory oscillation. Hence, we present a rule-compliance
path planner (RCPP) for lane-merge scenarios, which initially
employs the extended responsibility-sensitive safety (RSS) to
elucidate the right-of-way, followed by the potential field-based
sigmoid planner for path generation. In the simulation, we have
validated the efficacy of the proposed algorithm. The algorithm
demonstrated superior performance over previous approaches
in aspects such as merging time (Saved 72.3%), path length
(reduced 53.4%), and eliminating the trajectory oscillation.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Sugizaki2024,
title = {Digital Twin Based Open Platform for IoT Offloading Control: Enabling System Transparency and User Participation},
author = {Yusuke Sugizaki and Jin Nakazato and Manabu Tsukada},
year = {2024},
date = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Chauhan2024b,
title = {Connected Shared Spaces: Expert Insights into the Impact of eHMI and SPIU for Next-Generation Pedestrian-AV Communication},
author = {Vishal Chauhan and Anubhav Anubhav and Chia-Ming Chang and Jin Nakazato and Ehsan Javanmardi and Alex Orsholits and Takeo Igarashi and Kantaro Fujiwara and Manabu Tsukada},
year = {2024},
date = {2024-11-28},
urldate = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Nakazato2024,
title = {Toward 6G Mobility Networks: A Proposal for Cell-Free Cooperative Distributed Beamforming},
author = {Jin Nakazato and Sojin Ozawa and Yuki Sasaki and Kengo Suzuki and Kazuki Maruta andTetsuya Iye and Yuki Susukida and Eisaku Sato and Manabu Tsukada},
year = {2024},
date = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Kambara2024,
title = {Geographic-Aware Network Analysis and Visualization System for CAVs},
author = {Koichi Kambara and Ehsan Javanmardi and Jin Nakazato and Shunya Yamada and Hiroaki Takada and Yousuke Watanabe and Kenya Sato and Manabu Tsukada},
year = {2024},
date = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Dolatabadi2024,
title = {Neural Error Covariance Estimation for Precise LIDAR Localization},
author = {Minoo Dolatabadi and Fardin Ayar and Ehsan Javanmardi and Manabu Tsukada and Mahdi Javanmardi},
url = {https://arxiv.org/abs/2501.02558},
year = {2024},
date = {2024-11-28},
urldate = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Zakerian2024,
title = {Unsupervised Person re-identification Using Generative Adversarial Networks},
author = {Romina Zakerian and Ehsan Javanmardi and Manabu Tsukada and Mahdi Javanmardi and Mohammad Rahmati},
year = {2024},
date = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Ayar2024,
title = {LiDAR-Camera Fusion for Video Panoptic Segmentation without Video Training},
author = {Fardin Ayar and Ehsan Javanmardi and Manabu Tsukada and Mahdi Javanmardi and Mohammad Rahmati},
url = {https://arxiv.org/abs/2412.20881},
year = {2024},
date = {2024-11-28},
urldate = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
abstract = {Panoptic segmentation, which combines instance and semantic segmentation, has gained a lot of attention in autonomous vehicles, due to its comprehensive representation of the scene. This task can
be applied for cameras and LiDAR sensors, but there has been a limited focus on combining both sensors to enhance image panoptic segmentation (PS). Although previous research has acknowledged the benefit of 3D data on camera-based scene perception, no specific study has explored the influence of 3D data on image and video panoptic segmentation (VPS). This work seeks to introduce a feature fusion module that enhances PS and VPS by fusing LiDAR and image data for autonomous vehicles. We also illustrate that, in
addition to this fusion, our proposed model, which utilizes two simple modifications, can further deliver even more high-quality VPS without being trained on video data. The results demonstrate a substantial improvement in both the image and video panoptic segmentation evaluation metrics by up to 5 points.},
note = {Best Paper Award (Bronze)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{nokey,
title = {Where Do You Go? Pedestrian Trajectory Prediction using Scene Features},
author = {Mohammad Ali Rezaei and Fardin Ayar and Ehsan Javanmardi and Manabu Tsukada and Mahdi Javanmardi},
url = {https://arxiv.org/abs/2501.13848},
year = {2024},
date = {2024-11-28},
urldate = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Li2024,
title = {Cross-Attention Enhanced Imitation Learning for End-to-end Autonomous Driving in Unprotected Turns},
author = {Dongyang Li and Ehsan Javanmardi and Naren Bao and Manabu Tsukada},
year = {2024},
date = {2024-11-28},
urldate = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
abstract = {Performing an unprotected turn in the intersection is a complex scenario for autonomous vehicles. It not only requires a comprehensive understanding of the surrounding environment but also highly relies on the ego vehicle’s current state to make safe decisions. A conventional way to learn end-to-end autonomous driving is imitation learning, which is learning from expert demonstrations. While most imitation learning methods focus on imitating the expert action, they often fail to imitate a complex policy efficiently when the ego vehicle’s states are crucial to the scenario because there might be arbitrary optimal actions under different states. To address this issue and investigate how vehicle states affect autonomous driving, we present a novel cross-attention enhanced imitation learning approach for end-to-end autonomous driving in unprotected turns, focusing on capturing the relationships between the ego vehicle’s states and its perception of the environment. We evaluate our model in AWSIM, an open-source autonomous driving
simulator, and the results demonstrate that our model outperformed conventional imitation learning-based baselines in performing unprotected turn scenarios, showcasing its ability to imitate a complex policy efficiently.},
note = {Best Paper Award (Silver)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Orsholits2024b,
title = {PLATONE: Assessing Simulation Accuracy of Environment-Dependent Audio Spatialization},
author = {Alex Orsholits and Eric Nardini and Tsukada Manabu},
year = {2024},
date = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Gui2024b,
title = {"Text + Eye" on Autonomous Taxi to Provide Geospatial Instructions to Passenger},
author = {Xinyue Gui and Ehsan Javanmardi and Stela Hanbyeol Seo and Vishal Chauhan and Chia-Ming Chang and Manabu Tsukada and Takeo Igarashi},
doi = {10.1145/3687272.3690906},
year = {2024},
date = {2024-11-24},
urldate = {2024-11-24},
booktitle = {Proceedings of the 12th International Conference on Human-Agent Interaction(HAI 2024)},
pages = {429-431},
address = {Swansea University, UK},
abstract = {While text-based external human-machine interface (eHMI) is widely accepted, one limitation is the lack of capability to communicate spatial information such as a different person or location. We built a mixed-eHMI using "eye" as a target-specifier when "text" shows the clear intention to their communication partners. We conducted a pre-experimental observation to develop two testbed scenarios, followed by a video-based user study via life-size projection with a real-car prototype mounted a text display and a set of robotic eyes. The results demonstrated that our proposed "text + eye" combination may represent geospatial information by increasing the success pick-up rate.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Grosset2024,
title = {Generation of V2X messages from Carla Simulator for cooperative perception: Application to pedestrian safety},
author = {Juliette Grosset and Jean-Marie Bonnin and Alain-Jérôme Fougères and Manabu Tsukada and Moise Djoko-Kouam},
doi = {10.1109/VTC2024-Fall63153.2024.10757467},
year = {2024},
date = {2024-10-07},
urldate = {2024-10-07},
booktitle = {The IEEE 100th Vehicular Technology Conference (VTC2024-Fall)},
address = {Washington DC, USA},
abstract = {Despite advancements in connected and autonomous vehicles (CAVs), vulnerable road users (VRUs) face a challenge as they lack Communication-Intelligent Transport System (C-ITS) equipment. This deficiency impedes their interaction with CAVs. We underscore the significance of Vehicle-to-Everything (V2X) communication in enhancing road safety with VRUs by facilitating information exchange between CAVs and the infrastructure. This communication is pivotal for reintegrating VRUs into the environmental awareness of CAVs. The Carla Simulator, used for autonomous vehicle training, currently lacks comprehensive V2X communication capabilities. In response, we propose an architecture for Carla, integrating OpenCDA and ROS2 to establish a simulated V2X network communication system for CAVs and roadside units (RSUs) within the Carla environment. This setup allows for the generation of V2X datasets and the refinement of algorithms for Advanced Driver Assistance Systems (ADAS). To illustrate and assess our proposed architecture, we present a scenario involving a pedestrian concealed in a blind spot for a connected vehicle.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Yun2024,
title = {Large Language Models for Human-like Autonomous Driving Decision Making: A Survey},
author = {Yun Li and Kai Katsumata and Ehsan Javanmardi and Manabu Tsukada},
doi = {10.1109/ITSC58415.2024.10919629},
year = {2024},
date = {2024-09-24},
urldate = {2024-09-24},
booktitle = {27th IEEE International Conference on Intelligent Transportation Systems (ITSC 2024)},
address = {Edmonton, Canada},
abstract = {Large Language Models (LLMs), AI models trained on massive text corpora with remarkable language understanding and generation capabilities, are transforming the field of Autonomous Driving (AD). As AD systems evolve from rule-based and optimization-based methods to learning-based techniques like deep reinforcement learning, they are now poised to embrace a third and more advanced category: knowledge-based AD empowered by LLMs. This shift promises to bring AD closer to human-like AD. However, integrating LLMs into AD systems poses challenges in real-time inference, safety assurance, and deployment costs. This survey provides a comprehensive and critical review of recent progress in leveraging LLMs for AD, focusing on their applications in modular AD pipelines and end- to-end AD systems. We highlight key advancements, identify pressing challenges, and propose promising research directions to bridge the gap between LLMs and AD, thereby facilitating the development of more human-like AD systems. The survey first introduces LLMs’ key features and common training schemes, then delves into their applications in modular AD pipelines and end-to-end AD, respectively, followed by discussions on open challenges and future directions. Through this in-depth analysis, we aim to provide insights and inspiration for researchers and practitioners working at the intersection of AI and autonomous vehicles, ultimately contributing to safer, smarter, and more human-centric AD technologies.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Jiang2024b,
title = {Accurate Cooperative Localization Utilizing LiDAR-equipped Roadside Infrastructure for Autonomous Driving},
author = {Yuze Jiang and Ehsan Javanmardi and Manabu Tsukada and Hiroshi Esaki},
url = {https://arxiv.org/abs/2407.08384 },
doi = {10.1109/ITSC58415.2024.10920101},
year = {2024},
date = {2024-09-24},
urldate = {2024-09-24},
booktitle = {27th IEEE International Conference on Intelligent Transportation Systems (ITSC 2024)},
address = {Edmonton, Canada},
abstract = {Recent advancements in LiDAR technology have significantly lowered costs and improved both its precision and resolution, thereby solidifying its role as a critical component in autonomous vehicle localization. Using sophisticated 3D reg- istration algorithms, LiDAR now facilitates vehicle localization with centimeter-level accuracy. However, these high-precision techniques often face reliability challenges in environments devoid of identifiable map features. To address this limitation, we propose a novel approach that utilizes road side units (RSU) with vehicle-to-infrastructure (V2I) communications to assist vehicle self-localization. By using RSUs as stationary reference points and processing real-time LiDAR data, our method enhances localization accuracy through a cooperative localization framework. By placing RSUs in critical areas, our proposed method can improve the reliability and precision of vehicle localization when the traditional vehicle self-localization technique falls short. Evaluation results in an end-to-end autonomous driving simulator AWSIM show that the proposed method can improve localization accuracy by up to 80% under vulnerable environments compared to traditional localization methods. Additionally, our method also demonstrates robust resistance to network delays and packet loss in heterogeneous network environments.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Gui2024,
title = {Shrinkable Arm-based eHMI on Autonomous Delivery Vehicle for Effective Communication with Other Road Users},
author = {Xinyue Gui and Mikiya Kusunoki and Bofei Huang and Stela Hanbyeol Seo and Chia-Ming Chang and Haoran Xie and Manabu Tsukada and Takeo Igarashi},
doi = {10.1145/3640792.3675716},
year = {2024},
date = {2024-09-22},
urldate = {2024-09-22},
booktitle = {16th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI 2024)},
address = {California, USA},
abstract = {When employing autonomous driving technology in logistics, small autonomous delivery vehicles (aka delivery robots) encounter challenges different from passenger vehicles when interacting with other road users. We conducted an online video survey as a pre-study and found that autonomous delivery vehicles need external human-machine interfaces (eHMIs) to ask for help due to their small size and functional limitations. Inspired by everyday human communication, we chose arms as eHMI to show their request through limb motion and gesture. We held an in-house workshop to identify the arm’s requirements for designing a specific arm with shrink-ability (conspicuous when delivering messages but not affect traffic at other times). We prototyped a small delivery robot with a shrinkable arm and filmed the experiment videos. We conducted two studies (a video-based and a 360-degree-photo VR-based) with 18 participants. We demonstrated that arm-on-delivery robots can increase interaction efficiency by drawing more attention and communicating specific information.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Chauhan2024,
title = {Transforming Pedestrian and Autonomous Vehicles Interactions in Shared Spaces: A Think-Tank Study on Exploring Human-Centric Designs},
author = {Vishal Chauhan and Anubhav Anubhav and Chia-Ming Chang and Jin Nakazato and Ehsan Javanmardi and Alex Orsholits and Takeo Igarashi and Kantaro Fujiwara and Manabu Tsukada
},
doi = {10.1145/3641308.3685037},
year = {2024},
date = {2024-09-22},
urldate = {2024-09-22},
booktitle = {16th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI 2024), Work in Progress (WiP)},
pages = {1-8},
address = {California, USA},
abstract = {Our research focuses on the smart pole interaction unit (SPIU) as an infrastructure external human-machine interface (HMI) to enhance pedestrian interaction with autonomous vehicles (AVs) in shared spaces. We extensively study SPIU with external human-machine interfaces (eHMI) on AVs as an integrated solution. To discuss interaction barriers and enhance pedestrian safety, we engaged 25 participants aged 18-40 to brainstorm design solutions for pedestrian-AV interactions, emphasising effectiveness, simplicity, visibility, and clarity. Findings indicate a preference for real-time SPIU interaction over eHMI on AVs in multiple AV scenarios. However, the combined use of SPIU and eHMI on AVs is crucial for building trust in decision-making. Consequently, we propose innovative design solutions for both SPIU and eHMI on AVs, discussing their pros and cons. This study lays the groundwork for future autonomous mobility solutions by developing human-centric eHMI and SPIU prototypes as ieHMI.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Trumpp2024,
title = {RaceMOP: Mapless Online Path Planning for Multi-Agent Autonomous Racing using Residual Policy Learning},
author = {Raphael Trumpp and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada and Marco Caccamo},
url = {http://github.com/raphajaner/racemop},
doi = {10.1109/IROS58592.2024.10801657},
year = {2024},
date = {2024-09-14},
urldate = {2024-09-14},
booktitle = {The 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)},
address = {Abu Dhabi ,UAE},
abstract = {The interactive decision-making in multi-agent autonomous racing offers insights valuable beyond the domain of self-driving cars. Mapless online path planning is particularly of practical appeal but poses a challenge for safely overtaking opponents due to the limited planning horizon. Accordingly, this paper introduces RaceMOP, a novel method for mapless online path planning designed for multi-agent racing of F1TENTH cars. Unlike classical planners that depend on predefined racing lines, RaceMOP operates without a map, relying solely on local observations to overtake other race cars at high speed. Our approach combines an artificial potential field method as a base policy with residual policy learning to introduce long-horizon planning capabilities. We advance the field by introducing a novel approach for policy fusion with the residual policy directly in probability space. Our experiments for twelve simulated racetracks validate that RaceMOP is capable of long-horizon decision-making with robust collision avoidance during over- taking maneuvers. RaceMOP demonstrates superior handling over existing mapless planners while generalizing to unknown racetracks, paving the way for further use of our method in robotics. We make the open-source code for RaceMOP available at http://github.com/raphajaner/racemop.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Orsholits2024,
title = {PLATONE: An Immersive Geospatial Audio Spatialization Platform},
author = {Alex Orsholits and Yiyuan Qian and Eric Nardini and Yusuke Obuchi and Manabu Tsukada},
doi = {10.1109/MetaCom62920.2024.00020},
year = {2024},
date = {2024-08-12},
urldate = {2024-08-12},
booktitle = {The 2nd Annual IEEE International Conference on Metaverse Computing, Networking, and Applications (MetaCom 2024)},
address = {Hong Kong, China},
abstract = {In the rapidly evolving landscape of mixed reality (MR) and spatial computing, the convergence of physical and virtual spaces is becoming increasingly crucial for enabling immersive, large-scale user experiences and shaping inter-reality dynamics. This is particularly significant for immersive audio at city-scale, where the 3D geometry of the environment must be considered, as it drastically influences how sound is perceived by the listener. This paper introduces PLATONE, a novel proof-of-concept MR platform designed to augment urban contexts with environment-dependent spatialized audio. It leverages custom hardware for localization and orientation, alongside a cloud-based pipeline for generating real-time binaural audio. By utilizing open-source 3D building datasets, sound propagation effects such as occlusion, reverberation, and diffraction are accurately simulated. We believe that this work may serve as a compelling foundation for further research and development.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Takada2024,
title = {Design of Digital Twin Architecture for 3D Audio Visualization in AR},
author = {Tokio Takada and Jin Nakazato and Alex Orsholits and Manabu Tsukada and Hideya Ochiai and Hiroshi Esaki},
doi = {10.1109/MetaCom62920.2024.00044},
year = {2024},
date = {2024-08-12},
urldate = {2024-08-12},
booktitle = {The 2nd Annual IEEE International Conference on Metaverse Computing, Networking, and Applications (MetaCom 2024)},
address = {Hong Kong, China},
abstract = {Digital twins have recently attracted attention from academia and industry as a technology connecting physical space and cyberspace. Digital twins are compatible with Augmented Reality (AR) and Virtual Reality (VR), enabling us to understand information in cyberspace. In this study, we focus on music and design an architecture for a 3D representation of music using a digital twin. Specifically, we organize the requirements for a digital twin for music and design the architecture. We establish a method to perform 3D representation in cyberspace and map the recorded audio data in physical space. In this paper, we implemented the physical space representation using a smartphone as an AR device and employed a visual positioning system (VPS) for self-positioning. For evaluation, in addition to system errors in the 3D representation of audio data, we conducted a questionnaire evaluation with several users as a user study. From these results, we evaluated the effectiveness of the implemented system. At the same time, we also found issues we need to improve in the implemented system in future works.},
key = {CREST},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Iwaki2024b,
title = {Enhancing V2X Communication: Machine Learning Assisted Dynamic mmWave Beam Search},
author = {Ryo Iwaki and Jin Nakazato and Kazuki Maruta and Manabu Tsukada and Hideya Ochiai and Hiroshi Esaki},
doi = {10.1109/ICUFN61752.2024.10625435},
year = {2024},
date = {2024-07-02},
urldate = {2024-07-02},
booktitle = {The 15th International Conference on Ubiquitous and Future Networks (ICUFN2024)},
address = {Budapest, Hungary},
abstract = {Only the chairs can edit This paper addresses the challenges of dynamic beam search in millimeter wave (mmWave) communications for vehicle-to-everything (V2X) applications. With the rapid mobility of connected autonomous vehicles (CAVs) and dense urban environments, maintaining high-quality mmWave connections is critical for the reliability and efficiency of V2X communications. We propose a novel machine learning-assisted framework for dynamic mmWave beam search, which significantly enhances the adaptability and performance of V2X communication systems. Our approach leverages real-time environmental data and CAV dynamics to predict optimal beam directions, improving connection stability. Simulation results demonstrate the effectiveness of the proposed method in a real-world road scenario, offering a partial improvement over conventional beam search techniques.},
note = {Best Paper Award},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Yoshimura2024,
title = {Towards Robust Communication in ITS: A Comprehensive Study of Blockchain for V2I},
author = {Atsuki Yoshimura and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
doi = {10.1109/ICUFN61752.2024.10624948},
year = {2024},
date = {2024-07-02},
urldate = {2024-07-02},
booktitle = {The 15th International Conference on Ubiquitous and Future Networks (ICUFN2024)},
address = {Budapest, Hungary},
abstract = {V2X in connected autonomous vehicles (CAV) plays an important role in information sharing through communication. The integration of V2X and blockchain has the potential to create functionalities such as seamless V2X information sharing, similar to Bitcoin, and post-accident investigation utilities that leverage data immutability. However, the integration of blockchain into V2X communication requires addressing CAV mobility. In this study, we propose a framework that takes into account the high mobility of CAVs. Furthermore, we propose a method that not only addresses this challenge but also achieves load balancing by facilitating cooperation among nodes responsible for member management. In this paper, we integrate the ITS simulator, the communication simulator, and the blockchain simulator to build an infrastructure that can be evaluated end-to-end. Using the integrated simulator, we perform an evaluation based on metrics such as latency and member change rate in a mobile environment with a single roadside unit (RSU). In the future, we plan to implement the proposed methodology and perform evaluations in environments with multiple RSUs.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Chi2024,
title = {V2I Blockage Modeling and Performance Evaluation for Connected Autonomous Vehicle},
author = {Weiqi Chi and Jin Nakazato and Tomoki Murakami and Manabu Tsukada},
doi = {10.1109/VTC2024-Spring62846.2024.10683381},
year = {2024},
date = {2024-06-24},
urldate = {2024-06-24},
booktitle = {The IEEE 99th Vehicular Technology Conference (VTC2024-Spring)},
address = {Singapore},
abstract = {The burgeoning Intelligent Transportation System (ITS) spurs global technological advancements, notably in innovative community development through vehicle-to-everything (V2X) communication. This study focuses on the high data rates and low latency offered by a millimeter-wave (mmWave) enabled vehicular network while addressing the significant challenge of link quality degradation due to blockages, exacerbated by the mmWave band’s small wavelength in high mobility and traffic conditions. We propose an RSU-assisted ITS system tailored for multi-lane, straight-road scenarios, effectively identifying blockage status for vehicles. Combining Simulation of Urban Mobility (SUMO) and MATLAB, this blockage-aware scheme lays the groundwork for future ITS enhancements. The research also delves into the effects of various frequency bands, vehicle types, and communication ranges, offering a holistic system performance analysis.},
note = {IEEE VTS Tokyo/Japan Chapter Young Researcher's Encouragement Award},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Ito2024,
title = {Enhancing Real-Time Streaming Quality through a Multipath Redundant Communication Framework},
author = {Koki Ito and Jin Nakazato and Romain Fontugne and Manabu Tsukada and Hiroshi Esaki},
doi = {10.23919/IFIPNetworking62109.2024.10619885},
year = {2024},
date = {2024-06-03},
urldate = {2024-06-03},
booktitle = {IFIP/IEEE Networking 2024},
address = {Thessaloniki, Greece},
abstract = {Recently, as networks operate as the infrastructure of modern society, the demands placed on the network by applications have become more complex. In particular, an increasing annual demand for high-capacity and low-latency services, including real-time streaming. 5G services have been launched to meet this demand, but their stability varies de- pending on location and time and can only sometimes be considered sufficient. One method to improve communication stability is multipath redundant communication, and much research has been conducted in this area. However, most of this research has focused on TCP-based communication and cannot be applied to real-time UDP streaming. Hence, we propose a multipath redundant communication framework to improve the quality of real-time media streaming communication. Tunneling at the IP layer in our proposed framework was performed to overcome the limitations of transport layer protocols, which was a challenge for traditional multipath redundant communication systems. Furthermore, to address the packet order inconsistency caused by multipath redundant communication, a buffering mechanism was implemented on the receiving side of our system. Our proposed system was verified using multipath redundant communication and multiple mobile networks from a vehicle moving in an urban area. The experiments used a real-time streaming application based on WebRTC, and the framework significantly reduced packet loss and improved bitrate compared to existing multipath redundant communication systems without interfering with the application’s congestion control.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tao2024b,
title = {Zero-Knowledge Proof of Distinct Identity: a Standard-compatible Sybil-resistant Pseudonym Extension for C-ITS},
author = {Ye Tao and Hongyi Wu and Ehsan Javanmardi and Manabu Tsukada and Hiroshi Esaki},
url = {https://arxiv.org/abs/2403.14020},
doi = {10.1109/IV55156.2024.10588511},
year = {2024},
date = {2024-06-02},
urldate = {2024-06-02},
booktitle = {35th IEEE Intelligent Vehicles Symposium (IV2024)},
address = {Jeju Island, Korea},
abstract = {Pseudonyms are widely used in Cooperative Intelligent Transport Systems (C-ITS) to protect the location privacy of vehicles. However, the unlinkability nature of pseudonyms also enables Sybil attacks, where a malicious vehicle can pretend to be multiple vehicles at the same time. In this paper, we propose a novel protocol called zero-knowledge Proof of Distinct Identity (zk-PoDI,) which allows a vehicle to prove that it is not the owner of another pseudonym in the local area, without revealing its actual identity. Zk-PoDI is based on the Diophantine equation and zk-SNARK, and does not rely on any specific pseudonym design or infrastructure assistance. We show that zk-PoDI satisfies all the requirements for a practical Sybil-resistance pseudonym system, and it has low latency, adjustable difficulty, moderate computation overhead, and negligible communication cost. We also discuss the future work of implementing and evaluating zk-PoDI in a realistic city-scale simulation environment.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Maruta2024,
title = {Millimeter-Wave Fast Beam Tracking Enabled by RAN/V2X Cooperation},
author = {Kazuki Maruta and Jin Nakazato and Kengo Suzuki and Dou Hu and Ryo Iwaki and Sojin Ozawa and Yuki Sasaki and Hideya So and Manabu Tsukada},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/376818826_Millimeter-Wave_Fast_Beam_Tracking_Enabled_by_RANV2X_Cooperation/links/658ac62e6f6e450f19a60664/Millimeter-Wave-Fast-Beam-Tracking-Enabled-by-RAN-V2X-Cooperation.pdf},
doi = {10.1109/ICAIIC60209.2024.10463482},
year = {2024},
date = {2024-02-19},
urldate = {2024-02-19},
booktitle = {International Conference on Artificial Intelligence in Information and Communication (ICAIIC 2024)},
address = {Osaka, Japan},
abstract = {Only the chairs can edit Automated driving has the same limitations as human drivers because it functions as a replacement for humans and operates based on local information using onboard sensors and computers. Cooperative automated driving is expected to achieve both safety and efficiency, which could not be achieved by imitating human driving, by sharing sensor information from roadside equipment and other vehicles. Since such sensor information is enormous, it is desirable to utilize millimeter-waves, which are capable of high-capacity transmission. However, wireless communication systems for cooperative automated driving have the challenge of radio quality degradation due to vehicle movement. Our research project aims to realize stable millimeter-wave transmission by incorporating Open RAN (O-RAN) and vehicle-to-everything (V2X) functions. This paper presents the overall proposed concept and an example of validation; we show the results of evaluating our previously proposed fast beam following scheme in a handover environment with multiple roadside units.},
note = {Excellent Paper Award},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Karimata2024,
title = {Multi-UAVs Strategies for Ad Hoc Network with Multi-Agent Reinforcement Learning},
author = {George Karimata, Jin Nakazato, Gia Khanh Tran, Katsuya Suto, Manabu Tsukada and Hiroshi Esaki, },
url = {https://icoin.org/media?key=site/icoin2024/abs/P-4-1.pdf},
doi = {10.1109/ICOIN59985.2024.10572031},
year = {2024},
date = {2024-01-17},
urldate = {2024-01-17},
booktitle = {The 38th International Conference on Information Networking (ICOIN2024)},
address = {Ho Chi Minh City, Vietnam},
abstract = {In recent years, extensive research has focused on leveraging advanced technologies beyond 5G and for Industry 5.0 to promote sustainability and prosperity in society. Our study advances this effort by seeking to create an aerial perspective using Unmanned Aerial Vehicles (UAVs). This paper introduces a method for optimizing UAV deployment strategies using multi-agent reinforcement learning, facilitating the formation of a flying ad hoc network. The results demonstrate practical cooperation among UAVs in flight.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Jiang2023,
title = {Roadside LiDAR Assisted Cooperative Localization for Connected Autonomous Vehicles},
author = {Yuze Jiang and Ehsan Javanmard and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
url = {https://arxiv.org/abs/2311.07913},
year = {2023},
date = {2023-12-14},
urldate = {2023-12-14},
booktitle = {ACM Intelligent Computing and its Emerging Applications (ICEA 2023)},
abstract = {Advancements in LiDAR technology have led to more cost-effective production while simultaneously improving precision and resolution. As a result, LiDAR has become integral to vehicle localization, achieving centimeter-level accuracy through techniques like Normal Distributions Transform (NDT) and other advanced 3D registration algorithms. Nonetheless, these approaches are reliant on high-definition 3D point cloud maps, the creation of which involves significant expenditure. When such maps are unavailable or lack sufficient features for 3D registration algorithms, localization accuracy diminishes, posing a risk to road safety. To address this, we proposed to use LiDAR-equipped roadside unit and Vehicle-to-Infrastructure (V2I) communication to accurately estimate the connected vehicle's position and help the vehicle when its self-localization is not accurate enough. Our simulation results indicate that this method outperforms traditional NDT scan matching-based approaches in terms of localization accuracy.},
note = {Best paper award (Silver)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Matsumoto2023b,
title = {Localizability Estimation for Autonomous Driving: A Deep Learning-Based Place Recognition Approach},
author = {Kazuto Matsumoto and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada},
doi = {10.1109/IRC59093.2023.00052},
year = {2023},
date = {2023-12-11},
urldate = {2023-12-11},
booktitle = {IEEE Robotic Computing 2023},
address = {California, USA},
abstract = {In recent years, research and development aimed at the societal implementation of autonomous driving have attracted increasing attention. Localization, which involves obtaining in- formation about the surrounding environment from sensor data and estimating the vehicle’s position, is necessary for realizing autonomous driving. Localization is commonly performed with 3D LiDAR as a sensor owing to its high measurement accuracy and immunity to ambient light conditions, which allow for precise localization. However, localization accuracy may decrease when the surrounding area does not have distinctive features. In this study, we proposed a method based on deep learning to estimate localization accuracy for autonomous driving. The overall localization accuracy can be improved by estimating the accuracy of localization using other sensors, such as GNSS and IMU, or pavement markings in areas with poor accuracy. We created a dataset for estimating localization accuracy using an open-source autonomous driving simulator. In an experiment, we applied the proposed method to the created dataset. Estimations with low MSE were obtained. The results indicate that the proposed method can accurately estimate localization accuracy.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Furuta2023,
title = {Web-Based BIM Platform for Building Digital Twin},
author = {Satoru Furuta and Jin Nakazato and Manabu Tsukada},
doi = {10.1109/DTPI59677.2023.10365476},
year = {2023},
date = {2023-11-07},
urldate = {2023-11-07},
booktitle = {3rd Annual IEEE International Conference on Digital Twins and Parallel Intelligence (DTPI 2023)},
address = {Florida, USA},
abstract = {Building digital twin (BDT) is utilized throughout the lifecycle of a building. It serves for efficient operations during the design and construction phases, and during the operational and maintenance phases, it’s used for asset management and as field maps for robots. Building Information Modeling (BIM), which contains both semantics and geometry data of building elements, holds promise as a data source for BDT. We have extracted four key technical challenges of the digital twin, particularly vital during the operational and maintenance phases: software, visualization, update, and real-scene reconstruction. Due to the exclusive and static nature of BIM, these challenges also pose significant issues in the context of BDT. To address these challenges, we designed and implemented a Web-based BIM platform. The implemented application shows not only geometry data but also semantics data, enables easy overlay with the latest indoor conditions, and provides updating functionality. The developed system is essential for the continuous operation of BDT in dynamic indoor environments. We evaluated the application through a questionnaire survey. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Lin2023c,
title = {Potential Field-based Path Planning with Interactive Speed Optimization for Autonomous Vehicles},
author = {Pengfei Lin and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada},
url = {https://arxiv.org/abs/2306.06987},
doi = {10.1109/IECON51785.2023.10311890},
year = {2023},
date = {2023-10-16},
urldate = {2023-10-16},
booktitle = {49th Annual Conference of the IEEE Industrial Electronics Society (IECON 2023)},
abstract = {Path planning is critical for autonomous vehicles (AVs) to determine the optimal route while considering constraints and objectives. The potential field (PF) approach has become prevalent in path planning due to its simple structure and computational efficiency. However, current PF methods used in AVs focus solely on the path generation of the ego vehicle while assuming that the surrounding obstacle vehicles drive at a preset behavior without the PF-based path planner, which ignores the fact that the ego vehicle’s PF could also impact the path generation of the obstacle vehicles. To tackle this problem, we propose a PF-based path planning approach where local paths are shared among ego and obstacle vehicles via vehicle-to- vehicle (V2V) communication. Then by integrating this shared local path into an objective function, a new optimization function called interactive speed optimization (ISO) is designed to allow driving safety and comfort for both ego and obstacle vehicles. The proposed method is evaluated using MATLAB/Simulink in the urgent merging scenarios by comparing it with conventional methods. The simulation results indicate that the proposed method can mitigate the impact of other AVs’ PFs by slowing down in advance, effectively reducing the oscillations for both ego and obstacle AVs.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Sugizaki2023,
title = {Wireless Ad-Hoc Federated Learning for Cooperative Map Creation and Localization Models},
author = {Yusuke Sugizaki and Hideya Ochiai and Muhammad Asad and Manabu Tsukada and Hiroshi Esaki},
doi = {10.1109/WF-IoT58464.2023.10539517},
year = {2023},
date = {2023-10-12},
urldate = {2023-10-12},
booktitle = {The 9th IEEE World Forum on Internet of Things (IEEE WFIoT2023)},
address = {Aveiro, Portugal},
abstract = {Although Wi-Fi signals have been used for localization, many existing methods require gathering Wi-Fi information about the area in advance. This study proposed a novel system in which wireless ad-hoc federated learning is used to learn localization models and create maps cooperatively during regular movement. In this system, a combination of classification models is used to perform localization from Wi-Fi signal strength measured as received signal strength indicator (RSSI). In this study, RSSI data in a real-world Wi-Fi environment were collected to train and test localization models. The proposed method achieved localization accuracy between 91.30% and 96.11 %, which demonstrated the ability of the method to train localization models collaboratively.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Chauhan2023,
title = {Keep Calm and Cross: Smart Pole Interaction Unit for Easing Pedestrian Cognitive Load},
author = {Vishal Chauhan and Chia-Ming Chang and Ehsan Javanmardi and Jin Nakazato and Koki Toda and Pengfei Lin and Takeo Igarashi and Manabu Tsukada},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/374582122_Keep_Calm_and_Cross_Smart_Pole_Interaction_Unit_for_Easing_Pedestrian_Cognitive_Load/links/6525681eb32c91681fb2e1b5/Keep-Calm-and-Cross-Smart-Pole-Interaction-Unit-for-Easing-Pedestrian-Cognitive-Load.pdf},
doi = {10.1109/WF-IoT58464.2023.10539511},
year = {2023},
date = {2023-10-12},
urldate = {2023-10-12},
booktitle = {The 9th IEEE World Forum on Internet of Things (IEEE WFIoT2023)},
address = {Aveiro, Portugal},
abstract = {Recently, there has been a growing emphasis on autonomous vehicles (AVs), and as they coexist with pedestrians, ensuring pedestrian safety at crosswalks has become paramount. While AVs exhibit commendable performance on traditional roads with established traffic infrastructure, their interaction in different environments, such as shared spaces lacking traffic lights or sign rules (also known as naked streets), can present significant challenges, including right-of-way and accessibility concerns. To address these challenges, this study proposes a novel approach to enhance pedestrian safety in shared spaces, focusing on the proposed smart pole interaction unit (SPIU) combined with an external human-machine interface (eHMI). By evaluating the proposal of SPIU developed by a virtual reality system, we explore its usability and effectiveness in facilitating vehicle-to-pedestrian (V2P) interactions at crosswalks. Our findings from this study showed that SPIU facilitates safe, quicker decision-making to stop and pass at crosswalks in shared space and reduces cognitive load compared to scenarios where an SPIU is absent for pedestrians and reduce the need for eHMI to see on multiple AVs. The SPIU addition with the eHMI in vehicles yields a noteworthy 21 % improvement in response time, enhancing efficiency during pedestrian stops. In both scenarios, whether with a single AV (1-way) or multiple AVs (2-way), SPIU has a positive impact on interaction dynamics and statistically demonstrates a significant improvement (p = 0.001). },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Bao2023,
title = {Personalized Causal Factor Generalization for Subjective Risky Scene Understanding with Vision Transformer},
author = {Naren Bao and Alexander Carballo and Manabu Tsukada and Kazuya Takeda},
doi = {10.1109/ITSC57777.2023.10422148},
year = {2023},
date = {2023-09-24},
urldate = {2023-09-24},
booktitle = {The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)},
address = {Bilbao, Bizkaia, Spain},
abstract = {This paper presents a framework to understanding subjective driving scene perception by Vision Transformer for Environmental Feature Extraction within a Causal Modeling Analysis method. By leveraging vision transformer models, informative features are extracted from video camera images capturing the surrounding environment. Through the causal analysis, the causal effects of these variables on subjective risk perception are explored, shedding light on the factors influencing individuals' perception of driving risk. The findings demonstrate understanding of environmental features and individual difference on risk perception, providing a deeper understanding of risky scene perception. The paper concludes with this approach unifies selective attentional phenomena can improve the scene understanding for subjective perception in real-world driving scenarios aiming to enhance driving safety based on the identified causal factors. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Yamazaki2023,
title = {ToST: Tokyo SUMO traffic scenario },
author = {Yuji Yamazaki and Yasumasa Tamura and Xavier Defago and Ehsan Javanmardi and Manabu Tsukada},
url = {https://github.com/dfg-lab/ToSTScenario},
doi = {10.1109/ITSC57777.2023.10422517},
year = {2023},
date = {2023-09-24},
urldate = {2023-09-24},
booktitle = {The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)},
address = {Bilbao, Bizkaia, Spain},
abstract = {In recent years, research, development, and demonstrations aimed at the societal implementation of autonomous driving have attracted increasing attention. Localization, which involves obtaining information of the surrounding environment from sensor data and estimating the vehicle's position, is necessary for realizing autonomous driving. Localization is commonly performed with 3D LiDAR as a sensor owing to its high measurement accuracy and immunity to ambient light conditions, which allow for precise localization. However, when the surrounding area has distinctive features, localization accuracy may decrease. In this study, we proposed a method based on deep learning to predict the localization accuracy for autonomous driving. The overall localization accuracy can be improved by predicting the accuracy of localization using other sensors, such as GNSS and IMU, or pavement markings in areas with poor accuracy. We created a dataset for predicting the localization accuracy using an open-source autonomous driving simulator. In an experiment, we applied the proposed method to the created dataset. Thresholds were set for errors in the x-direction, y-direction, and distance for localization. Predictions with high accuracy and F-values were obtained. The results indicate that the proposed method can accurately predict the localization accuracy. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Lin2023b,
title = {Occlusion-Aware Path Planning for Collision Avoidance: Leveraging Potential Field Method with Responsibility-Sensitive Safety},
author = {Pengfei Lin and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada},
url = {https://arxiv.org/abs/2306.06981},
doi = {10.1109/ITSC57777.2023.10422621},
year = {2023},
date = {2023-09-24},
urldate = {2023-09-24},
booktitle = {The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)},
series = {Bilbao, Bizkaia, Spain},
abstract = {Collision avoidance (CA) has always been the foremost task for autonomous vehicles (AVs) under safety criteria. And path planning is directly responsible for generating a safe path to accomplish CA while satisfying other commands. Due to the real-time computation and simple structure, the potential field (PF) has emerged as one of the mainstream path-planning algorithms. However, the current PF is primarily simulated in ideal CA scenarios, assuming complete obstacle information while disregarding occlusion issues where obstacles can be partially or entirely hidden from the AV's sensors. During the occlusion period, the occluded obstacles do not possess a PF. Once the occlusion is over, these obstacles can generate an instantaneous virtual force that impacts the ego vehicle. Therefore, we propose an occlusion-aware path planning (OAPP) with the responsibility-sensitive safety (RSS)-based PF to tackle the occlusion problem for non-connected AVs. We first categorize the detected and occluded obstacles, and then we proceed to the RSS violation check. Finally, we can generate different virtual forces from the PF for occluded and non-occluded obstacles. We compare the proposed OAPP method with other PF-based path planning methods via MATLAB/Simulink. The simulation results indicate that the proposed method can eliminate instantaneous lateral oscillation or sway and produce a smoother path than conventional PF methods.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tao2023c,
title = {Flowsim: A Modular Simulation Platform for Microscopic Behavior Analysis of City-Scale Connected Autonomous Vehicles},
author = {Ye Tao and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
url = {https://github.com/tlab-wide/flowsim
https://arxiv.org/abs/2306.05738},
doi = {10.1109/ITSC57777.2023.10421900},
year = {2023},
date = {2023-09-24},
urldate = {2023-09-24},
booktitle = {The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)},
address = {Bilbao, Bizkaia, Spain},
abstract = {As connected autonomous vehicles (CAVs) become increasingly prevalent, there is a growing need for simulation platforms that can accurately evaluate CAV behavior in large-scale environments. In this paper, we propose Flowsim, a novel simulator specifically designed to meet these requirements. Flowsim offers a modular and extensible architecture that enables the analysis of CAV behaviors in large-scale scenarios. It provides researchers with a customizable platform for studying CAV interactions, evaluating communication and networking protocols, assessing cybersecurity vulnerabilities, optimizing traffic management strategies, and developing and evaluating policies for CAV deployment. Flowsim is implemented in pure Python in approximately 1,500 lines of code, making it highly readable, understandable, and easily modifiable. We verified the functionality and performance of Flowsim via a series of experiments based on realistic traffic scenarios. The results show the effectiveness of Flowsim in providing a flexible and powerful simulation environment for evaluating CAV behavior and data flow. Flowsim is a valuable tool for researchers, policymakers, and industry professionals who are involved in the development, evaluation, and deployment of CAVs. The code of Flowsim is publicly available on GitHub under the MIT license. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Yosodipuro2023,
title = {Mixed-traffic Intersection Management using Traffic-load-responsive Reservation and V2X-enabled Speed Coordination},
author = {Nicholaus Danispadmanaba Yosodipuro and Ehsan Javanmardi and Jin Nakazato and Yasumasa Tamura and Xavier Defago and Manabu Tsukada},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/374470825_Mixed-traffic_Intersection_Management_using_Traffic-load-responsive_Reservation_and_V2X-enabled_Speed_Coordination/links/651ee8d63ab6cb4ec6bde79a/Mixed-traffic-Intersection-Management-using-Traffic-load-responsive-Reservation-and-V2X-enabled-Speed-Coordination.pdf},
doi = {10.1109/ITSC57777.2023.10422248},
year = {2023},
date = {2023-09-24},
urldate = {2023-09-24},
booktitle = {The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)},
address = {Bilbao, Bizkaia, Spain},
abstract = {Vehicle-to-everything (V2X) communication enables connected autonomous vehicles (CAVs) to share information and generate optimal decisions. The networking abilities of CAVs have led to the development of unsignalized autonomous intersection management (AIM) methods that leverage CAVs to significantly improve traffic flows. However, AIM methods assume 100% CAV market penetration, which is currently unrealistic owing to the gradual adoption of CAVs. Therefore, CAVs must share road usage with nonconnected vehicles (NCVs). Thus, we propose a mixed-traffic intersection management method that considers NCVs while ensuring high traffic flow, called traffic-load-responsive reservation for intersection management (TLRRIM). In TLRRIM, the roadside unit (RSU) first classifies vehicles and groups them into clusters before selecting a reservation cluster to cross an intersection. The reservation cluster selection considers both traffic load and crossing urgency. In addition, the RSU utilizes V2X-enabled speed coordination (VESC) for CAVs within the reservation cluster to further improve traffic flow, while utilizing traffic lights to guide NCVs. Simulation-based experiments using OpenCDA and CARLA showed that TLRRIM can increase throughput and reduce waiting time by up to 89.63% and 60.71%, respectively, compared with the fixed-time signaling method. Moreover, adding VESC can increase throughput by 12.21% and reduce waiting time by 10.80%, further enhancing traffic flow. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Wang2023,
title = {Overcoming Environmental Challenges in CAVs Through MEC-Based Federated Learning},
author = {Zekun Wang and Jin Nakazato and Muhammad Asad and Ehsan Javanmardi and Manabu Tsukada},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/371685830_Overcoming_Environmental_Challenges_in_CAVs_through_MEC-based_Federated_Learning/links/64a420ea8de7ed28ba7465c7/Overcoming-Environmental-Challenges-in-CAVs-through-MEC-based-Federated-Learning.pdf},
doi = {10.1109/ICUFN57995.2023.10200688},
year = {2023},
date = {2023-07-04},
urldate = {2023-07-04},
booktitle = {14th International Conference on Ubiquitous and Future Networks (ICUFN2023)},
pages = {1-6},
address = {Paris, France},
abstract = {Connected autonomous vehicles (CAVs), through vehicle-to-everything communication and computing resources, enable the vital exchange of information. Although deep learning is crucial in this landscape, it requires extensive and intricate datasets covering all potential scenarios. Furthermore, this situation poses a hazard, as the likelihood of accidents associated with imbalanced datasets increases, particularly in scenarios where processing analysis is compromised due to fluctuating weather conditions. We propose a Federated Learning (FL) framework undergirded by Multi-Access Edge Computing (MEC) to counter these challenges. This local device-focused framework enhances task-specific models' caching and continual updating across various conditions. In a more specific sense, edge nodes (ENs) operate as MEC, each caching multiple dedicated models and serving as the aggregator as part of the FL process. Additionally, we have engineered two innovative algorithms that categorize various states into multiple classes, thereby ensuring the efficient utilization of computing resources in ENs. Simulation results substantiate the effectiveness of our approach, showing that the proposed dedicated model consistently outperforms a general model designed for all situations.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Inokuchi2023b,
title = {Semantic Digital Twin for interoperability and comprehensive management of data assets},
author = {Kazuma Inokuchi and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/371903091_Semantic_digital_twin_for_interoperability_and_Comprehensive_Management_of_Data_Assets/links/649b13318de7ed28ba5ca665/Semantic-digital-twin-for-interoperability-and-Comprehensive-Management-of-Data-Assets.pdf},
doi = {10.1109/MetaCom57706.2023.00049},
year = {2023},
date = {2023-06-26},
urldate = {2023-06-26},
booktitle = {IEEE International Conference on Metaverse Computing, Networking and Applications (IEEE MetaCom 2023)},
address = {Kyoto, Japan},
abstract = {Fusion of the real and virtual worlds is essential for applying digital technology to the infrastructure of human life. A digital twin is one of the technologies that aim to integrate real and virtual space. It creates a digital world with high fidelity to reality by accumulating exhaustive information from sensors to improve simulation and prediction accuracy. However, traditional digital twins have data asset management challenges owing to the physical, temporal, and structural heterogeneity of their objects. In this paper, we propose two metadata schemas that leverage semantics to construct a designer-oriented digital twin. Moreover, we implemented a viewer that reproduced the office-like demonstration field to verify the application of the proposed ontology. The proposed method enables a generic description of the dynamic behaviors of any entity by integrating physical twins faithful to the real world with virtual models expected by designers. We compared the proposed ontologies with existing techniques, conducted user evaluations, and discussed possible approaches for further enhancements for widespread use.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Asabe2023b,
title = {AutowareV2X: Reliable V2X Communication and Collective Perception for Autonomous Driving},
author = {Yu Asabe and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
url = {https://github.com/tlab-wide/AutowareV2X
https://tlab-wide.github.io/AutowareV2X/main/
https://www.youtube.com/watch?v=57fx3-gUNxU},
doi = {10.1109/VTC2023-Spring57618.2023.10199425},
year = {2023},
date = {2023-06-20},
urldate = {2023-06-20},
booktitle = {The 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)},
address = {Florence, Italy},
abstract = {For cooperative intelligent transport systems (C-ITS), vehicle-to-everything (V2X) communication is utilized to allow autonomous vehicles to share critical information with each other. We propose AutowareV2X, an implementation of a V2X communication module that is integrated into the autonomous driving (AD) software, Autoware. AutowareV2X provides external connectivity to the entire AD stack, enabling the end-to-end (E2E) experimentation and evaluation of connected autonomous vehicles (CAV). The Collective Perception Service was also implemented, allowing the transmission of Collective Perception Messages (CPMs). A dual-channel mechanism that enables wireless link redundancy on the critical object information shared by CPMs is also proposed. Performance evaluation in field experiments has indicated that the E2E latency of perception information is around 30 ms, and shared object data can be used by the AD software to conduct collision avoidance maneuvers. Dual-channel delivery of CPMs enabled the CAV to dynamically select the best CPM from CPMs received from different links, depending on the freshness of their information.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Lin2023,
title = {Time-To-Collision-Aware Lane-Change Strategy Based on Potential Field and Cubic Polynomial for Autonomous Vehicles},
author = {Pengfei Lin and Ehsan Javanmardi and Ye Tao and Vishal Chauhan and Jin Nakazato and Manabu Tsukada},
url = {https://arxiv.org/abs/2306.06981},
year = {2023},
date = {2023-06-04},
urldate = {2023-06-04},
booktitle = {2023 IEEE Intelligent Vehicles Symposium (IEEE IV 2023)},
address = {Anchorage, Alaska, USA},
abstract = {Making safe and successful lane changes (LCs) is one of the many vitally important functions of autonomous vehicles (AVs) that are needed to ensure safe driving on expressways. Recently, the simplicity and real-time performance of the potential field (PF) method have been leveraged to design decision and planning modules for AVs. However, the LC trajectory planned by the PF method is usually lengthy and takes the ego vehicle laterally parallel and close to the obstacle vehicle, which creates a dangerous situation if the obstacle vehicle suddenly steers. To mitigate this risk, we propose a time-to-collision-aware LC (TTCA-LC) strategy based on the PF and cubic polynomial in which the TTC constraint is imposed in the optimized curve fitting. The proposed approach is evaluated using MATLAB/Simulink under high-speed conditions in a comparative driving scenario. The simulation results indicate that the TTCA-LC method performs better than the conventional PF-based LC (CPF-LC) method in generating shorter, safer, and smoother trajectories. The length of the LC trajectory is shortened by over 27.1%, and the curvature is reduced by approximately 56.1% compared with the CPF-LC method.
},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tao2023,
title = {zk-PoT: Zero-Knowledge Proof of Traffic for Privacy Enabled Cooperative Perception},
author = {Ye Tao and Yuze Jiang and Pengfei Lin and Manabu Tsukada and Hiroshi Esaki},
url = {http://arxiv.org/abs/2211.07875},
doi = {10.1109/CCNC51644.2023.10059601},
year = {2023},
date = {2023-01-08},
urldate = {2023-01-08},
booktitle = {2023 IEEE 20th Annual Consumer Communications & Networking Conference (CCNC)},
address = {Las Vegas, NV, USA},
abstract = {Cooperative perception is an essential and widely discussed application of connected automated vehicles. However, the authenticity of perception data is not ensured, because the vehicles cannot independently verify the event they did not see. Many methods, including trust-based (i.e., statistical) approaches and plausibility-based methods, have been proposed to determine data authenticity. However, these methods cannot verify data without a priori knowledge. In this study, a novel approach of constructing a self-proving data from the number plate of target vehicles was proposed. By regarding the pseudonym and number plate as a shared secret and letting multiple vehicles prove they know it independently, the data authenticity problem can be transformed to a cryptography problem that can be solved without trust or plausibility evaluations. Our work can be adapted to the existing works including ETSI/ISO ITS standards while maintaining backward compatibility. Analyses of common attacks and attacks specific to the proposed method reveal that most attacks can be prevented, whereas preventing some other attacks, such as collusion attacks, can be mitigated. Experiments based on realistic data set show that the rate of successful verification can achieve 70% to 80% at rush hours.
},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Lin2022b,
title = {Cooperative Path Planning Using Responsibility-Sensitive Safety (RSS)-based Potential Field with Sigmoid Curve},
author = {Pengfei Lin and Manabu Tsukada},
url = {https://youtu.be/AhgptWUyzSc},
doi = {10.1109/VTC2022-Spring54318.2022.9860508},
year = {2022},
date = {2022-06-19},
urldate = {2022-06-19},
booktitle = {The 2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring)},
address = {Helsinki, Finland},
abstract = {Potential field (PF)-based path planning is reported to be highly efficient for autonomous vehicles because it performs risk-aware computation and has a simple structure. However, the inherent limitations of the PF make it vulnerable in some specific traffic scenarios, such as local minima and oscillations in close obstacles. Therefore, a hybrid path planning with the sigmoid curve has recently been presented to generate better trajectories than those generated by the PF for collision avoidance. However, it is time-consuming and less applicable in complex dynamic environments, especially in traffic emergencies. To address these limitations, we propose a cooperative hybrid path planning (CHPP) approach that involves collaboration with adjacent vehicles for emergency collision avoidance via V2V communication. Moreover, the responsibility-sensitive safety (RSS) model is introduced to enhance the PF and sigmoid curve for safe-critical and time-saving requirements. The effectiveness of the proposed CHPP method compared with the state-of-the-art methods is studied through simulation of both static and dynamic traffic emergency scenarios. The simulation results prove that the CHPP approach performs better in terms of computation time (0.02 s faster) and driving safety (avoiding collision) than other methods, which are more supportive for emergency cooperative driving.
},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Lin2022c,
title = {Adaptive Potential Field with Collision Avoidance for Connected Autonomous Vehicles},
author = {Pengfei Lin and Manabu Tsukada},
doi = {10.23919/ASCC56756.2022.9828160},
year = {2022},
date = {2022-05-03},
urldate = {2022-05-03},
booktitle = {13th Asian Control Conference (ASCC) 2022},
address = {Jeju, Korea},
abstract = {Potential field (PF), as a risk assessment method, is proposed to enhance autonomous vehicles’ (AVs) safety in collision avoidance. However, current PF targets mainly standalone-mode AVs (SAVs) by evaluating their relative position and velocity. In addition, the risk energy of the PF is usually assigned an infinite value along the z-axis. Therefore, this study presents an adaptive potential field (APF) for connected autonomous vehicles (CAVs). Valuable information (heading angle, steering wheel angle, etc.) other than relative position and velocity is supplemented to PF. Furthermore, we separate the APF from the cost function of the model predictive controller (MPC) to compute the desired reference signals directly, saving more computation time. The proposed APF-MPC is co-simulated in a comparative driving scenario via MATLAB/Simulink and CarSim simulator compared with the latest PF-MPC method.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tsukada2022,
title = {Misbehavior Detection Using Collective Perception under Privacy Considerations},
author = {Manabu Tsukada and Shimpei Arii and Hideya Ochiai and Hiroshi Esaki},
url = {https://arxiv.org/abs/2111.03461
https://youtu.be/UeHoSv5OAuc},
doi = {10.1109/CCNC49033.2022.9700564},
year = {2022},
date = {2022-01-08},
urldate = {2022-01-08},
booktitle = {2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC)},
address = {Online},
abstract = {In cooperative ITS, security and privacy protection are essential. Cooperative Awareness Message (CAM) is a basic V2V message standard, and misbehavior detection is critical for protection against attacking CAMs from the inside system, in addition to node authentication by Public Key Infrastructure (PKI). On the contrary, pseudonym IDs, which have been introduced to protect privacy from tracking, make it challenging to perform misbehavior detection. In this study, we improve the performance of misbehavior detection using observation data of other vehicles. This is referred to as collective perception message (CPM), which is becoming the new standard in European countries. We have experimented using realistic traffic scenarios and succeeded in reducing the rate of rejecting valid CAMs (false positive) by approximately 15 percentage points while maintaining the rate of correctly detecting attacks (true positive).},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Nakagawa2021,
title = {WebRTC-based measurement tool for peer-to-peer applications and preliminary findings with real users},
author = {Kosuke Nakagawa and Manabu Tsukada and Keiichi Shima and Hiroshi Esaki},
url = {http://arxiv.org/abs/2112.02163
https://youtu.be/4XeCpuBLa7E},
doi = {10.1145/3497777.3498544},
year = {2021},
date = {2021-12-14},
urldate = {2021-12-14},
booktitle = {16th Asian Internet Engineering Conference (AINTEC)},
address = {Online},
abstract = {Direct peer-to-peer (P2P) communication is often used to minimize the end-to-end latency for real-time applications that require accurate synchronization, such as remote musical ensembles. However, there are few studies on the performance of P2P communication between home network environments, thus hindering the deployment of services that require synchronization. In this study, we developed a P2P performance measurement tool using the Web Real-Time Communication (WebRTC) statistics application programming interface. Using this tool, we can easily measure P2P performance between home network environments on a web browser without downloading client applications. We also verified the reliability of round-trip time (RTT) measurements using WebRTC and confirmed that our system could provide the necessary measurement accuracy for RTT and jitter measurements for real-time applications. In addition, we measured the performance of a full mesh topology connection with 10 users in an actual environment in Japan. Consequently, we found that only 66% of the peer connections had a latency of 30 ms or less, which is the minimum requirement for high synchronization applications, such as musical ensembles.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Cai2021,
title = {MAC address randomization tolerant crowd monitoring system using Wi-Fi packets},
author = {Yuyi Cai and Manabu Tsukada and Hideya Ochiai and Hiroshi Esaki},
url = {http://arxiv.org/abs/2112.02161
https://youtu.be/gVkdQ8LWDdI},
doi = {10.1145/3497777.3498547},
year = {2021},
date = {2021-12-14},
urldate = {2021-12-14},
booktitle = {16th Asian Internet Engineering Conference (AINTEC)},
address = {Online},
abstract = {Media access control (MAC) addresses inside Wi-Fi packets can be used for beneficial activities such as crowdedness estimation, marketing, and hazard maps. However, the MAC address randomization systems introduced around 2014 make all conventional MAC-address-based crowd monitoring systems count the same device more than once. Therefore, there is a need to create a new crowd monitoring system tolerant to MAC address randomization to estimate the number of devices accurately. In this paper, Vision and TrueSight, two new crowd monitoring algorithms that estimate the number of devices, are proposed to prove that MAC-address-based crowd monitoring is still possible. In addition to probe requests, Vision uses data packets and beacon packets to mitigate the influence of randomization. Moreover, TrueSight uses sequence numbers and hierarchical clustering to estimate the number of devices. The experimental results of this study show that even without installing any special software, Vision can gather 440 randomly generated MAC addresses into one group and count only once, and TrueSight can estimate the number of devices with an accuracy of more than 75% with an acceptable error range of 1.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Chen2021,
title = {Reinforcement Learning Based Optimal Camera Placement for Depth Observation of Indoor Scenes},
author = {Yichuan Chen and Manabu Tsukada and Hiroshi Esaki},
url = {https://arxiv.org/abs/2110.11106},
doi = {10.1109/ICNSC52481.2021.9702214},
year = {2021},
date = {2021-12-03},
urldate = {2021-12-03},
booktitle = {The 2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)},
address = {Xiamen, China},
abstract = {Exploring the most task-friendly camera setting---optimal camera placement (OCP) problem---in tasks that use multiple cameras is of great importance. However, few existing OCP solutions specialize in depth observation of indoor scenes, and most versatile solutions work offline. To this problem, an OCP online solution to depth observation of indoor scenes based on reinforcement learning is proposed in this paper. The proposed solution comprises a simulation environment that implements scene observation and reward estimation using shadow maps and an agent network containing a soft actor-critic (SAC)-based reinforcement learning backbone and a feature extractor to extract features from the observed point cloud layer-by-layer. Comparative experiments with two state-of-the-art optimization-based offline methods are conducted. The experimental results indicate that the proposed system outperforms seven out of ten test scenes in obtaining lower depth observation error. The total error in all test scenes is also less than 90% of the baseline ones. Therefore, the proposed system is more competent for depth camera placement in scenarios where there is no prior knowledge of the scenes or where a lower depth observation error is the main objective.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Hirata2021b,
title = {Roadside-assisted Cooperative Planning using Future Path Sharing for Autonomous Driving},
author = {Mai Hirata and Manabu Tsukada and Keisuke Okumura and Yasumasa Tamura and Hideya Ochiai and Xavier Défago},
url = {https://arxiv.org/abs/2108.04629
https://youtu.be/xaBIQC0SClE},
doi = {10.1109/VTC2021-Fall52928.2021.9625324},
year = {2021},
date = {2021-09-27},
urldate = {2021-09-27},
booktitle = {IEEE 94th Vehicular Technology Conference (VTC2021-Fall)},
address = {Online},
abstract = {Cooperative intelligent transportation systems (ITS) are used by autonomous vehicles to communicate with surrounding autonomous vehicles and roadside units (RSU). Current C-ITS applications focus primarily on real-time information sharing, such as cooperative perception. In addition to real-time information sharing, self-driving cars need to coordinate their action plans to achieve higher safety and efficiency. For this reason, this study defines a vehicle’s future action plan/path and designs a cooperative path-planning model at intersections using future path sharing based on the future path information of multiple vehicles. The notion is that when the RSU detects a potential conflict of vehicle paths or an acceleration opportunity according to the shared future paths, it will generate a coordinated path update that adjusts the speeds of the vehicles. We implemented the proposed method using the open-source Autoware autonomous driving software and evaluated it with the LGSVL autonomous vehicle simulator. We conducted simulation experiments with two vehicles at a blind intersection scenario, finding that each car can travel safely and more efficiently by planning a path that reflects the action plans of all vehicles involved. The time consumed by introducing the RSU is 23.0 % and 28.1 % shorter than that of the stand-alone autonomous driving case at the intersection.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Mizutani2021b,
title = {AutoMCM: Maneuver Coordination Service with Abstracted Functions for Autonomous Driving},
author = {Masaya Mizutani and Manabu Tsukada and Hiroshi Esaki},
url = {https://arxiv.org/abs/2107.06627
https://youtu.be/eC7L0R_1Ybo
https://youtu.be/s3l5zypizxQ
https://youtu.be/XBpZeT-apGE},
doi = {10.1109/ITSC48978.2021.9564556},
year = {2021},
date = {2021-09-19},
urldate = {2021-09-19},
booktitle = {24th IEEE International Conference on Intelligent Transportation (ITSC)},
address = {Indianapolis, IN, United States},
abstract = {A cooperative intelligent transport system (C-ITS) uses vehicle-to-everything (V2X) technology to make self-driving vehicles safer and more efficient. Current C-ITS applications have mainly focused on real-time information sharing, such as for cooperative perception. In addition to better real-time perception, self-driving vehicles need to achieve higher safety and efficiency by coordinating action plans. This study designs a maneuver coordination (MC) protocol that uses seven messages to cover various scenarios and an abstracted MC support service. We implement our proposal as AutoMCM by extending two open-source software tools: Autoware for autonomous driving and OpenC2X for C-ITS. The results show that our system effectively reduces the communication bandwidth by limiting message exchange in an event-driven manner. Furthermore, it shows that the vehicles run 15% faster when the vehicle speed is 30 km/h and 28% faster when the vehicle speed is 50 km/h using our scheme. Our system shows robustness against packet loss in experiments when the message timeout parameters are appropriately set.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tsukada2020,
title = {AutoC2X: Open-source software to realize V2X cooperative perception among autonomous vehicles},
author = {Manabu Tsukada and Takaharu Oi and Akihide Ito and Mai Hirata and Hiroshi Esaki},
url = {https://github.com/esakilab/AutoC2X-AW
https://hal.archives-ouvertes.fr/hal-02942051/document?.pdf
https://youtu.be/kyv0sTyCIgU},
doi = {10.1109/VTC2020-Fall49728.2020.9348525},
year = {2020},
date = {2020-11-18},
urldate = {2020-11-18},
booktitle = {The 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall)},
address = {Victoria, B.C., Canada},
abstract = {The realization of vehicle-to-everything (V2X) communication enhances the capabilities of autonomous vehicles in terms of safety efficiency and comfort. In particular, sensor data sharing, known as cooperative perception, is a crucial technique to accommodate vulnerable road users in a cooperative intelligent transport system (ITS). In this regard, open-source software plays a significant role in prototyping, validation, and deployment. Specifically, in the developer community, Autoware is a popular open-source software for self-driving vehicles, and OpenC2X is an open-source experimental and prototyping platform for cooperative ITS. This paper reports on a system named AutoC2X to enable cooperative perception by using OpenC2X for Autoware-based autonomous vehicles. The developed system is evaluated by conducting field experiments involving real hardware. The results demonstrate that AutoC2X can deliver the cooperative perception message within 100 ms in the worst case. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Inokuchi2020b,
title = {co-Sound: An interactive medium with WebAR and spatial synchronization },
author = {Kazuma Inokuchi and Manabu Tsukada and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-02942505/document?.pdf
https://youtu.be/Bn1yPlbgqaA},
doi = {10.1007/978-3-030-65736-9_22},
year = {2020},
date = {2020-11-10},
booktitle = {The 19th IFIP International Conference on Entertainment Computing (ICEC) 2020},
pages = {255-263},
publisher = {Springer, Cham},
address = {Xi'an, China},
abstract = {An Internet-based media service platform can control recording processes and manage video and audio data interconnected by an IP network. Furthermore, the design and implementation of an object-based system for recording enable the flexible playback of the viewing contents. Augmented Reality (AR) is a three-dimensional video projection technology that allows us to interact with both elements in real space and digital space information. However, there are few examples of its use as a method for audio-visual media platforms. In this study, we propose co-Sound, which is an interactive audio-visual playback application for music events, using WebAR. co-Sound was designed as a multimodal interface that dynamically renders object-based AR in response to various actions from viewers on a web browser with low entry costs. Furthermore, by sharing AR objects among multiple devices in real time and bidirectionally, the relationship between users and contents was extended, and interaction among multiple users in the same AR space was possible. We implemented a prototype application, measured the performance of the AR spatial synchronization, and conducted a questionnaire-based evaluation. For subjective evaluation, 25 people experienced co-Sound and completed a questionnaire. We confirmed that the system was developed by object-based method with AR, achieved low-latency synchronization to accept operations from multiple users in real time, and the general acceptance of the system was very high.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Kato2020,
title = {Web360^{2}: An Interactive Web Application for viewing 3D Audio-visual Contents},
author = {Shin Kato and Tomohiro Ikeda and Mitsuaki Kawamorita and Manabu Tsukada and Hiroshi Esaki},
url = {https://zenodo.org/record/3898664/files/SMCCIM_2020_paper_102.pdf
https://github.com/sdm-wg/web360square
https://youtu.be/qg7aGhzO2Nc},
doi = {10.5281/zenodo.3898664},
year = {2020},
date = {2020-06-25},
booktitle = {17th Sound and Music Computing Conference (SMC)},
pages = {32-39},
address = {Torino, Italy},
abstract = {The use of video streaming services is expanding, and currently accounts for the majority of downstream Internet traffic. With the availability of virtual reality (VR) services and 360-degree cameras for consumer use, 3D services are also gaining in popularity. In recent years, the technology supporting for 3D representation on the Web has advanced. Users can easily utilize this technology without installing dedicated applications. In this study, we design and implement a Web application, called “Web360$^2$,” which plays 360-degree video and object-based 3D sounds interactively on the Web. We also evaluated Web360$^2$ through a questionnaire survey.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tsukada2019b,
title = {Cooperative awareness using roadside unit networks in mixed traffic},
author = {Manabu Tsukada and Masahiro Kitazawa and Takaharu Oi and Hideya Ochiai and Hiroshi Esaki
},
url = {https://hal.archives-ouvertes.fr/hal-02335068/?.pdf},
doi = {10.1109/VNC48660.2019.9062773},
year = {2019},
date = {2019-12-04},
booktitle = {2019 IEEE Vehicular Networking Conference (VNC)},
pages = {9-16},
abstract = {Vehicle-to-vehicle (V2V) messaging is an indispensable component of connected autonomous vehicle systems. Although V2V standards have been specified by the European Union, United States, and Japan, the deployment phase represents mixed traffic in which connected and legacy vehicles co-exist. To enhance cooperative awareness in this mixed traffic, we assessed the special roadside unit that we developed in our previous work that generates required V2V messages on behalf of sensed target vehicles. In this paper, we extend our earlier work to propose a system called “Grid Proxy Cooperative Awareness Message to broaden the cooperative awareness message dissemination area by connecting infrastructure using high-speed roadside networks. To minimize delay in message delivery, we designed the proposed system to use edge computing. The proposed scheme delivers cooperative messages to a wider area with a low delay and a high packet delivery ratio by prioritizing packets by their respective safety contributions. Our simulation results indicate that the proposed scheme efficiently delivers messages in heavy road traffic conditions modeled on real maps of Tokyo and Paris. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Kasuya2019,
title = {LiVRation: Remote VR live platform with interactive 3D audio-visual service},
author = {Takashi Kasuya and Manabu Tsukada and Yu Komohara and Shigeki Takasaka and Takuhiro Mizuno and Yoshitaka Nomura and Yuta Ueda and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-02136247/document?.pdf
https://youtu.be/4MElM4_t2Io},
doi = {10.1109/GEM.2019.8811549},
year = {2019},
date = {2019-06-19},
booktitle = {IEEE Games Entertainment & Media Conference (IEEE GEM) 2019},
pages = {1-7},
address = {Yale University, New Haven, CT, U.S.},
abstract = {Of late, various audio-visual services based on the internet are being deployed extensively. Among these, object- based audio-visual services are attracting more attention. In 2014, we had established the software defined media (SDM) consortium to investigate object-based and internet-based audio- visual services. Despite the increasing demand and popularity of live concert events, the placement of the microphone and camera limit the free-viewpoint watching of the contents of package media, such as DVDs. In this study, we design and implement an interactive 3D audio-visual service system called LiVRation, with a free-view-listen point. For subjective evaluation, 211 people were made to experience LiVRation and answer a questionnaire, subsequently. In addition, we demonstrated the system in the ”Billboard Live Hackasong 2017” hosted by Billboard Japan and received the first prize, based on the votes of the judges as well as the audience.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Kitazawa2018,
title = {Wide transmission of Proxy Cooperative Awareness Message},
author = {Masahiro Kitazawa and Manabu Tsukada and Hideya Ochiai and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01879100/document?.pdf},
year = {2018},
date = {2018-06-10},
booktitle = {The Seventh International Conference on Advances in Vehicular Systems, Technologies and Applications (VEHICULAR 2018)},
pages = {54-59},
address = {Venice, Italy},
note = {Best Paper Award},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Azuma2018b,
title = {Improvement of False Positives in Misbehavoir Detection},
author = {Shuntaro Azuma and Manabu Tsukada and Kennya Sato},
url = {https://hal.archives-ouvertes.fr/hal-01879101/document?.pdf},
year = {2018},
date = {2018-06-10},
booktitle = {The Seventh International Conference on Advances in Vehicular Systems, Technologies and Applications (VEHICULAR 2018)},
pages = {78-83},
address = {Venice, Italy},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tsukada2017b,
title = {Roadside-Assisted V2V Messaging for Connected Autonomous Vehicle},
author = {Manabu Tsukada},
url = {https://hal.archives-ouvertes.fr/hal-01558066v2/document?.pdf},
year = {2017},
date = {2017-07-20},
booktitle = {The Thirteenth International Conference on Wireless and Mobile Communications (ICWMC 2017)},
pages = {89-94},
address = {Nice, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Azuma2017,
title = {A Method of Detecting Camouflage Data with Mutual Vehicle Position Monitoring},
author = {Shuntaro Azuma and Manabu Tsukada and Teruaki Nomura and Kenya Sato},
url = {https://hal.archives-ouvertes.fr/hal-01879103/document?.pdf},
year = {2017},
date = {2017-07-20},
booktitle = {The Sixth International Conference on Advances in Vehicular Systems, Technologies and Applications (VEHICULAR 2017)},
pages = {48-53},
address = {Nice, France},
note = {Best Paper Award},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tao2017b,
title = {Positioning and Perception in cooperative ITS application simulator},
author = {Ye Tao and Manabu Tsukada and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01879102/document?.pdf},
year = {2017},
date = {2017-07-20},
booktitle = {The Sixth International Conference on Advances in Vehicular Systems, Technologies and Applications (VEHICULAR 2017)},
pages = {54-59},
address = {Nice, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Kitazawa2017,
title = {Remote Proxy V2V Messaging using IPv6 and GeoNetworking},
author = {Masahiro Kitazawa and Manabu Tsukada and Kai Morino and Hideya Ochiai and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01578410/document?.pdf},
year = {2017},
date = {2017-07-10},
booktitle = {The Sixth International Conference on Advances in Vehicular Systems, Technologies and Applications (VEHICULAR 2017)},
pages = {74-80},
address = {Nice, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tsukada2017c,
title = {Software Defined Media: Virtualization of Audio-Visual Services},
author = {Manabu Tsukada and Keiko Ogawa and Masahiro Ikeda and Takuro Sone and Kenta Niwa and Shoichiro Saito and Takashi Kasuya and Hideki Sunahara and Hiroshi Esaki},
url = {https://arxiv.org/pdf/1702.07452.pdf},
doi = {10.1109/ICC.2017.7996610},
isbn = {1938-1883},
year = {2017},
date = {2017-05-21},
booktitle = {IEEE International Conference on Communications (ICC2017)},
pages = {1-7},
address = {Paris, France},
abstract = {Internet-native audio-visual services are witnessing rapid development. Among these services, object-based audio-visual services are gaining importance. In 2014, we established the Software Defined Media (SDM) consortium to target new research areas and markets involving object-based digital media and Internet-by-design audio-visual environments. In this paper, we introduce the SDM architecture that virtualizes networked audio-visual services along with the development of smart buildings and smart cities using Internet of Things (IoT) devices and smart building facilities. Moreover, we design the SDM architecture as a layered architecture to promote the development of innovative applications on the basis of rapid advancements in software-defined networking (SDN). Then, we implement a prototype system based on the architecture, present the system at an exhibition, and provide it as an SDM API to application developers at hackathons. Various types of applications are developed using the API at these events. An evaluation of SDM API access shows that the prototype SDM platform effectively provides 3D audio reproducibility and interactiveness for SDM applications.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Kitazato2016,
title = {Proxy Cooperative Awareness Message: An Infrastructure-Assisted V2V Messaging},
author = {Tomoya Kitazato and Manabu Tsukada and Hideya Ochiai and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01341850/document?.pdf},
doi = {10.1109/ICMU.2016.7742092},
year = {2016},
date = {2016-10-04},
booktitle = {The Ninth International Conference on Mobile Computing and Ubiquitous Networking (ICMU2016)},
address = {DFKI Kaiserslautern, Kaiserslautern, Germany},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Ikeda2016,
title = {New Recording Application for Software Defined Media},
author = {Masahiro Ikeda and Takuro Sone and Kenta Niwa and Shoichiro Saito and Manabu Tsukada and Hiroshi Esaki},
url = {http://www.aes.org/e-lib/browse.cfm?elib=18414},
year = {2016},
date = {2016-09-10},
booktitle = {Audio Engineering Society Convention Paper, 141st AES Convention},
address = {Los Angeles, USA},
abstract = {In recent years, hardware-based systems are becoming software-based and networked. From IP based media networks, the notion of Software Defined Media (SDM) has arisen. SDM is an architectural approach to media as a service by virtualization and abstraction of networked infrastructure. With this approach, it would be possible to provide more flexible and versatile systems. To test this concept, a baroque orchestra was recorded by various methods with 82 channels of microphones in total. All the data was organized based on the object-based concept and we applied advanced signal processing to the data based on array signal processing technology to produce a content matching various purposes of possible applications. Through this study, the value of SDM concept is verified.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tao2016,
title = {DUPE: Duplicated Unicast Packet Encapsulation in Position-Based Routing VANET},
author = {Ye Tao and Xin Li and Manabu Tsukada and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01321553/document?.pdf},
doi = {10.1109/WMNC.2016.7543979},
year = {2016},
date = {2016-07-20},
booktitle = {9th IFIP Wireless and Mobile Networking Conference (WMNC 2016)},
address = {Colmar, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Ikegami2015,
title = {Electric Current Based Power Line Communication for Plug-Load Device Auto Identification},
author = {Hiroyuki Ikegami and Manabu Tsukada and Hideya Ochiai and Hideaki Nii and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01317105/document?.pdf},
doi = {10.1109/SmartGridComm.2015.7436287},
year = {2015},
date = {2015-09-20},
booktitle = {IEEE International Conference on Smart Grid Communications (SmartGridComm 2015)},
address = {Miami, United States},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tao2015,
title = {Reproducing and Extending Real Testbed Evaluation of GeoNetworking Implementation in Simulated Networks},
author = {Ye Tao and Manabu Tsukada and Xin Li and Masatoshi Kakiuchi and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01317104/document?.pdf},
doi = {10.1145/2775088.2775092},
year = {2015},
date = {2015-06-20},
booktitle = {The 10th International Conference on Future Internet Technologies (CFI 2015)},
address = {Seoul, Korea},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tsukada2014b,
title = {AnaVANET: an experiment and visualization tool for vehicular networks},
author = {Manabu Tsukada and José Santa and Satoshi Matsuura and Thierry Ernst and Kazutoshi Fujikawa},
url = {https://hal.inria.fr/hal-00983479/document?.pdf
https://youtu.be/NamJUd-_0jw},
doi = {10.1007/978-3-319-13326-3_13},
year = {2014},
date = {2014-05-20},
booktitle = {9th International Conference on Testbeds and Research Infrastructures for the Development of Networks & Communities (TRIDENTCOM 2014)},
address = {Guangzhou, China},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Noguchi2011,
title = {Location-aware service discovery on IPv6 GeoNetworking for VANET},
author = {Satoru Noguchi and Manabu Tsukada and Thierry Ernst and Atsuo Inomata and Kazutoshi Fujikawa},
url = {https://hal.inria.fr/inria-00625796/document?.pdf},
doi = {10.1109/ITST.2011.6060058},
year = {2011},
date = {2011-08-20},
booktitle = {11th International Conference on Intelligent Transport System Telecommunications (ITST 2011)},
address = {Saint-Petersburg, Russia},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Toukabri2011,
title = {Experimental evaluation of an open source implementation of IPv6 GeoNetworking in VANETs},
author = {Thouraya Toukabri and Manabu Tsukada and Thierry Ernst and Lamjed Bettaieb},
url = {https://hal.inria.fr/inria-00625789/document?.pdf},
doi = {10.1109/ITST.2011.6060060},
year = {2011},
date = {2011-08-20},
booktitle = {11th International Conference on Intelligent Transport System Telecommunications (ITST 2011)},
address = {Saint-Petersburg, Russia},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Noguchi2011b,
title = {Real-vehicle integration of driver support application with IPv6 GeoNetworking},
author = {Satoru Noguchi and Manabu Tsukada and Ines Ben Jemaa and Thierry Ernst},
url = {https://hal.inria.fr/inria-00567852/document?.pdf},
doi = {10.1109/VETECS.2011.5956756},
year = {2011},
date = {2011-05-20},
booktitle = {2011 IEEE 73rd Vehicular Technology Conference (VTC2011-Spring)},
address = {Budapest, Hungary},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Jemaa2010,
title = {Validation and evaluation of NEMO in VANET using geographic routing},
author = {Ines Ben Jemaa and Manabu Tsukada and Hamid Menouar and Thierry Ernst},
url = {https://hal.inria.fr/inria-00567786/document?.pdf},
year = {2010},
date = {2010-11-20},
booktitle = {10th International Conference on Intelligent Transport System Telecommunications (ITST 2010)},
address = {Kyoto, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tsukada2010b,
title = {Experimental Evaluation for IPv6 over VANET Geographic routing},
author = {Manabu Tsukada and Ines Ben Jemaa and Hamid Menouar and Wenhui Zhang and Maria Goleva and Thierry Ernst},
url = {https://hal.inria.fr/inria-00505921/document?.pdf
https://youtu.be/MFo12Nxik94},
doi = {10.1145/1815396.1815565},
year = {2010},
date = {2010-06-10},
booktitle = {6th International Wireless Communications and Mobile Computing Conference, IWCMC 2010},
address = {Caen, France},
note = {Best Paper Award},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Khaled2009b,
title = {Geographical information extension for IPv6: application to VANET},
author = {Yacine Khaled and Manabu Tsukada and Thierry Ernst},
url = {https://hal.inria.fr/inria-00567786/document?.pdf},
doi = {10.1109/ITST.2009.5399339},
year = {2009},
date = {2009-10-20},
booktitle = {9th International Conference on Intelligent Transport System Telecommunications (ITST 2009)},
address = {Lille, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Khaled2009c,
title = {Application of IPv6 multicast to VANET},
author = {Yacine Khaled and Ines Ben Jemaa and Manabu Tsukada and Thierry Ernst},
url = {https://ieeexplore.ieee.org/document/5399356/},
doi = {10.1109/ITST.2009.5399356},
year = {2009},
date = {2009-10-20},
booktitle = {9th International Conference on Intelligent Transport System Telecommunications (ITST 2009)},
address = {Lille, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Khaled2009d,
title = {On the Design of efficient Vehicular Applications},
author = {Yacine Khaled and Manabu Tsukada and José Santa and Thierry Ernst.},
url = {https://hal.inria.fr/inria-00355878/document?.pdf},
doi = {10.1109/VETECS.2009.5073727},
year = {2009},
date = {2009-04-06},
booktitle = {2009 IEEE 69th Vehicular Technology Conference (VTC2009-Spring)},
address = {Barcelona, Spain},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Choi2008,
title = {IPv6 support for VANET with geographical routing},
author = {JinHyeock Choi and Yacine Khaled and Manabu Tsukada and Thierry Ernst},
url = {https://hal.inria.fr/inria-00336450/document?.pdf},
doi = {10.1109/ITST.2008.4740261},
year = {2008},
date = {2008-10-22},
booktitle = {8th International Conference on Intelligent Transport System Telecommunications (ITST 2008)},
journal = {8th International Conference on Intelligent Transport System Telecommunications (ITST 2008)},
address = {Phuket, Thailand},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Montavont2008,
title = {Anemone: A ready-to-go testbed for IPv6 compliant Intelligent Transport Systems},
author = {Nicolas Montavont and Antoine Boutet and Tanguy Ropitault and Manabu Tsukada and Thierry Ernst and Jari Korva and Cesar Viho and Laszlo Bokor},
url = {https://ieeexplore.ieee.org/document/4740262/},
doi = {10.1109/ITST.2008.4740262},
year = {2008},
date = {2008-10-22},
booktitle = {8th International Conference on Intelligent Transport System Telecommunications (ITST 2008)},
address = {Phuket, Thailand},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Dhraief2007,
title = {E-Bicycle Demonstration on the Tour De France},
author = {Amine Dhraief and Nicolas Montavont and Romain Kuntz and Manabu Tsukada},
doi = {10.1109/ICCGI.2007.23},
year = {2007},
date = {2007-03-04},
booktitle = {International Multi-Conference on Computing in the Global Information Technology (ICCGI '07)},
address = {Guadeloupe, French Caribbean},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tsukada2005,
title = {Dynamic Management of Multiple Mobile Routers},
author = {Manabu Tsukada and Thierry Ernst and Ryuji Wakikawa and Koshiro Mitsuya},
url = {https://www.nautilus6.org/doc/paper/20051116-ICON-NEMO-MMRM-ManabuT.pdf
https://youtu.be/fcKOUsYC6ro},
doi = {10.1109/ICON.2005.1635682},
year = {2005},
date = {2005-11-20},
booktitle = {IEEE Malaysia International Conference on Communications and IEEE International Conference on Networks (MICC & ICON 2005)},
volume = {2},
pages = {1108-1113},
address = {Kuala Lumpur, Malaysia},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@workshop{Hu2025,
title = {A Low PAPR Layered Multi-User OTFS Modulation},
author = {Dou Hu and Jin Nakazato and Kazuki Maruta and Omid Abbassi Aghda and Rui Dinis and Manabu Tsukada},
year = {2025},
date = {2025-06-17},
urldate = {2025-06-17},
booktitle = {AI-Driven Connectivity for Vehicular and Wireless Networks in VTC2025-Spring},
address = {Oslo, Norway},
abstract = {In modern communication systems, meeting the growing demand for high-capacity transmission requires developing efficient and robust modulation techniques. To address
this, we propose a low-PAPR page-style Orthogonal Time Frequency Space (OTFS) modulation framework that enhances communication capacity while maintaining a low peak-to-average power ratio (PAPR). The proposed design introduces a novel pilot signal placement and analysis method, improving channel estimation accuracy and system performance in high-mobility multi-user scenarios. This paper provides an overview of recent advancements in OTFS-based multi-user communication systems, emphasizing their contributions to enhancing spectral efficiency, reliability, and robustness. Through extensive simulations, we demonstrate the effectiveness of the proposed framework in achieving superior BER performance, improved interference mitigation, and robust transmission capabilities compared to traditional methods, validating its suitability for next-generation communication networks.},
howpublished = {Workshop on AI-Driven Connectivity for Vehicular and Wireless Networks in VTC2025-Spring},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@workshop{Ozawa2024b,
title = {Toward O-RAN-based Cell-Free Architecture: Cooperative O-RU/V2X mmWave Beam Tracking},
author = {Sojin Ozawa and Yuki Sasaki and Jin Nakazato and Manabu Tsukada and Kazuki Maruta},
year = {2024},
date = {2024-06-24},
urldate = {2024-06-24},
booktitle = {Technologies and Proof-of-Concept Activities for 6G 2024 (TPoC6G 2024) at The IEEE 99th Vehicular Technology Conference (VTC2024-Spring)},
address = {Singapore},
abstract = {Coordination of connected autonomated vehicles (CAVs) is expected to provide a more efficient sequential route design and enhance safety. This is accomplished by sharing sensor data among roadside equipment and other vehicles. Given the substantial volume of sensor data involved, it is advantageous to employ millimeter-wave (mmWave) band. MmWave offers high-speed and large-capacity for transmission. However, wireless communication systems designed for CAVs face the challenge of radio signal degradation caused by the movement of vehicles. This paper proposes a fast beam tracking Open Radio Access Network (O-RAN) architecture for CAVs. The most prominent aspect of this system is its Near-Realtime (Near-RT) RAN Intelligent Controller (RIC), which swiftly adjusts and tracks the beam using vehicle information transmitted every 100 msec by CAV. By conducting simulations using Simulation of Urban Mobility (SUMO), which emulates vehicle movement on various roads, we verified the effective operation of the proposed architechture.
},
note = {IEEE VTS Tokyo/Japan Chapter Young Researcher's Encouragement Award},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@workshop{Suzuki2024,
title = {Toward B5G/6G Connected Autonomous Vehicles: O-RAN-Driven Millimeter-Wave Beam Management and Handover Management},
author = {Kengo Suzuki and Jin Nakazato and Yuki Sasaki and Kazuki Maruta and Manabu Tsukada and Hiroshi Esaki},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/378439256_Toward_B5G6G_Connected_Autonomous_Vehicles_O-RAN-Driven_Millimeter-Wave_Beam_Management_and_Handover_Management/links/664b976f0b0d2845744b1ea8/Toward-B5G-6G-Connected-Autonomous-Vehicles-O-RAN-Driven-Millimeter-Wave-Beam-Management-and-Handover-Management.pdf},
year = {2024},
date = {2024-05-20},
urldate = {2024-05-20},
booktitle = {IEEE INFOCOM WKSHPS, Next-generation Open and Programmable Radio Access Networks (NG-OPERA 2024)},
address = {Vancouver, Canada},
abstract = {Connected autonomous vehicles (CAVs) are crucial to a future society that embraces advanced technologies. For these vehicles to effectively share information with nearby vehicles or infrastructures through vehicle-to-everything (V2X) interfaces, stable mmWave communication is essential yet challenging. This paper presents an innovative approach to millimeter-wave beam management for CAVs, utilizing open radio access network (O- RAN) architecture to improve beam and handover efficiency in diverse road scenarios. Our approach uniquely combines CAV application data with mobility management to predict vehicles’ location. Our findings show that this method substantially surpasses the traditional beam sweeping method by consistently maintaining a higher signal-to-noise ratio (SNR), even in challenging scenarios such as vehicle obstruction at intersections. This research underscores the potential of integrating O-RAN with vehicle-to-infrastructure (V2I) communication in CAVs, paving the way for future advancements in autonomous transportation technology.},
note = {Best Paper Award},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@workshop{Hu2024,
title = {A Research of Kalman Filter enabled Beam Tracking for Multiple Vehicles},
author = {Dou Hu and Jin Nakazato and Ehsan Javanmardi and Muhammad Asad and Maruta Kazuki and Manabu Tsukada},
url = {https://www.ieice.org/publications/proceedings/bin/pdf_link.php?fname=15.pdf&iconf=ASPIRE_WS&year=2024&vol=80&number=P-15&lang=E?.pdf},
doi = {10.34385/proc.80.P-15},
isbn = {2188-5079},
year = {2024},
date = {2024-03-05},
urldate = {2024-03-05},
booktitle = {ASPIRE Workshop 2024 in conjunction with the IEICE General Conference},
address = {Hiroshima, Japan},
abstract = {In the era of Beyond 5G, the significance of interdisciplinary research has become increasingly important. Within this context, the Kalman filter, a technology integral to self-positioning estimation in autonomous driving, is already being adopted in various societal applications. This study proposes a method wherein beam tracking, in conjunction with the Kalman filter, is an alternative to GPS in specific scenarios. This research is particularly relevant in environments such as intersections flanked by high-rise buildings, where GPS signals are prone to interference.
},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@workshop{Sasaki2024b,
title = {Beam-Space Expansion with Broad-Range Null-Steering for V2I Multiuser MIMO Transmission},
author = {Yuki Sasaki and Sojin Ozawa and Kabuto Arai and Jin Nakazato and Manabu Tsukada and Kazuki Maruta},
url = {https://www.ieice.org/publications/proceedings/bin/pdf_link.php?fname=8.pdf&iconf=ASPIRE_WS&year=2024&vol=80&number=P-8&lang=E?.pdf},
doi = {10.34385/proc.80.P-8},
isbn = {2188-5079},
year = {2024},
date = {2024-03-05},
urldate = {2024-03-05},
booktitle = {ASPIRE Workshop 2024 in conjunction with the IEICE General Conference},
address = {Hiroshima, Japan},
abstract = {This paper proposes a novel inter-user interference (IUI) suppression approach in multiuser MIMO (MU-MIMO) with time-varying channel environments. It introduces location based beam-space expansion (BSE) scheme for vehicle-to-infrastructure (V2I) MU-MIMO transmission. This scheme expands the main lobe of the beam based on vehicle location information. MU-MIMO enables to enhance spectral efficiency in V2I by multiplexing a number of user terminals (UTs) in spatial domain. However, UTs move at high speed, which causes IUI. Null-space expansion (NSE) and broad-range null-steering (BRNS) improves IUI suppression capability by only steering nulls to interfering users. The proposed BSE expands the beam for the desired user. The performance of NSE, BRNS and BSE are compared through computer simulation to show the effectiveness of BSE.},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@workshop{Ozawa2024,
title = {Laboratory Experiment of Broad-Range Null-Steering for Millimeter-Wave V2I Multiuser MIMO},
author = {Sojin Ozawa and Yuki Sasaki and Ryo Iwaki and Jin Nakazato and Manabu Tsukada and Kazuki Maruta
},
url = {https://www.ieice.org/publications/proceedings/bin/pdf_link.php?fname=19.pdf&iconf=ASPIRE_WS&year=2024&vol=80&number=P-19&lang=E?.pdf},
doi = {10.34385/proc.80.P-19},
issn = {2188-5079},
year = {2024},
date = {2024-03-05},
urldate = {2024-03-05},
booktitle = {ASPIRE Workshop 2024 in conjunction with the IEICE General Conference},
address = {Hiroshima, Japan},
abstract = {This paper demonstrates the practical effectiveness of our proposed broad-range null-steering (BRNS) scheme through indoor experiment using 28-GHz band 4-element array antenna transmission system.For realization of cooperative automated driving, use of millimeter-wave band and multiuser multiple-input multiple-output (MIMO) are essential. However, inter-user interference is an challenging issue to perform spatial multiplexing under mobility environment due to outdated channel state information (CSI). BRNS was previously proposed that additionally nullifies around interfering users based on geometrical information of vehicles that can be obtained through cooperative awareness message (CAM). Its effectiveness have been confirmed through computer simulations, supposing cooperative automated operation in V2X. Our laboratory-experimental result confirms null-steering range can be expanded.},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@workshop{Bao2023b,
title = {Towards a Trusted Inter-Reality: Exploring System Architectures for Digital Identification},
author = {Naren Bao and Jin Nakazato and Muhammad Asad and Ehsan Javanmardi and Manabu Tsukada},
doi = {10.1145/3627050.3631566},
year = {2023},
date = {2023-11-07},
urldate = {2023-11-07},
booktitle = {The 1st International Workshop on Internet of Realities (IoR-WS 2023) at International Conference on the Internet of Things},
address = {Nagoya, Japan},
abstract = {The concept of a trusted inter-reality, where physical and virtual worlds seamlessly converge, represents a paradigm shift in how digital identities are formed and managed. This paper explores the complex landscape of system architectures designed to enable secure and user-centric digital identification within interconnected realities. Our survey focuses on user-centric security, recognizing the prevalence of wearable devices and immersive technologies in inter-reality environments. We advocate for user-friendly authentication methods and privacy-preserving techniques that prioritize user control within the trust model. Furthermore, we delve into the influence of social and cultural factors, particularly age and gender, on the shaping of digital identity within interconnected realities. We argue in favor of adaptable system architectures that respect generational and gender diversity. In conclusion, we emphasize the alignment of system architectures with these principles to promote a secure, user-centric, and culturally sensitive digital identity experience. This research contributes to the ongoing discourse on digital identification in interconnected realities, providing actionable guidance for stakeholders in the evolving landscape of trusted inter-reality.
},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@workshop{Sugizaki2023b,
title = {Umpire Assistance System in Baseball Game},
author = {Yusuke Sugizaki and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
doi = {10.1145/3627050.3631569},
year = {2023},
date = {2023-11-07},
urldate = {2023-11-07},
booktitle = {The 1st International Workshop on Internet of Realities (IoR-WS 2023) at International Conference on the Internet of Things},
address = {Nagoya, Japan},
abstract = {In recent years, information technology has become an important element in the pursuit of a more prosperous society, and this extends to the realm of sports. Events across various domains are undergo- ing digitalization. Within the context of baseball, the introduction of automated technology for umpiring is garnering significant at- tention. Conversely, the frequent misjudgments by human referees have become a contentious issue. Both Major League Baseball (MLB) and Nippon Professional Baseball (NPB) have introduced a chal- lenge system that requires replay verification in instances where there’s disagreement with an umpire’s decision. However, this sys- tem is not permissible in venues lacking the necessary facilities. A consequential shortage of umpires intensifies their workload, potentially leading to more misjudgments. This paper proposes an architecture for an umpire assistance system designed to ad- dress these challenges in baseball games. Our proposed system architecture facilitates the filming of a baseball game from multiple angles, rendering the challenge system independent of the venue’s infrastructure. Moreover, the system autonomously makes judg- ments using footage from multiple cameras, thereby supporting both human umpires and automated officiating. Looking ahead, we also discuss the potential advancements when integrating this with digital twins and explore its applicability to other sports.},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@workshop{Cheng2023,
title = {Pedestrian-centric Augmented Reality Visualization of Real-time Autonomous Vehicle Dynamics},
author = {Yiwei Cheng and Jin Nakazato and Ehsan Javanmardi and Chia-Ming Chang and Manabu Tsukada},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/374387897_Pedestrian-centric_Augmented_Reality_Visualization_of_Real-time_Autonomous_Vehicle_Dynamics/links/651bda961e2386049df3c4ee/Pedestrian-centric-Augmented-Reality-Visualization-of-Real-time-Autonomous-Vehicle-Dynamics.pdf},
doi = {10.1109/CloudNet59005.2023.10490048},
year = {2023},
date = {2023-11-04},
urldate = {2023-11-04},
booktitle = {The Workshop on Intelligent Cloud Continuum for B5G Services in the IEEE International Conference on Cloud Networking (CloudNet) 2023},
address = {New York City, USA},
abstract = {Connected Autonomous Vehicles (CAVs) produce a variety of information within their systems. With the advancement of communication and V2X (Vehicle-to-Everything) communication technology, there is a growing challenge to effectively convey this information to pedestrians and enhance their sense of safety when encountering such vehicles. Efforts to communicate this information to pedestrians have been made through various means, with Augmented Reality (AR) emerging as a notable approach. However, previous studies have yet to integrate a functional AR application with a real-world autonomous driving system. In response to this gap, we proposed an architecture for an AR application that visualizes real-time data from an active CAV and subsequently developed the system. Furthermore, we conducted field experiments using this developed system and conducted user surveys during exhibitions to gather insights into the public’s perception of the system. Our results showed that the system can effectively transmit information from the CAV, and when provided with additional information, people tend to feel safer regarding the vehicle. },
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@workshop{Mizutani2020,
title = {3D maps distribution of self-driving vehicles using roadside edges},
author = {Masaya Mizutani and Manabu Tsukada and Yuki Iida and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-02997520/document?.pdf
https://youtu.be/JBuX3JKDVjA},
doi = {10.1109/CANDARW51189.2020.00021},
year = {2020},
date = {2020-11-25},
urldate = {2020-11-25},
booktitle = {2020 Eighth International Symposium on Computing and Networking Workshops (CANDARW)},
pages = {40-45},
publisher = {IEEE},
address = {Okinawa, Japan},
abstract = {Three-dimensional (3D) maps have become a shared digital infrastructure for autonomous vehicles, especially in urban areas. Point Cloud Data (PCD) maps are used for scan matching to enable self-localization. Autonomous vehicles need to maintain PCD maps along with the destination that is often decided on demand and to keep the PCD map updated. In this paper, we propose a system that delivers PCD maps cached at roadside edges in real time. We implement the system in Autoware, open-source software for autonomous driving. Subsequently, we evaluate whether the autonomous vehicle can simultaneously download the PCD map from its edge and enable self-localization. Our results show that autonomous vehicles can perform self-localization while downloading the PCD map from the edge server. Additionally, we measure the download time with variable bandwidth and examine the bandwidth in which the self-localization normally operates. In our results, the download time of the PCD map at 100 Mbps was 698 ms at maximum, and the possibility of downloading PCD maps through 4G communication is shown.},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@workshop{Atarashi2018,
title = {The Software Defined Media Ontology for Music Events},
author = {Ray Atarashi and Takuro Sone and Yu Komohara and Manabu Tsukada and Takashi Kasuya and Hiraku Okumura and Masahiro Ikeda and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01879099/document?.pdf},
doi = {10.1145/3243907.3243915},
year = {2018},
date = {2018-10-08},
urldate = {2018-10-08},
booktitle = {Workshop on Semantic Applications for Audio and Music (SAAM) held in conjunction with ISWC 2018},
pages = {15-23},
address = {Monterey, California, USA.},
abstract = {With the advent of viewing services based on the Internet, the importance of object-based viewing services for interpreting objects existing in space and utilizing them as the content is increasing. Since 2014, the Software Defined Media Consortium has been researching object-based media and Internet-based viewing spaces. This paper defines a framework in event participants and professional recorders each freely share recorded data, and a third party can create an application based on the data. This study aims to provide an SDM ontology-based contents management mechanism with a detailed description of the object-based audio and video data and the recording environment. The data can be shared via the Internet and is highly reusable. We implemented this management mechanism and have developed and validated applications that are capable of interactively playing 3D content from any viewpoints freely.},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@workshop{Shagdar2012,
title = {Experimentation Towards IPv6 over IEEE 802.11p with ITS Station Architecture},
author = {Oyunchimeg Shagdar and Manabu Tsukada and Kakiuchi Masatoshi and Thouraya Toukabri and Thierry Ernst},
url = {https://hal.inria.fr/hal-00702923/document?.pdf},
year = {2012},
date = {2012-06-20},
urldate = {2012-06-20},
booktitle = {International Workshop on IPv6-based Vehicular Networks (Vehi6 2012) colocated with IEEE Intelligent Vehicles Symposium},
address = {Madrid, Spain},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@workshop{Santa2009b,
title = {Experimental Analysis of Multi-hop Routing in Vehicular Ad-hoc Networks},
author = {José Santa and Manabu Tsukada and Thierry Ernst and Antonio F. Gómez-Skarmeta},
url = {https://hal.inria.fr/inria-00625645/document?.pdf},
doi = {10.1109/TRIDENTCOM.2009.4976248},
year = {2009},
date = {2009-04-06},
urldate = {2009-04-06},
booktitle = {2nd Workshop on Experimental Evaluation and Deployment Experiences on Vehicular networks (WEEDEV 2009) in conjunction with TRIDENTCOM 2009},
journal = {2nd Workshop on Experimental Evaluation and Deployment Experiences on Vehicular networks (WEEDEV 2009) in conjunction with TRIDENTCOM 2009},
address = {Washington D.C., USA},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@workshop{Tsukada2008,
title = {Simultaneous Usage of NEMO and MANET for Vehicular Communication},
author = {Manabu Tsukada and Olivier Mehani and Thierry Ernst},
url = {https://hal.inria.fr/inria-00265652/document?.pdf
https://youtu.be/LtFrE8Ezho0},
doi = {10.4108/weedev.2008.3118},
year = {2008},
date = {2008-03-18},
urldate = {2008-03-18},
booktitle = {1st Workshop on Experimental Evaluation and Deployment Experiences on Vehicular networks (WEEDEV 2008) conjunction with TRIDENTCOM 2008},
address = {Innsbruck, Austria},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@workshop{Tsukada2007,
title = {Vehicle Communication Experiment Environment With MANET And NEMO},
author = {Manabu Tsukada and Thierry Ernst},
doi = {doi:10.1109/SAINT-W.2007.104},
year = {2007},
date = {2007-01-15},
urldate = {2007-01-15},
booktitle = {The 2007 International Symposium on Applications and the Internet, Workshop on Network Mobility (SAINT WONEMO 2007)},
address = {Hiroshima, Japan},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@misc{Orsholits2025b,
title = {Edge Vision AI Co-Processing for Dynamic Context Awareness in Mixed Reality},
author = {Alex Orsholits and Manabu Tsukada},
url = {https://www.youtube.com/watch?v=xxahKZl4K9w
https://ieeevr.org/2025/awards/conference-awards/#poster-honorable},
doi = {10.1109/VRW66409.2025.00293},
year = {2025},
date = {2025-03-08},
urldate = {2025-03-08},
booktitle = {2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)},
address = {Saint-Malo, France},
abstract = {Spatial computing is evolving towards leveraging data streaming for computationally demanding applications, facilitating a shift to lightweight, untethered, and standalone devices. These devices are ideal candidates for co-processing, where real-time scene context understanding and low-latency data streaming are fundamental for general-purpose Mixed Reality (MR) experiences. This poster demonstrates and evaluates a scalable approach to augmented contextual understanding in MR by implementing edge AI co-processing through a Hailo-8 AI accelerator, a low-power ARM-based single board computer (SBC), and the Magic Leap 2 AR headset. The resulting inferences are streamed back to the headset for spatial reprojection into the user’s vision.},
howpublished = {IEEE VR 2025, Poster},
note = {Honorable mention},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
@misc{Nakazato2025,
title = {Toward 6G Mobility Network: Design of a Wireless Digital Twin for Connected Autonomous Vehicle},
author = {Jin Nakazato and Tetsuya Iye and Yuki Susukida and Eisaku Sato and Yuki Sasaki and Kazuki Maruta and Manabu Tsukada
},
year = {2025},
date = {2025-01-10},
pages = {1-2},
howpublished = {2025 IEEE 22nd Consumer Communications & Networking Conference (CCNC), Poster},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
@misc{Chauhan2024c,
title = {Reimagining Smart Poles: A Sustainable Interface for Pedestrian-AV Ecosystems},
author = {Vishal Chauhan and Anubhav and Megha Sharma and Manabu Tsukada},
year = {2024},
date = {2024-12-03},
urldate = {2024-12-03},
address = {Brisbane, Australia},
howpublished = {OzCHI Student Design Challenge (Poster, Video)},
note = {Finalists Award},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
@misc{Sasaki2024,
title = {Location-Based Broad-Range Null-Steering in V2X Multiuser MIMO Transmission},
author = {Yuki Sasaki and Sojin Ozawa and Kabuto Arai and Jin Nakazato and Manabu Tsukada and Kazuki Maruta },
url = {https://tlab.hongo.wide.ad.jp/papers/2023_CCNC2023_poster_Sasaki.pdf},
year = {2024},
date = {2024-01-06},
urldate = {2024-01-06},
address = {Las Vegas, NV, USA},
abstract = {This paper proposes an intensive null-steering around the target in angular domain to effectively suppress inter-user interference (IUI) leakage caused by channel varying environment such as vehicular multiuser spatial multiplexing. Multiuser MIMO can enhance spectral efficiency by multiplexing a number of user terminals in spatial domain. Suppose applying multiuser MIMO downlink in vehicle-to-everything (V2X) scenario, vehicles move at high speed which causes IUI. Null-space expansion has been conceived that can improve IUI suppression capability by steering nulls to the past and the present channel states on interfered users. Collective perception in intelligent transport systems (ITS) provides location information of vehicles every 100 ms. Exploiting this feature, this paper proposes angular-domain null-space expansion; broad-range null-steering (BRNS). Computer simulation verifies its effectiveness.},
howpublished = {2024 IEEE 21st Consumer Communications & Networking Conference (CCNC), Poster},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
@misc{Iwaki2024,
title = {Optimizing mmWave Beamforming for High-Speed Connected Autonomous Vehicles: An Adaptive Approach},
author = {Ryo Iwaki and Jin Nakazato and Muhammad Asad and Ehsan Javanmardi and Kazuki Maruta and Manabu Tsukada and Hideya Ochiai and Hiroshi Esaki},
url = {https://tlab.hongo.wide.ad.jp/papers/2023_CCNC2023_poster_Iwaki.pdf},
year = {2024},
date = {2024-01-06},
urldate = {2024-01-06},
abstract = {The commercialization of 5G has been initiated for a while. Furthermore, millimeter wave (mmWave) has been introduced to small cells with small coverage due to its strong linearity and non-winding characteristics. On the other hand, in connected autonomous vehicles (CAVs), where various traffic systems can cooperatively perform recognition, decision-making, and execution, communication is assumed to be always connected. Therefore, to use low latency mmWave for high-speed moving CAV, beamforming in 5G cannot follow them at high speed. This paper proposes an improved beam tracking algorithm for high-speed CAVs, which can be evaluated in a more general environment using a traffic simulator. We proposed an adaptive algorithm for a general road environment by increasing the number of beam searches and search dimensions.},
howpublished = {2024 IEEE 21st Consumer Communications & Networking Conference (CCNC), Poster},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
@misc{Yamamoto2023b,
title = {Evaluation of IPv6-only-Capable Iterative Resolvers},
author = {Momoka Yamamoto and Jin Nakazato and Romain Fontugne and Manabu Tsukada and Hiroshi Esaki},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/373512394_Evaluation_of_IPv6-only-Capable_Iterative_Resolvers/links/64ef62530f7ab20a8666c879/Evaluation-of-IPv6-only-Capable-Iterative-Resolvers.pdf},
doi = {10.1145/3603269.3610850},
year = {2023},
date = {2023-09-10},
urldate = {2023-09-10},
pages = {1102-1104},
address = {New York City, USA},
abstract = {This paper introduces an "IPv6-only-Capable resolver" to address the issue of many zones remaining unresolvable due to a lack of IPv6 connectivity in authoritative name servers. The proposed method utilizes NAT64 to transmit packets to IPv4-only authoritative name servers and increases resolution success rates with competitive response times compared to a traditional IPv6-only resolver.},
howpublished = {ACM Special Interest Group on Data Communication (SIGCOMM), Poster},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
@misc{Yamamoto2023,
title = {Iterative Resolution with IPv6 Packets Failing},
author = {Momoka Yamamoto and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki
},
url = {https://tlab.hongo.wide.ad.jp/papers/ICCCN2023_poster.pdf},
doi = {10.1109/ICCCN58024.2023.10230170},
year = {2023},
date = {2023-07-24},
urldate = {2023-07-24},
address = {Waikiki Beach/Honolulu, Hawaii, USA},
abstract = {The exhaustion of IPv4 addresses has driven the rapid adoption of IPv6 networks, which has created challenges in the domain name resolution process, particularly for IPv6-only iterative resolvers. This paper presents an experimental analysis to quantify the extent of this problem, revealing a significantly lower success rate of name resolution using IPv6-only resolvers (64.1%) compared to IPv4-only resolvers (98.8%). By analysing the success rates and percentages of A and AAAA records for the top 1,000,000 domains in the Tranco list, we identify the limitations of IPv6-only iterative resolvers and highlight the urgent need for comprehensive solutions to improve DNS resolution in IPv6-only networks. Our findings emphasise the importance of full IPv6 adoption for improved compatibility in IPv6-only environments, and serve as a basis for addressing the challenges faced by IPv6-only networks.},
howpublished = {The 32nd International Conference on Computer Communication and Networks (ICCCN 2023), Poster},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
@misc{Hu2023,
title = {An Extended Kalman Filter Enabled Beam Tracking Framework in Intersection Management},
author = {Dou Hu and Jin Nakazato and Ehsan Javanmardi and Muhammad Asad and Manabu Tsukada
},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/371358188_An_Extended_Kalman_Filter_Enabled_Beam_Tracking_Framework_in_Intersection_Management/links/64807e24b3dfd73b776baeed/An-Extended-Kalman-Filter-Enabled-Beam-Tracking-Framework-in-Intersection-Management.pdf},
year = {2023},
date = {2023-06-06},
urldate = {2023-06-06},
address = {Gothenburg, Sweden},
abstract = {Recently, vehicle-to-everything (V2X) has been at- tracting attention for its potential to improve traffic safety and increase traffic volume worldwide, improving the accuracy of data and parameters collected from moving vehicles is widely discussed in the V2X. The most common technique of GPS may not be efficient during some specific scenarios, like some intersections full of skyscrapers, or some special terrains with obstacles. In such cases, GPS technology has a longer detection period and lower tracking accuracy, so beam tracking can be a fast and efficient solution in these circumstances. Therefore we propose an anti-diverge extend Kalman filter-enabled beam tracking method in V2X to help the intersection management. The numerical results show that our method has the ability to resist the Kalman filter’s divergence and can detect data in an accurate manner.},
howpublished = {European Conference on Networks and Communications (EuCNC) & 6G Summit Poster},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
@misc{Asabe2022,
title = {AutowareV2X: Enabling V2X Communication and Collective Perception for Autonomous Driving},
author = {Yu Asabe and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
url = {https://tlab.hongo.wide.ad.jp/papers/2022_AINTEC2022_poster_asabe.pdf
https://tlab-wide.github.io/AutowareV2X/main/},
year = {2022},
date = {2022-12-20},
urldate = {2022-12-20},
abstract = {For cooperative intelligent transport systems (C-ITS), vehicle-to-everything (V2X) communication is utilized to allow autonomous vehicles to share critical information with each other, enabling cooperatively enhanced environmental awareness and decision-making. We propose AutowareV2X, an implementation of a V2X communication module that is integrated into the autonomous driving (AD) software, Autoware. AutowareV2X provides external connectivity to the entire AD stack, enabling the end-to-end experimentation and evaluation of connected autonomous vehicles (CAV).},
howpublished = {Asian Internet Engineering Conference (AINTEC) 2022 Poster},
note = {Best Poster Award},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
@misc{Kambara2022,
title = {Towards Cooperative Automated Driving: Geographic-Aware Network Analysis and Visualization tool},
author = {Koichi Kambara and Ehsan Javanmardi and Jin Nakazato and Yousuke Watanabe and Kenya Sato and Hiroaki Takada and Manabu Tsukada},
url = {https://tlab.hongo.wide.ad.jp/papers/2022_AINTEC2022_poster_kambara.pdf},
year = {2022},
date = {2022-12-19},
urldate = {2022-12-19},
abstract = {In recent years the cooperative automated vehicle (CAV) concept has been gaining attention due to its potential to increase traffic safety and traffic flow by utilizing the vehicle-to-everything communication capability. One of the key requirements for CAV is ensuring every vehicle receives relevant messages at the right time and place; therefore, measuring and visualizing network performance is vital. However, for CAV applications, more network analyzers than those extant are needed because these do not consider geographical characteristics. In this study, we proposed a geographically-aware CAV-specific network analysis and visualization tool that can report the network performance factors such as packet loss, bandwidth, and jitter in real time. Further, we developed a proposal tool and evaluated it in an outdoor proof-of-concept study at the University of Tokyo’s Hongo Campus.},
howpublished = {Asian Internet Engineering Conference (AINTEC) 2022 Poster},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
@misc{Kiuchi2022,
title = {ZIGEN: A Windowing System Enabling Multitasking Among 3D and 2D Applications in Immersive Environments},
author = {Akihiro Kiuchi and Taishi Eguchi and Jun Rekimoto and Manabu Tsukada and Hiroshi Esaki},
doi = {10.1145/3532719.3543200},
year = {2022},
date = {2022-08-08},
urldate = {2022-08-08},
address = {Vancouver, Canada},
abstract = {In modern desktop environments, a windowing system allows multiple applications to be displayed simultaneously and work together, but in today's immersive environments, multitasking with multiple applications is very limited. Therefore, we designed and developed a windowing system, ZIGEN, from the ground up to achieve multitasking among 3D and 2D applications.},
howpublished = {ACM SIGGRAPH 2022 Posters},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
@misc{Tsukada2025b,
title = {V2X Communication Technologies in the Era of End-to-End Autonomous Driving},
author = {Manabu Tsukada},
url = {https://sites.google.com/view/b-stem-iot/},
year = {2025},
date = {2025-06-22},
urldate = {2025-06-22},
abstract = {Autonomous driving technology is undergoing a significant paradigm shift from traditional rule-based systems to integrated End-to-End (E2E) deep learning architectures. This transition necessitates a fundamental rethinking of Vehicle-to-Everything (V2X) communication, as existing V2X standards, primarily designed for rule-based systems, may not fully leverage the capabilities or address the needs of E2E models. This presentation explores the evolution required for V2X technologies in the E2E era. We contrast rule-based and E2E architectures, highlighting the limitations of current V2X approaches like object-level message sharing for E2E systems that benefit from richer data. While intermediate feature sharing via V2X is promising, its practical implementation faces hurdles, notably the heterogeneity of sensors, AI models, and tasks across vehicles. To address these challenges, we introduce a research approach aiming to maximize V2X value through an E2E pipeline encompassing data foundation (Co3SOP dataset for collaborative 3D semantic occupancy), perception adaptation (PHCP framework for heterogeneous collaboration during inference), and decision optimization (PrefDrive integrating LLMs with preference learning). Through these interconnected efforts, we aim to unlock the full potential of V2X communication to enhance the safety, efficiency, and robustness of E2E autonomous driving systems.},
howpublished = {The 2nd Workshop on Secure connected vehicles: Digital Twin, UAVs, and Smart Transportation, at IEEE IV 2025},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
@misc{Tsukada2024d,
title = {Cooperative Autonomous Mobility through Open Standards and Real-World Experiments},
author = {Manabu Tsukada},
url = {https://coop-intelligence.github.io/},
year = {2024},
date = {2024-09-30},
urldate = {2024-09-30},
address = {Milan, Italy},
abstract = {This talk explores how open standards and real-world experiments are driving the advancement of cooperative autonomous mobility. It highlights the transformative potential of integrating Vehicle-to-Everything (V2X) communication and Roadside Perception Units (RSPUs) to enhance autonomous driving capabilities. Through field tests and simulations, the presentation showcases the effectiveness of these systems in enabling cooperative perception and maneuver coordination, paving the way for a safer and more efficient transportation future. The emphasis on open standards underscores their crucial role in fostering interoperability and innovation within the intelligent transportation ecosystem.},
howpublished = {1st Workshop on Cooperative Intelligence for Embodied AI in The 18th European Conference on Computer Vision (ECCV 2024 Workshop)},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
@misc{Tsukada2024c,
title = {Integrating Infrastructure-Assisted V2X Communication for Enhanced Cooperative Autonomous Driving},
author = {Manabu Tsukada},
url = {https://www.iiotbdsc.net/},
year = {2024},
date = {2024-09-21},
abstract = {This presentation delves into the integration of smart infrastructure with Vehicle-to-Everything (V2X) communication to advance the safety and intelligence of Cooperative Autonomous Driving (CAD). We will explore how roadside sensors and V2X communication enhance vehicles' environmental awareness, enabling them to make safer and more informed decisions. The session will highlight key projects such as AutowareV2X and AutoMCM, demonstrating the potential of coordinated vehicle interactions to improve driving efficiency and safety. Furthermore, the talk will discuss the role of roadside LiDAR in providing precise localization, even in challenging environments. By showcasing the synergy of these cutting-edge technologies, the presentation aims to illustrate a future of safer, more connected, and efficient autonomous driving.},
howpublished = {Invited talk at the 5th International Conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC 2024)},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
@misc{Tsukada2024b,
title = {Integrating Infrastructure-Assisted V2X Communication for Next-Generation Cooperative Autonomous Driving},
author = {Manabu Tsukada},
url = {https://www.fzi.de/en/veranstaltungen/iv-workshop-intelligent-infrastructure-and-uav/},
year = {2024},
date = {2024-06-02},
urldate = {2024-06-02},
abstract = {This talk explores how combining smart road technology and vehicle communication (V2X) can make self-driving cars (CAD) safer and smarter. It will discuss how roadside sensors and V2X can help cars understand their surroundings better and make safer decisions. The talk will also introduce projects like AutowareV2X and AutoMCM, showing how cars working together can make driving smoother and safer. Additionally, it will cover how roadside LiDAR can help cars know exactly where they are, even in tough situations. Overall, the presentation will show how these advanced technologies work together to make future driving safer, more connected, and efficient.},
howpublished = {Invited talk at 1st Workshop on Application of Intelligent Infrastructure for Automated Driving as part of the IEEE IV Symposium 2024},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
@misc{Tsukada2024,
title = {V2X and Beyond: Integrating Roadside Units for Next-Level Cooperative Automated Driving},
author = {Manabu Tsukada},
url = {https://www.pervehicle.org/},
year = {2024},
date = {2024-03-15},
urldate = {2024-03-15},
abstract = {As the frontier of vehicular technology continues to expand, the integration of cooperative systems, particularly through Vehicle-to-Everything (V2X) communication and Roadside Perception Units (RSPUs), emerges as a pivotal force in redefining road safety, efficiency, and vehicular autonomy. This talk delves into the transformative potential of networked RSPUs in enhancing autonomous driving capabilities, underpinned by the robust foundation of V2X communication. It explores the innovative concept of infrastructure-assisted vehicular communication, spotlighting the pivotal role of RSPUs in facilitating cooperative perception – a critical component for achieving comprehensive situational awareness and bolstering the cooperative intelligent transport system (ITS). By harnessing the synergy of V2X and advanced middleware, such as AutowareV2X and AutoMCM, the discourse presents a comprehensive overview of the seamless integration of autonomous vehicles within the intelligent vehicular ecosystem. Through a series of rigorous field tests and simulation experiments, the talk showcases the efficacy of these advanced systems, highlighting their proficiency in delivering vital messages with minimal latency, even under challenging traffic conditions. Moreover, it underscores the significance of collective perception and maneuver coordination in optimizing vehicular operations, thereby paving the way for a future where autonomous vehicles and intelligent infrastructure coalesce to form a harmonious, safe, and highly efficient transportation network. The insights shared in this talk not only reflect a deep understanding of current technological advancements but also chart a course for future innovations in the realm of pervasive computing for vehicular systems.},
howpublished = {Keynote speech at 6th International Workshop on Pervasive Computing for Vehicular Systems (PerVehicle) 2024},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
@misc{Tsukada2021,
title = {[Invited talk] Cooperative Automated Driving Using Vehicle-to-Everything (V2X)},
author = {Manabu Tsukada},
url = {https://interlab.ait.ac.th/aintec2021/
https://youtu.be/WreVkQEGIKk
https://tlab.hongo.wide.ad.jp/papers/20211124-AINTEC2021-talk2.pdf
},
year = {2021},
date = {2021-12-16},
urldate = {2021-12-16},
address = {Online},
abstract = {Autonomous automatic driving is essentially a replacement for humans. It has the same limitations as human drivers because it uses onboard sensors and computers to drive based on localized information. Cooperative automatic driving is expected to achieve a level of safety and efficiency that has not been possible with human driving imitation by accurately perceiving a wide range of physical space through V2X communication. This talk will introduce the prospects of cooperative automated driving and the attempts of cooperative perception and cooperative planning implemented using Autoware, an automated driving software.},
howpublished = {Invited talk at the 16th Asian Internet Engineering Conference (AINTEC), Virtual Conference, 14-17 December 2021},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
@misc{Tsukada2020bb,
title = {Roadside cooperative perception for autonomous vehicles},
author = {Manabu Tsukada},
url = {https://tlab.hongo.wide.ad.jp/jfli-workshop-2020/},
year = {2020},
date = {2020-02-17},
abstract = {Cooperative Intelligent Transportation Systems (ITS) are systems where the vehicles, the roadside infrastructure, central control centres and other elements exchange information to achieve better road safety, traffic efficiency and comfort of the road users. These cooperative elements share the information by organising Vehicular Ad-hoc Network (VANET) or using the mobile network (e.g. 3G and LTE) and realises various cooperative ITS services. To promote wider deployment of such services, many stakeholders made consensus that the communication platform must be based on a common architecture and drive rapid international standardisation (e.g. ISO, ETSI, IEEE). In the near future, autonomous vehicles also benefit from such communication platforms by enforcing the perception, planing, and decision-making. However the most basic Vehicle-to-Vehicle (V2V) messages standardized in Europe, US and Japan suffer from same issues such as 1) unable to receive messages from the object without V2V transmitter, 2) message loss because of obstacle and wireless range, 3) vulnerable for malfunctioning and malicious node. For the solution, we propose an infrastructure-assisted V2V messaging system to support cooperative autonomous driving. We design the system based on the ITS Station architecture standardized in ISO/ETSI, working with any vehicle sensing technology. Moreover, we implement the prototype roadside system with a stereo vision for the vehicle sensing. The prototype system is evaluated in a field test in the campus of the University of Tokyo. The results show that the proposed system significantly improves the coverage of V2V messaging while the system overhead is limited. The proposal is being integrated to our personal mobility autonomous vehicle system based on an open source software. We are also planning the contribution to the international standardization of the technique.},
howpublished = {JFLI Workshop 2020 on Next Generation Networking},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
@article{ITO2025108157,
title = {A multipath redundancy communication framework for enhancing 5G mobile communication quality},
author = {Koki Ito and Jin Nakazato and Romain Fontugne and Manabu Tsukada and Esaki Hiroshi},
url = {https://www.sciencedirect.com/science/article/pii/S0140366425001148},
doi = {https://doi.org/10.1016/j.comcom.2025.108157},
issn = {0140-3664},
year = {2025},
date = {2025-04-23},
urldate = {2025-04-23},
journal = {Computer Communications},
pages = {108157},
abstract = {As networks increasingly become the backbone of modern society, the demands placed on them by various applications have become more complex. In particular, the demand for high-capacity, low-latency services such as real-time streaming is increasing every year. Although 5G has been deployed to meet these needs, its effectiveness can vary significantly by location and time, and sometimes falls short of requirements. Traditionally, much of the research to improve communication stability has focused on TCP-based systems, which do not translate well to real-time UDP streaming applications. To address the above challenges, we propose a multipath redundant communication framework designed to improve the quality of real-time media streaming. This framework has been tested using multipath redundant communication over two mobile networks with a moving vehicle in an urban environment. Using a real-time streaming application based on WebRTC, our framework demonstrates a significant reduction in packet loss and an increase in bitrate, outperforming existing multipath redundant communication systems without interfering with the application’s congestion control mechanisms.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Asad2024b,
title = {Federated Learning for Secure and Efficient Vehicular Communications in Open RAN},
author = {Muhammad Asad and Saima Shaukat and Jin Nakazato and Ehsan Javanmardi and Manabu Tsukada},
url = {https://rdcu.be/d7RSW},
doi = {10.1007/s10586-024-04932-3},
issn = {1386-7857},
year = {2025},
date = {2025-01-28},
urldate = {2024-11-25},
journal = {Cluster Computing},
volume = {28},
number = {211},
abstract = {This paper presents a comprehensive exploration of federated learning applied to vehicular communications within the context of Open RAN. Through an in-depth review of existing literature and analysis of fundamental concepts, critical challenges are identified within the current methodologies employed in this sphere. A novel framework is proposed to address these shortcomings, fundamentally based on federated learning principles. This framework aims to enhance security and efficiency in vehicular communications, leveraging the flexibility of Open RAN architecture. The paper further delves into a rigorous justification of the proposed solution, highlighting its potential impact and the improvements it could bring to vehicular communications. Ultimately, this study provides a roadmap for future research in applying federated learning for more secure and efficient vehicular communications in Open RAN, opening up new avenues for exploration in this exciting interdisciplinary domain.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {A Survey on Recent Advancements in Autonomous Driving Using Deep Reinforcement Learning: Applications, Challenges, and Solutions},
author = {Rui Zhao and Yun Li and Yuze Fan and Fei Gao and Manabu Tsukada and Zhenhai Gao},
doi = {10.1109/TITS.2024.3452480},
isbn = {1524-9050},
year = {2024},
date = {2024-09-18},
urldate = {2024-09-18},
journal = {IEEE Transactions on Intelligent Transportation Systems},
volume = {25},
issue = {2},
pages = {19365 - 19398},
abstract = {Autonomous driving (AD) endows vehicles with the capability to drive partly or entirely without human intervention. AD agents generate driving policies based on online perception results, which are crucial to the realization of safe, efficient, and comfortable driving behaviors, particularly in high-dimensional and stochastic traffic scenarios. Currently, deep reinforcement learning (DRL) techniques to derive and validate AD policies have witnessed vast research efforts and have shown rapid development in recent years. However, a comprehensive interpretation and evaluation of their strengths and limitations concerning the full-stack AD tasks remain uncharted. This paper presents a survey of this body of work, which is conducted at three levels. First, it analyzes the multi-level AD task characteristics and delves deeply into the current DRL methodologies primarily employed in AD. Second, a taxonomy of the literature studies is constructed from the system perspective, identifying six modes of DRL model integration into an AD architecture that span the entire spectrum of AD policy processes, from perception understanding and decision-making to motion control, as well as verification and validation. Each literature review comprehensively encompasses the main elements of designing such a system, including modeling partially observable environments, state and action spaces, reward structuring, and the design and training methodologies of neural network models. Finally, an in-depth foresight is conducted on how the eight critical issues of AD application development are addressed by the DRL models tailored for real-world AD challenges.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Asabe2024,
title = {Enhancing Reliability in Infrastructure-based Collective Perception: A Dual-Channel Hybrid Delivery Approach with Real-Time Monitoring},
author = {Yu Asabe and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
doi = {10.1109/OJVT.2024.3443877},
issn = {2644-1330},
year = {2024},
date = {2024-08-30},
urldate = {2024-08-30},
journal = {IEEE Open Journal of Vehicular Technology},
volume = {5},
pages = {1124-1138},
abstract = {Standalone autonomous vehicles primarily rely on their onboard sensors and may have blind spots or limited situational awareness in complex or dynamic traffic scenarios, leading to difficulties in making safe decisions. Collective perception enables connected autonomous vehicles (CAVs) to overcome the limitations of standalone autonomous vehicles by sharing sensory information with nearby road users. However, unfavorable conditions of the wireless communication medium it uses can lead to limited reliability and reduced quality of service. In this paper, we propose methods for increasing the reliability of collective perception through real-time packet delivery rate monitoring and a dual-channel hybrid delivery approach. We have implemented AutowareV2X, a vehicle-to-everything (V2X) communication module integrated into the autonomous driving (AD) software Autoware. AutowareV2X provides connectivity to the AD stack, enabling end-to-end (E2E) experimentation and evaluation of CAVs. The Collective Perception Service (CPS) was also implemented, allowing the transmission of Collective Perception Messages (CPMs). Our proposed methods using AutowareV2X were evaluated using actual hardware and vehicles in reallife field tests. Results have indicated that the E2E network latency of the perception information sent is around 30 ms, and the AD software can use shared object data to conduct collision avoidance maneuvers. The dual-channel delivery of CPMs enabled the CAV to dynamically select the best CPM from CPMs received from different links, depending on the freshness of their information. This enabled the reliable transmission of CPMs even when there was significant packet loss on one of the transmitting channels.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Lin2024b,
title = {Clothoid Curve-based Emergency Stopping Path-Planning with Adaptive Potential Field for Autonomous Vehicles},
author = {Pengfei Lin and Ehsan Javanmardi and Manabu Tsukada},
doi = {10.1109/TVT.2024.3380745},
issn = {0018-9545},
year = {2024},
date = {2024-07-24},
urldate = {2024-03-22},
journal = {IEEE Transactions on Vehicular Technology},
volume = {73},
issue = {7},
pages = {9747-9762},
abstract = {Potential Field-based path planning methods are widely embraced in the context of autonomous vehicles due to their real-time efficiency and simplicity. While the potential field effectively enforces a rigid road boundary to keep the vehicle within the confines of the road, it can lead to the “blind alley” problem caused by local minima in specific high- speed scenarios, resulting in indecision, erratic behavior, or even accidents. Therefore, the objective of this research is to anticipate and address the aforementioned problem in order to proactively avoid potential collisions. We have also found that existing methods do not offer a root cause analysis or practical solutions for this issue, which limits the practicality of the potential field in handling complicated traffic situations. In this paper, we propose an Emergency-Stopping Path Planning (ESPP) approach that incorporates an adaptive potential field with the clothoid curve. First, we design an emergency triggering estimation to detect the ”blind alley” problem. Second, we regionalize the driving scene to search for the optimal breach point on the road PF and the final stopping point for the vehicle by considering the motion range of the obstacle. Finally, we use the optimized clothoid curve to fit these calculated points under vehicle dynamics constraints to generate a smooth emergency avoidance path. The proposed ESPP method was evaluated by conducting the co-simulation between MATLAB/Simulink and CarSim Simulator in a freeway scene. The simulation results reveal that the proposed method shows increased performance in emergency collision avoidance and renders the vehicle safer, in which the duration of wheel slip is 61.9% shorter, and the maximum steering angle amplitude is 76.9% lower than other potential field-based methods.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Asad2024,
title = {Secure and Efficient Blockchain-based Federated Learning Approach For VANETs},
author = {Muhammad Asad and Saima Shaukat and Ehsan Javanmardi and Jin Nakazato and Naren Bao and Manabu Tsukada},
doi = {10.1109/JIOT.2023.3322221},
issn = {2327-4662},
year = {2024},
date = {2024-03-01},
urldate = {2023-10-05},
journal = {IEEE Internet of Things Journal},
volume = {11},
issue = {5},
pages = {9047-9055},
abstract = {The rapid increase in the number of connected vehicles on roads has made Vehicular Ad-hoc Networks (VANETs) an attractive target for malicious actors. As a result, VANETs require secure data transmission to maintain the network’s integrity. Federated Learning (FL) has been proposed as a secure data-sharing method for VANETs, but it is limited in its ability to protect sensitive data. This paper proposes integrating Blockchain technology into FL to provide an additional layer of security for VANETs. In particular, we propose a Secure and Efficient Blockchain-based FL (SEBFL) approach to ensure communication efficiency and data privacy in VANETs. To this end, we use the FL model for VANETs, where computation tasks are decomposed from a base station to individual vehicles. This effectively reduces the congestion delay and communication overhead. Integrating blockchain with the FL model provides a reliable and secure data communication system between vehicles, roadside units, and a cloud server. Additionally, we use a Homomorphic Encryption System (HES) that effectively preserves the confidentiality and credibility of vehicles. Besides, the proposed SEBFL leverages the asynchronous FL model, minimizing the long delay while avoiding possible threats and attacks using HES. The experiment results show the proposed SEBFL achieves 0.87% accuracy while a model inversion attack and 0.86% accuracy while a membership inference attack.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Location-Aided Fast Beam Tracking Algorithm for Millimeter-Wave V2I},
author = {Sojin Ozawa and Tokio Ikuta and Yuki Sasaki and Ryo Iwaki and Jin Nakazato and Manabu Tsukada and Hideya So and Kazuki Maruta},
doi = {10.23919/comex.2024XBL0001},
year = {2024},
date = {2024-02-15},
urldate = {2024-02-15},
journal = {IEICE Communications Express (ComEX)},
abstract = {This article proposes a millimeter-wave fast beam tracking algorithm for moving vehicles, considering a geometry of road environment. Focusing on the fact that vehicle movement is constrained on roads, horizontal and vertical beam directions are determined based on obtainable driving direction and road shape. In addition, we perform a two-pattern beam selection for the vehicle’s forward and rearward directions to esti- mate the beam tracking speed. By conducting simulations using SUMO, which emulates vehicle movement on various roads, we verified the effective operation of the proposed scheme and confirmed its superiority over the existing beam sweeping approach.},
note = {ComEX Top Downloaded Letter Award},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Tao2023b,
title = {Zero-Knowledge Proof of Traffic: A Deterministic and Privacy-Preserving Cross Verification Mechanism for Cooperative Perception Data},
author = {Ye Tao and Ehsan Javanmardi and Pengfei Lin and Yuze Jiang and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
url = {https://arxiv.org/abs/2312.07948},
doi = {10.1109/ACCESS.2023.3343405},
issn = {2169-3536},
year = {2023},
date = {2023-12-17},
urldate = {2023-12-17},
journal = {IEEE Access},
volume = {11},
pages = {142846-142861},
abstract = {Cooperative perception is crucial for connected automated vehicles in intelligent transportation systems (ITSs); however, ensuring the authenticity of perception data remains a challenge as the vehicles cannot verify events that they do not witness independently. Various studies have been conducted on establishing the authenticity of data, such as trust-based statistical methods and plausibility-based methods. However, these methods are limited as they require prior knowledge such as previous sender behaviors or predefined rules to evaluate the authenticity. To overcome this limitation, this study proposes a novel approach called zero-knowledge Proof of Traffic (zk-PoT), which involves generating cryptographic proofs to the traffic observations. Multiple independent proofs regarding the same vehicle can be deterministically cross-verified by any receivers without relying on ground truth, probabilistic, or plausibility evaluations. Additionally, no private information is compromised during the entire procedure. A full on-board unit software stack that reflects the behavior of zk-PoT is implemented within a specifically designed simulator called Flowsim. A comprehensive experimental analysis is then conducted using synthesized city-scale simulations, which demonstrates that zk-PoT’s cross-verification ratio ranges between 80 % to 96 %, and 90 % of the verification is achieved in 5 s, with a protocol overhead of approximately 25 %. Furthermore, the analyses of various attacks indicate that most of the attacks could be prevented, and some, such as collusion attacks, can be mitigated. The proposed approach can be incorporated into existing works, including the European Telecommunications Standards Institute (ETSI) and the International Organization for Standardization (ISO) ITS standards, without disrupting the backward compatibility.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Chauhan2023c,
title = {Fostering Fuzzy Logic in Enhancing Pedestrian Safety: Harnessing Smart Pole Interaction Unit for Autonomous Vehicle-to-Pedestrian Communication and Decision Optimization},
author = {Vishal Chauhan and Chia-Ming Chang and Ehsan Javanmardi and Jin Nakazato and Pengfei Lin and Takeo Igarashi and Manabu Tsukada},
url = {https://www.mdpi.com/2079-9292/12/20/4207},
doi = {10.3390/electronics12204207},
issn = {2079-9292},
year = {2023},
date = {2023-10-11},
urldate = {2023-10-11},
journal = {Electronics},
volume = {12},
number = {20},
abstract = {In autonomous vehicles (AVs), ensuring pedestrian safety within intricate and dynamic settings, particularly at crosswalks, has gained substantial attention. While AVs perform admirably in standard road conditions, their integration into unique environments like shared spaces devoid of traditional traffic infrastructure control presents complex challenges. These challenges involve issues of right-of-way negotiation and accessibility, particularly in “naked streets”. This research delves into an innovative smart pole interaction unit (SPIU) with an external human–machine interface (eHMI). Utilizing virtual reality (VR) technology to evaluate the SPIU efficacy, this study investigates its capacity to enhance interactions between vehicles and pedestrians at crosswalks. The SPIU is designed to communicate the vehicles’ real-time intentions well before arriving at the crosswalk. The study findings demonstrate that the SPIU significantly improves secure decision making for pedestrian passing and stops in shared spaces. Integrating an SPIU with an eHMI in vehicles leads to a substantial 21% reduction in response time, greatly enhancing the efficiency of pedestrian stops. Notable enhancements are observed in unidirectional (one-way) and bidirectional (two-way) scenarios, highlighting the positive impact of the SPIU on interaction dynamics. This work contributes to AV–pedestrian interaction and underscores the potential of fuzzy-logic-driven solutions in addressing complex and ambiguous pedestrian behaviors.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Lin2022e,
title = {Safety Tunnel-Based Model Predictive Path-Planning Controller with Potential Functions for Emergency Navigation},
author = {Pengfei Lin and Ying Shuai Quan and Jin Ho Yang and Chung Choo Chung and Manabu Tsukada},
doi = {10.1109/TITS.2022.3229699},
issn = {1524-9050},
year = {2023},
date = {2023-04-01},
urldate = {2023-04-01},
journal = {IEEE Transactions on Intelligent Transportation Systems},
volume = {24},
issue = {4},
pages = {3974 - 3985},
abstract = {The potential functions (PFs) have generally shown good performances in real-time path planning with computation efficiency conforming to the requirements of lower control systems in autonomous driving. However, several inherent limitations exist in using the PFs, including a local minimum in specific scenarios and no passage between closely spaced obstacles. Recent studies have focused on conventional scenarios where PFs are assumed to work normally, without malfunctioning, occurring during perilous situations. Therefore, we propose a specific safety tunnel (ST)-based model predictive controller (MPC) combined with PFs (PF-STMPC) to handle path-planning in extreme-emergency traffic scenarios (e.g., emergency braking and lane-changing obstacles). To further guarantee driving safety, we improve PFs with the responsibility-sensitive safety (RSS) model that accurately calculates the minimum safe longitudinal and lateral distances. Furthermore, a sigmoid-based ST is designed for emergency navigation if the PFs fail to plan a safe path due to the aforementioned inherent limitations, enabling the controller with planning functionality if necessary. The ST is embedded in the MPC-based tracking controller as a safe constraint sensitive to surrounding environments (e.g., road structure and obstacles). The proposed PF-STMPC was co-simulated using MATLAB/Simulink and CarSim Simulator under the constant speed condition. Compared with the state-of-the-art method, the proposed method demonstrated better performance in finding a safe path and eliminating severe yawing of the ego-vehicle (82.8% less in sideslip yawing amplitude and 57.7% shorter in the oscillation period of yaw angle) when facing traffic emergencies.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Masuda2022,
title = {Feature-based Vehicle Identification Framework for Optimization of Collective Perception Messages in Vehicular Networks},
author = {Hidetaka Masuda and Oussama El Marai and Manabu Tsukada and Tarik Taleb and Hiroshi Esaki},
doi = {10.1109/TVT.2022.3211852},
isbn = {0018-9545},
year = {2022},
date = {2022-10-04},
urldate = {2022-10-04},
journal = {IEEE Transactions on Vehicular Technology},
volume = {72},
issue = {2},
pages = {2120-2129},
abstract = {The world is moving towards a fully connected digital world, where objects produce and consume data, at a sultry pace. Autonomous vehicles will play a key role in bolstering the digitization of the world. These connected vehicles must communicate timely data with their surrounding objects and road participants to fully and accurately understand their environments and eventually operate smoothly. As a result, the hugely exchanged data would scramble the network traffic that, at a given point, would no longer increase the awareness level of the vehicle. In this paper, we propose a vision-based approach to identify connected vehicles and use it to optimize the exchange of collective perception messages (CPMs), in terms of both the CPM generation frequency and the number of generated CPMs. To validate our proposed approach, we created a Cartery framework that integrates SUMO, Carla, and OMNeT++. We also compared our solution with both baselines and European Telecommunications Standards Institute solutions, considering three main KPIs: the channel busy ratio, environmental awareness, and the CPM generation frequency. Simulation results show that our proposed solution exhibits the best trade-off between the network load and situational awareness.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Lin2022,
title = {Model Predictive Path-Planning Controller with Potential Function for Emergency Collision Avoidance on Highway Driving},
author = {Pengfei Lin and Manabu Tsukada},
doi = {10.1109/LRA.2022.3152693},
isbn = {2377-3766},
year = {2022},
date = {2022-04-22},
urldate = {2022-04-22},
journal = {Robotics and Automation Letters (RA-L) with IEEE International Conference on Robotics and Automation (ICRA) option},
volume = {7},
issue = {2},
pages = {4662-4669},
abstract = {Existing potential functions (PFs) utilized in autonomous vehicles mainly focus on solving the path-planning problems in some conventional driving scenarios; thus, their performance may not be satisfactory in the context of emergency obstacle avoidance. Therefore, we propose a novel model predictive path-planning controller (MPPC) combined with PFs to handle complex traffic scenarios (e.g., emergency avoidance when a sudden accident occurs). Specifically, to enhance the safety of the PFs, we developed an MPPC to handle an emergency case with a sigmoid-based safe passage embedded in the MPC constraints (SPMPC) with a specific triggering analysis algorithm on monitoring traffic emergencies. The presented PF-SPMPC algorithm was compiled in a comparative simulation study using MATLAB/Simulink and CarSim. The algorithm outperformed the latest PF-MPC approach to eliminate the severe tire oscillations and guarantee autonomous driving safety when handling the traffic emergency avoidance scenario.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Tsukada2020b,
title = {Networked Roadside Perception Units for Autonomous Driving},
author = {Manabu Tsukada and Takaharu Oi and Masahiro Kitazawa and Hiroshi Esaki
},
url = {https://www.mdpi.com/1424-8220/20/18/5320/pdf?.pdf
https://youtu.be/n7gD0L7NDEM},
doi = {10.3390/s20185320},
issn = {1424-8220},
year = {2020},
date = {2020-09-17},
urldate = {2020-09-17},
journal = {MDPI Sensors},
volume = {20},
number = {18},
abstract = {Vehicle-to-Everything (V2X) communication enhances the capability of autonomous driving through better safety, efficiency, and comfort. In particular, sensor data sharing, known as cooperative perception, is a crucial technique to accommodate vulnerable road users in a cooperative intelligent transport system (ITS). In this paper, we describe a roadside perception unit (RSPU) that combines sensors and roadside units (RSUs) for infrastructure-based cooperative perception. We propose a software called AutoC2X that we designed to realize cooperative perception for RSPUs and vehicles. We also propose the concept of networked RSPUs, which is the inter-connection of RSPUs along a road over a wired network, and helps realize broader cooperative perception. We evaluated the RSPU system and the networked RSPUs through a field test, numerical analysis, and simulation experiments. Field evaluation showed that, even in the worst case, our RSPU system can deliver messages to an autonomous vehicle within 100 ms. The simulation result shows that the proposed priority algorithm achieves a wide perception range with a high delivery ratio and low latency, especially under heavy road traffic conditions. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Azuma2018,
title = {A Method of Misbehavior Detection with Mutual Vehicle Position Monitoring},
author = {Shuntaro Azuma and Manabu Tsukada and Kenya Sato},
url = {https://hal.archives-ouvertes.fr/hal-01879098/document?.pdf},
year = {2018},
date = {2018-06-10},
journal = {IntTech18v11n12, International Journal On Advances in Internet Technology},
volume = {11},
number = {1&2},
pages = {82-91},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Tao2017,
title = {Reliable Overlay Networking on ETSI GeoNetworking Standards},
author = {Ye Tao and Xin Li and Manabu Tsukada and Hiroshi Esaki},
url = {http://rdcu.be/qyX5},
doi = {https://doi.org/10.1007/s13177-017-0141-7},
isbn = {1348-8503},
year = {2017},
date = {2017-04-20},
journal = {International Journal of Intelligent Transportation Systems Research},
volume = {16},
number = {2},
pages = {98-111},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Tsukada2014,
title = {On the Experimental Evaluation of Vehicular Networks: Issues, Requirements and Methodology Applied to a Real Use Case},
author = {Manabu Tsukada and Jos{'e} Santa and Satoshi Matsuura and Thierry Ernst and Kazutoshi Fujikawa},
url = {https://hal.inria.fr/hal-01095282/document?.pdf
https://youtu.be/NamJUd-_0jw},
doi = {http://dx.doi.org/10.4108/inis.1.1.e4},
isbn = {2410-0218},
year = {2014},
date = {2014-12-01},
journal = {EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
volume = {1},
number = {1},
pages = {1-14},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Li2014,
title = {MultiVehicle Cooperative Local Mapping: A Methodology Based on Occupancy Grid Map Merging},
author = {Hao Li and Manabu Tsukada and Fawzi Nashashibi and Michel Parent},
url = {https://hal.inria.fr/hal-01107534/file/LI_Nashashibi_Draft_Juillet2013.pdf},
doi = {10.1109/TITS.2014.2309639},
isbn = {1524-9050},
year = {2014},
date = {2014-10-10},
journal = {IEEE Transactions on Intelligent Transportation Systems},
volume = {15},
number = {5},
pages = {2089-2100},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Lee2012,
title = {MNPP: Mobile Network Prefix Provisioning for Enabling Route Optimization in Geographic Vehicular Networks},
author = {Jong-Hyouk Lee and Manabu Tsukada and Thierry Ernst},
url = {http://www.oldcitypublishing.com/journals/ahswn-home/ahswn-issue-contents/ahswn-volume-15-number-1-2-2012/},
issn = {1551-9899},
year = {2012},
date = {2012-06-13},
journal = {AHSWN - Ad Hoc & Sensor Wireless Networks},
volume = {15},
number = {1},
pages = {5-19},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Noguchi2012,
title = {Design and Field evaluation of geographical location-aware service discovery on IPv6 GeoNetworking for VANET},
author = {Satoru Noguchi and Manabu Tsukada and Thierry Ernst and Astuo Inomata and Kazutoshi Fujikawa
},
url = {https://hal.inria.fr/hal-00784409/document?.pdf},
doi = {10.1186/1687-1499-2012-29},
isbn = {1687-1499},
year = {2012},
date = {2012-02-20},
journal = {special issue (SI) of "Network Routing and Communication Algorithm for Intelligent Transportation Systems" in EURASIP Journal on Wireless Communications and Networking},
pages = {1-16},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Tsukada2010,
title = {Design and Experimental Evaluation of a Vehicular Network Based on NEMO and MANET},
author = {Manabu Tsukada and José Santa and Olivier Mehani and Yacine Khaled and Thierry Ernst},
url = {https://hal.inria.fr/hal-00784433/document?.pdf
https://youtu.be/LtFrE8Ezho0},
doi = {10.1155/2010/656407},
isbn = {1687-6180},
year = {2010},
date = {2010-09-10},
journal = {The special issue for Vehicular Ad Hoc Networks, EURASIP Journal on Advances in Signal Processing},
volume = {2010},
pages = {1-16},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Santa2009,
title = {Assessment of VANET multi-hop routing over an experimental platform},
author = {José Santa and Manabu Tsukada and Thierry Ernst and Olivier Mehani and Antonio F. Gómez-Skarmeta},
url = {https://hal.inria.fr/inria-00625837/document?.pdf},
doi = {10.1504/IJIPT.2009.028655},
issn = {1743-8209},
year = {2009},
date = {2009-09-30},
journal = {International Journal of Internet Protocol Technology},
volume = {4},
number = {3},
pages = {158-172},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Khaled2009,
title = {A usage oriented analysis of vehicular networks: from technologies to applications},
author = {Yacine Khaled and Manabu Tsukada and José Santa and JinHyeock Choi and Thierry Ernst},
url = {https://pdfs.semanticscholar.org/dea9/8e19ab72a60e2f5783066c5f7912efca3b64.pdf},
doi = {10.4304/jcm.4.5.357-368},
issn = {1796-2021},
year = {2009},
date = {2009-06-10},
journal = {Journal of Communications (JCM), Academy Publisher, Special Issue on Challenges in Future Vehicular AD HOC Networks, vol. 4, no. 5, pp. 357-368, May 2009},
volume = {4},
number = {5},
pages = {357-368},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@bachelorthesis{Shim2021,
title = {VANETにおける知覚情報共有を利用したPseudonym対応の不正行為検出},
author = {有井慎平(Shimpei Arii)},
year = {2021},
date = {2021-03-31},
urldate = {2021-03-31},
organization = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
school = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
@bachelorthesis{Ito2020,
title = {V2X協調型運転システムにおける通信性能のリアルタイム可視化ツール},
author = {伊藤 彰秀(Akihide Ito)},
year = {2020},
date = {2020-03-31},
urldate = {2020-03-31},
organization = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
school = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
@bachelorthesis{昌大2017b,
title = {協調型ITSにおける携帯網を併用した車車間通信の支援},
author = {北沢 昌大 (Masahiro Kitazawa)},
year = {2017},
date = {2017-03-31},
urldate = {2017-03-31},
organization = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
school = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
@bachelorthesis{知也2016b,
title = {協調型ITS における車々間メッセージ代理生成・送信システムの設計},
author = {北里 知也 (Tomoya Kitazato)},
year = {2016},
date = {2016-03-30},
urldate = {2016-03-30},
organization = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
school = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
@inbook{Sato2016,
title = {Probe Vehicle Information Systems},
author = {Masaaki Sato and Manabu Tsukada and Hiroshi Ito},
url = {https://www.crcpress.com/Intelligent-Transportation-Systems-From-Good-Practices-to-Standards/Pagano/p/book/9781498721868},
doi = {10.1201/9781315370866-9},
isbn = {9781498721868},
year = {2016},
date = {2016-08-20},
pages = {151-170},
publisher = {Intelligent Transportation Systems: From Good Practices to Standards, CRC Press Book},
chapter = {8},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@inbook{Khaled2010,
title = {The Role of Communication Technologies in Vehicular Applications},
author = {Yacine Khaled and Manabu Tsukada and José Santa and Thierry Ernst},
url = {https://www.igi-global.com/book/advances-vehicular-hoc-networks/37323},
doi = {10.4018/978-1-61520-913-2},
isbn = {9781615209132},
year = {2010},
date = {2010-04-20},
pages = {37-57},
publisher = {Advances in Vehicular Ad-Hoc Networks: Developments and Challenges, IGI Global},
chapter = {3},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@inproceedings{Li2025b,
title = {State-Guided Spatial Cross-Attention for Enhanced End-to-End Autonomous Driving},
author = {Dongyang Li and Ehsan Javanmardi and Manabu Tsukada},
year = {2025},
date = {2025-09-30},
urldate = {2025-09-30},
booktitle = {IEEE International Automated Vehicle Validation Conference (IAVVC 2025)},
address = {Baden-Baden, Germany},
abstract = {Handling near-accident scenarios is a significant challenge for end-to-end autonomous driving (E2E-AD), as these situations often involve sudden environmental changes, complex interactions with other road users, and high-risk decision-making under uncertainty. Unlike routine driving tasks, near-accident scenarios require rapid and precise responses based on external perception and internal vehicle dynamics. Successfully navigating such situations demands not only a comprehensive understanding of the surrounding environment but also an accurate assessment of the ego vehicle's state, including speed, acceleration, and steering angle, to ensure safe and reliable control. However, conventional E2E-AD models struggle to handle these safety-critical situations effectively. Standard approaches primarily rely on raw sensor inputs to learn driving policies, often overlooking the crucial role of vehicle state information in decision-making. Since many near-accident scenarios involve conditions where the same environmental observation could require vastly different responses depending on the ego vehicle's motion state-such as whether the vehicle is braking, accelerating, or experiencing traction loss-ignoring these internal dynamics can lead to unsafe or suboptimal actions. Furthermore, E2E-AD models typically learn a direct mapping from sensory inputs to control outputs, making it difficult to generalize to highly dynamic and unpredictable interactions, such as emergency evasive maneuvers or sudden braking events. To address these challenges, we propose a state-guided cross-attention mechanism that explicitly models the interaction between the ego vehicle's states and its perception of the environment. By incorporating vehicle state information into the decision-making process, our approach ensures that the model can dynamically adjust its attention to critical sensory inputs based on real-time driving conditions. This allows the autonomous system to make more context-aware decisions, improving its ability to respond effectively to complex and safety-critical scenarios.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Li2025c,
title = {PrefDrive: Enhancing Autonomous Driving through Preference-Guided Large Language Models},
author = {Yun Li and Ehsan Javanmardi and Simon Thompson and Kai Katsumata and Alex Orsholits and Manabu Tsukada},
url = {https://github.com/LiYun0607/PrefDrive/
https://huggingface.co/liyun0607/PrefDrive
https://huggingface.co/datasets/liyun0607/PrefDrive},
year = {2025},
date = {2025-06-22},
urldate = {2025-06-22},
booktitle = {36th IEEE Intelligent Vehicles Symposium (IV2025)},
address = {Cluj-Napoca, Romania},
abstract = {This paper presents PrefDrive, a novel framework that integrates driving preferences into autonomous driving models through large language models (LLMs). While recent advances in LLMs have shown promise in autonomous driving, existing approaches often struggle to align with specific driving behaviors (e.g., maintaining safe distances, smooth acceleration patterns) and operational requirements (e.g., traffic rule compliance, route adherence). We address this challenge by developing a preference learning framework that combines multimodal perception with natural language understanding. Our approach leverages Direct Preference Optimization (DPO) to fine-tune LLMs efficiently on consumer-grade hardware, making advanced autonomous driving research more accessible to the broader research community. We introduce a comprehensive dataset of 74,040 sequences, carefully annotated with driving preferences and driving decisions, which, along with our trained model checkpoints, will be made publicly available to facilitate future research. Through extensive experiments in the CARLA simulator, we demonstrate that our preference-guided approach significantly improves driving performance across multiple metrics, including distance maintenance and trajectory smoothness. Results show up to 28.1% reduction in traffic rule violations and 8.5% improvement in navigation task completion while maintaining appropriate distances from obstacles. The framework demonstrates robust performance across different urban environments, showcasing the effectiveness of preference learning in autonomous driving applications. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Jiang2025,
title = {Towards Efficient Roadside LiDAR Deployment: A Fast Surrogate Metric Based on Entropy-Guided Visibility},
author = {Yuze Jiang and Ehsan Javanmardi and Manabu Tsukada and Hiroshi Esaki},
url = {https://arxiv.org/abs/2504.06772},
year = {2025},
date = {2025-06-22},
urldate = {2025-06-22},
booktitle = {36th IEEE Intelligent Vehicles Symposium (IV2025)},
address = {Cluj-Napoca, Romania},
abstract = {The deployment of roadside LiDAR sensors plays a crucial
role in the development of Cooperative Intelligent
Transport Systems (C-ITS). However, the high cost of LiDAR
sensors necessitates efficient placement strategies to
maximize detection performance. Traditional roadside LiDAR
deployment methods rely on expert insight, making them
time-consuming. Automating this process, however, demands
extensive computation, as it requires not only visibility
evaluation but also assessing detection performance across
different LiDAR placements. To address this challenge, we
propose a fast surrogate metric, the Entropy-Guided
Visibility Score (EGVS), based on information gain to
evaluate object detection performance in roadside LiDAR
configurations. EGVS leverages Traffic Probabilistic
Occupancy Grids (TPOG) to prioritize critical areas and
employs entropy-based calculations to quantify the
information captured by LiDAR beams. This eliminates the
need for direct detection performance evaluation, which
typically requires extensive labeling and computational
resources. By integrating EGVS into the optimization
process, we significantly accelerate the search for optimal
LiDAR configurations. Experimental results using the AWSIM
simulator demonstrate that EGVS strongly correlates with
Average Precision (AP) scores and effectively predicts
object detection performance. This approach offers a
computationally efficient solution for roadside LiDAR
deployment, facilitating scalable smart infrastructure
development. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Lin2024c,
title = {A Rule-Compliance Path Planner for Lane-Merge Scenarios Based on Responsibility-Sensitive Safety},
author = {Pengfei Lin and Ehsan Javanmardi and Yuze Jiang and Manabu Tsukada},
doi = {10.1109/ICARCV63323.2024.10821557},
year = {2024},
date = {2024-12-12},
urldate = {2024-12-12},
booktitle = {2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV)},
address = {Dubai, UAE},
abstract = {Lane merging is one of the critical tasks for selfdriving cars, and how to perform lane-merge maneuvers effectively and safely has become one of the important standards
in measuring the capability of autonomous driving systems.
However, due to the ambiguity in driving intentions and
right-of-way issues, the lane merging process in autonomous
driving remains deficient in terms of maintaining or ceding
the right-of-way and attributing liability, which could result
in protracted durations for merging and problems such as
trajectory oscillation. Hence, we present a rule-compliance
path planner (RCPP) for lane-merge scenarios, which initially
employs the extended responsibility-sensitive safety (RSS) to
elucidate the right-of-way, followed by the potential field-based
sigmoid planner for path generation. In the simulation, we have
validated the efficacy of the proposed algorithm. The algorithm
demonstrated superior performance over previous approaches
in aspects such as merging time (Saved 72.3%), path length
(reduced 53.4%), and eliminating the trajectory oscillation.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Chauhan2024b,
title = {Connected Shared Spaces: Expert Insights into the Impact of eHMI and SPIU for Next-Generation Pedestrian-AV Communication},
author = {Vishal Chauhan and Anubhav Anubhav and Chia-Ming Chang and Jin Nakazato and Ehsan Javanmardi and Alex Orsholits and Takeo Igarashi and Kantaro Fujiwara and Manabu Tsukada},
year = {2024},
date = {2024-11-28},
urldate = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Nakazato2024,
title = {Toward 6G Mobility Networks: A Proposal for Cell-Free Cooperative Distributed Beamforming},
author = {Jin Nakazato and Sojin Ozawa and Yuki Sasaki and Kengo Suzuki and Kazuki Maruta andTetsuya Iye and Yuki Susukida and Eisaku Sato and Manabu Tsukada},
year = {2024},
date = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Kambara2024,
title = {Geographic-Aware Network Analysis and Visualization System for CAVs},
author = {Koichi Kambara and Ehsan Javanmardi and Jin Nakazato and Shunya Yamada and Hiroaki Takada and Yousuke Watanabe and Kenya Sato and Manabu Tsukada},
year = {2024},
date = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Dolatabadi2024,
title = {Neural Error Covariance Estimation for Precise LIDAR Localization},
author = {Minoo Dolatabadi and Fardin Ayar and Ehsan Javanmardi and Manabu Tsukada and Mahdi Javanmardi},
url = {https://arxiv.org/abs/2501.02558},
year = {2024},
date = {2024-11-28},
urldate = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Zakerian2024,
title = {Unsupervised Person re-identification Using Generative Adversarial Networks},
author = {Romina Zakerian and Ehsan Javanmardi and Manabu Tsukada and Mahdi Javanmardi and Mohammad Rahmati},
year = {2024},
date = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Ayar2024,
title = {LiDAR-Camera Fusion for Video Panoptic Segmentation without Video Training},
author = {Fardin Ayar and Ehsan Javanmardi and Manabu Tsukada and Mahdi Javanmardi and Mohammad Rahmati},
url = {https://arxiv.org/abs/2412.20881},
year = {2024},
date = {2024-11-28},
urldate = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
abstract = {Panoptic segmentation, which combines instance and semantic segmentation, has gained a lot of attention in autonomous vehicles, due to its comprehensive representation of the scene. This task can
be applied for cameras and LiDAR sensors, but there has been a limited focus on combining both sensors to enhance image panoptic segmentation (PS). Although previous research has acknowledged the benefit of 3D data on camera-based scene perception, no specific study has explored the influence of 3D data on image and video panoptic segmentation (VPS). This work seeks to introduce a feature fusion module that enhances PS and VPS by fusing LiDAR and image data for autonomous vehicles. We also illustrate that, in
addition to this fusion, our proposed model, which utilizes two simple modifications, can further deliver even more high-quality VPS without being trained on video data. The results demonstrate a substantial improvement in both the image and video panoptic segmentation evaluation metrics by up to 5 points.},
note = {Best Paper Award (Bronze)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{nokey,
title = {Where Do You Go? Pedestrian Trajectory Prediction using Scene Features},
author = {Mohammad Ali Rezaei and Fardin Ayar and Ehsan Javanmardi and Manabu Tsukada and Mahdi Javanmardi},
url = {https://arxiv.org/abs/2501.13848},
year = {2024},
date = {2024-11-28},
urldate = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Li2024,
title = {Cross-Attention Enhanced Imitation Learning for End-to-end Autonomous Driving in Unprotected Turns},
author = {Dongyang Li and Ehsan Javanmardi and Naren Bao and Manabu Tsukada},
year = {2024},
date = {2024-11-28},
urldate = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
abstract = {Performing an unprotected turn in the intersection is a complex scenario for autonomous vehicles. It not only requires a comprehensive understanding of the surrounding environment but also highly relies on the ego vehicle’s current state to make safe decisions. A conventional way to learn end-to-end autonomous driving is imitation learning, which is learning from expert demonstrations. While most imitation learning methods focus on imitating the expert action, they often fail to imitate a complex policy efficiently when the ego vehicle’s states are crucial to the scenario because there might be arbitrary optimal actions under different states. To address this issue and investigate how vehicle states affect autonomous driving, we present a novel cross-attention enhanced imitation learning approach for end-to-end autonomous driving in unprotected turns, focusing on capturing the relationships between the ego vehicle’s states and its perception of the environment. We evaluate our model in AWSIM, an open-source autonomous driving
simulator, and the results demonstrate that our model outperformed conventional imitation learning-based baselines in performing unprotected turn scenarios, showcasing its ability to imitate a complex policy efficiently.},
note = {Best Paper Award (Silver)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Orsholits2024b,
title = {PLATONE: Assessing Simulation Accuracy of Environment-Dependent Audio Spatialization},
author = {Alex Orsholits and Eric Nardini and Tsukada Manabu},
year = {2024},
date = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Gui2024b,
title = {"Text + Eye" on Autonomous Taxi to Provide Geospatial Instructions to Passenger},
author = {Xinyue Gui and Ehsan Javanmardi and Stela Hanbyeol Seo and Vishal Chauhan and Chia-Ming Chang and Manabu Tsukada and Takeo Igarashi},
doi = {10.1145/3687272.3690906},
year = {2024},
date = {2024-11-24},
urldate = {2024-11-24},
booktitle = {Proceedings of the 12th International Conference on Human-Agent Interaction(HAI 2024)},
pages = {429-431},
address = {Swansea University, UK},
abstract = {While text-based external human-machine interface (eHMI) is widely accepted, one limitation is the lack of capability to communicate spatial information such as a different person or location. We built a mixed-eHMI using "eye" as a target-specifier when "text" shows the clear intention to their communication partners. We conducted a pre-experimental observation to develop two testbed scenarios, followed by a video-based user study via life-size projection with a real-car prototype mounted a text display and a set of robotic eyes. The results demonstrated that our proposed "text + eye" combination may represent geospatial information by increasing the success pick-up rate.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Grosset2024,
title = {Generation of V2X messages from Carla Simulator for cooperative perception: Application to pedestrian safety},
author = {Juliette Grosset and Jean-Marie Bonnin and Alain-Jérôme Fougères and Manabu Tsukada and Moise Djoko-Kouam},
doi = {10.1109/VTC2024-Fall63153.2024.10757467},
year = {2024},
date = {2024-10-07},
urldate = {2024-10-07},
booktitle = {The IEEE 100th Vehicular Technology Conference (VTC2024-Fall)},
address = {Washington DC, USA},
abstract = {Despite advancements in connected and autonomous vehicles (CAVs), vulnerable road users (VRUs) face a challenge as they lack Communication-Intelligent Transport System (C-ITS) equipment. This deficiency impedes their interaction with CAVs. We underscore the significance of Vehicle-to-Everything (V2X) communication in enhancing road safety with VRUs by facilitating information exchange between CAVs and the infrastructure. This communication is pivotal for reintegrating VRUs into the environmental awareness of CAVs. The Carla Simulator, used for autonomous vehicle training, currently lacks comprehensive V2X communication capabilities. In response, we propose an architecture for Carla, integrating OpenCDA and ROS2 to establish a simulated V2X network communication system for CAVs and roadside units (RSUs) within the Carla environment. This setup allows for the generation of V2X datasets and the refinement of algorithms for Advanced Driver Assistance Systems (ADAS). To illustrate and assess our proposed architecture, we present a scenario involving a pedestrian concealed in a blind spot for a connected vehicle.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Yun2024,
title = {Large Language Models for Human-like Autonomous Driving Decision Making: A Survey},
author = {Yun Li and Kai Katsumata and Ehsan Javanmardi and Manabu Tsukada},
doi = {10.1109/ITSC58415.2024.10919629},
year = {2024},
date = {2024-09-24},
urldate = {2024-09-24},
booktitle = {27th IEEE International Conference on Intelligent Transportation Systems (ITSC 2024)},
address = {Edmonton, Canada},
abstract = {Large Language Models (LLMs), AI models trained on massive text corpora with remarkable language understanding and generation capabilities, are transforming the field of Autonomous Driving (AD). As AD systems evolve from rule-based and optimization-based methods to learning-based techniques like deep reinforcement learning, they are now poised to embrace a third and more advanced category: knowledge-based AD empowered by LLMs. This shift promises to bring AD closer to human-like AD. However, integrating LLMs into AD systems poses challenges in real-time inference, safety assurance, and deployment costs. This survey provides a comprehensive and critical review of recent progress in leveraging LLMs for AD, focusing on their applications in modular AD pipelines and end- to-end AD systems. We highlight key advancements, identify pressing challenges, and propose promising research directions to bridge the gap between LLMs and AD, thereby facilitating the development of more human-like AD systems. The survey first introduces LLMs’ key features and common training schemes, then delves into their applications in modular AD pipelines and end-to-end AD, respectively, followed by discussions on open challenges and future directions. Through this in-depth analysis, we aim to provide insights and inspiration for researchers and practitioners working at the intersection of AI and autonomous vehicles, ultimately contributing to safer, smarter, and more human-centric AD technologies.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Jiang2024b,
title = {Accurate Cooperative Localization Utilizing LiDAR-equipped Roadside Infrastructure for Autonomous Driving},
author = {Yuze Jiang and Ehsan Javanmardi and Manabu Tsukada and Hiroshi Esaki},
url = {https://arxiv.org/abs/2407.08384 },
doi = {10.1109/ITSC58415.2024.10920101},
year = {2024},
date = {2024-09-24},
urldate = {2024-09-24},
booktitle = {27th IEEE International Conference on Intelligent Transportation Systems (ITSC 2024)},
address = {Edmonton, Canada},
abstract = {Recent advancements in LiDAR technology have significantly lowered costs and improved both its precision and resolution, thereby solidifying its role as a critical component in autonomous vehicle localization. Using sophisticated 3D reg- istration algorithms, LiDAR now facilitates vehicle localization with centimeter-level accuracy. However, these high-precision techniques often face reliability challenges in environments devoid of identifiable map features. To address this limitation, we propose a novel approach that utilizes road side units (RSU) with vehicle-to-infrastructure (V2I) communications to assist vehicle self-localization. By using RSUs as stationary reference points and processing real-time LiDAR data, our method enhances localization accuracy through a cooperative localization framework. By placing RSUs in critical areas, our proposed method can improve the reliability and precision of vehicle localization when the traditional vehicle self-localization technique falls short. Evaluation results in an end-to-end autonomous driving simulator AWSIM show that the proposed method can improve localization accuracy by up to 80% under vulnerable environments compared to traditional localization methods. Additionally, our method also demonstrates robust resistance to network delays and packet loss in heterogeneous network environments.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Gui2024,
title = {Shrinkable Arm-based eHMI on Autonomous Delivery Vehicle for Effective Communication with Other Road Users},
author = {Xinyue Gui and Mikiya Kusunoki and Bofei Huang and Stela Hanbyeol Seo and Chia-Ming Chang and Haoran Xie and Manabu Tsukada and Takeo Igarashi},
doi = {10.1145/3640792.3675716},
year = {2024},
date = {2024-09-22},
urldate = {2024-09-22},
booktitle = {16th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI 2024)},
address = {California, USA},
abstract = {When employing autonomous driving technology in logistics, small autonomous delivery vehicles (aka delivery robots) encounter challenges different from passenger vehicles when interacting with other road users. We conducted an online video survey as a pre-study and found that autonomous delivery vehicles need external human-machine interfaces (eHMIs) to ask for help due to their small size and functional limitations. Inspired by everyday human communication, we chose arms as eHMI to show their request through limb motion and gesture. We held an in-house workshop to identify the arm’s requirements for designing a specific arm with shrink-ability (conspicuous when delivering messages but not affect traffic at other times). We prototyped a small delivery robot with a shrinkable arm and filmed the experiment videos. We conducted two studies (a video-based and a 360-degree-photo VR-based) with 18 participants. We demonstrated that arm-on-delivery robots can increase interaction efficiency by drawing more attention and communicating specific information.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Chauhan2024,
title = {Transforming Pedestrian and Autonomous Vehicles Interactions in Shared Spaces: A Think-Tank Study on Exploring Human-Centric Designs},
author = {Vishal Chauhan and Anubhav Anubhav and Chia-Ming Chang and Jin Nakazato and Ehsan Javanmardi and Alex Orsholits and Takeo Igarashi and Kantaro Fujiwara and Manabu Tsukada
},
doi = {10.1145/3641308.3685037},
year = {2024},
date = {2024-09-22},
urldate = {2024-09-22},
booktitle = {16th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI 2024), Work in Progress (WiP)},
pages = {1-8},
address = {California, USA},
abstract = {Our research focuses on the smart pole interaction unit (SPIU) as an infrastructure external human-machine interface (HMI) to enhance pedestrian interaction with autonomous vehicles (AVs) in shared spaces. We extensively study SPIU with external human-machine interfaces (eHMI) on AVs as an integrated solution. To discuss interaction barriers and enhance pedestrian safety, we engaged 25 participants aged 18-40 to brainstorm design solutions for pedestrian-AV interactions, emphasising effectiveness, simplicity, visibility, and clarity. Findings indicate a preference for real-time SPIU interaction over eHMI on AVs in multiple AV scenarios. However, the combined use of SPIU and eHMI on AVs is crucial for building trust in decision-making. Consequently, we propose innovative design solutions for both SPIU and eHMI on AVs, discussing their pros and cons. This study lays the groundwork for future autonomous mobility solutions by developing human-centric eHMI and SPIU prototypes as ieHMI.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Trumpp2024,
title = {RaceMOP: Mapless Online Path Planning for Multi-Agent Autonomous Racing using Residual Policy Learning},
author = {Raphael Trumpp and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada and Marco Caccamo},
url = {http://github.com/raphajaner/racemop},
doi = {10.1109/IROS58592.2024.10801657},
year = {2024},
date = {2024-09-14},
urldate = {2024-09-14},
booktitle = {The 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)},
address = {Abu Dhabi ,UAE},
abstract = {The interactive decision-making in multi-agent autonomous racing offers insights valuable beyond the domain of self-driving cars. Mapless online path planning is particularly of practical appeal but poses a challenge for safely overtaking opponents due to the limited planning horizon. Accordingly, this paper introduces RaceMOP, a novel method for mapless online path planning designed for multi-agent racing of F1TENTH cars. Unlike classical planners that depend on predefined racing lines, RaceMOP operates without a map, relying solely on local observations to overtake other race cars at high speed. Our approach combines an artificial potential field method as a base policy with residual policy learning to introduce long-horizon planning capabilities. We advance the field by introducing a novel approach for policy fusion with the residual policy directly in probability space. Our experiments for twelve simulated racetracks validate that RaceMOP is capable of long-horizon decision-making with robust collision avoidance during over- taking maneuvers. RaceMOP demonstrates superior handling over existing mapless planners while generalizing to unknown racetracks, paving the way for further use of our method in robotics. We make the open-source code for RaceMOP available at http://github.com/raphajaner/racemop.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Iwaki2024b,
title = {Enhancing V2X Communication: Machine Learning Assisted Dynamic mmWave Beam Search},
author = {Ryo Iwaki and Jin Nakazato and Kazuki Maruta and Manabu Tsukada and Hideya Ochiai and Hiroshi Esaki},
doi = {10.1109/ICUFN61752.2024.10625435},
year = {2024},
date = {2024-07-02},
urldate = {2024-07-02},
booktitle = {The 15th International Conference on Ubiquitous and Future Networks (ICUFN2024)},
address = {Budapest, Hungary},
abstract = {Only the chairs can edit This paper addresses the challenges of dynamic beam search in millimeter wave (mmWave) communications for vehicle-to-everything (V2X) applications. With the rapid mobility of connected autonomous vehicles (CAVs) and dense urban environments, maintaining high-quality mmWave connections is critical for the reliability and efficiency of V2X communications. We propose a novel machine learning-assisted framework for dynamic mmWave beam search, which significantly enhances the adaptability and performance of V2X communication systems. Our approach leverages real-time environmental data and CAV dynamics to predict optimal beam directions, improving connection stability. Simulation results demonstrate the effectiveness of the proposed method in a real-world road scenario, offering a partial improvement over conventional beam search techniques.},
note = {Best Paper Award},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Yoshimura2024,
title = {Towards Robust Communication in ITS: A Comprehensive Study of Blockchain for V2I},
author = {Atsuki Yoshimura and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
doi = {10.1109/ICUFN61752.2024.10624948},
year = {2024},
date = {2024-07-02},
urldate = {2024-07-02},
booktitle = {The 15th International Conference on Ubiquitous and Future Networks (ICUFN2024)},
address = {Budapest, Hungary},
abstract = {V2X in connected autonomous vehicles (CAV) plays an important role in information sharing through communication. The integration of V2X and blockchain has the potential to create functionalities such as seamless V2X information sharing, similar to Bitcoin, and post-accident investigation utilities that leverage data immutability. However, the integration of blockchain into V2X communication requires addressing CAV mobility. In this study, we propose a framework that takes into account the high mobility of CAVs. Furthermore, we propose a method that not only addresses this challenge but also achieves load balancing by facilitating cooperation among nodes responsible for member management. In this paper, we integrate the ITS simulator, the communication simulator, and the blockchain simulator to build an infrastructure that can be evaluated end-to-end. Using the integrated simulator, we perform an evaluation based on metrics such as latency and member change rate in a mobile environment with a single roadside unit (RSU). In the future, we plan to implement the proposed methodology and perform evaluations in environments with multiple RSUs.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Chi2024,
title = {V2I Blockage Modeling and Performance Evaluation for Connected Autonomous Vehicle},
author = {Weiqi Chi and Jin Nakazato and Tomoki Murakami and Manabu Tsukada},
doi = {10.1109/VTC2024-Spring62846.2024.10683381},
year = {2024},
date = {2024-06-24},
urldate = {2024-06-24},
booktitle = {The IEEE 99th Vehicular Technology Conference (VTC2024-Spring)},
address = {Singapore},
abstract = {The burgeoning Intelligent Transportation System (ITS) spurs global technological advancements, notably in innovative community development through vehicle-to-everything (V2X) communication. This study focuses on the high data rates and low latency offered by a millimeter-wave (mmWave) enabled vehicular network while addressing the significant challenge of link quality degradation due to blockages, exacerbated by the mmWave band’s small wavelength in high mobility and traffic conditions. We propose an RSU-assisted ITS system tailored for multi-lane, straight-road scenarios, effectively identifying blockage status for vehicles. Combining Simulation of Urban Mobility (SUMO) and MATLAB, this blockage-aware scheme lays the groundwork for future ITS enhancements. The research also delves into the effects of various frequency bands, vehicle types, and communication ranges, offering a holistic system performance analysis.},
note = {IEEE VTS Tokyo/Japan Chapter Young Researcher's Encouragement Award},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Ito2024,
title = {Enhancing Real-Time Streaming Quality through a Multipath Redundant Communication Framework},
author = {Koki Ito and Jin Nakazato and Romain Fontugne and Manabu Tsukada and Hiroshi Esaki},
doi = {10.23919/IFIPNetworking62109.2024.10619885},
year = {2024},
date = {2024-06-03},
urldate = {2024-06-03},
booktitle = {IFIP/IEEE Networking 2024},
address = {Thessaloniki, Greece},
abstract = {Recently, as networks operate as the infrastructure of modern society, the demands placed on the network by applications have become more complex. In particular, an increasing annual demand for high-capacity and low-latency services, including real-time streaming. 5G services have been launched to meet this demand, but their stability varies de- pending on location and time and can only sometimes be considered sufficient. One method to improve communication stability is multipath redundant communication, and much research has been conducted in this area. However, most of this research has focused on TCP-based communication and cannot be applied to real-time UDP streaming. Hence, we propose a multipath redundant communication framework to improve the quality of real-time media streaming communication. Tunneling at the IP layer in our proposed framework was performed to overcome the limitations of transport layer protocols, which was a challenge for traditional multipath redundant communication systems. Furthermore, to address the packet order inconsistency caused by multipath redundant communication, a buffering mechanism was implemented on the receiving side of our system. Our proposed system was verified using multipath redundant communication and multiple mobile networks from a vehicle moving in an urban area. The experiments used a real-time streaming application based on WebRTC, and the framework significantly reduced packet loss and improved bitrate compared to existing multipath redundant communication systems without interfering with the application’s congestion control.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tao2024b,
title = {Zero-Knowledge Proof of Distinct Identity: a Standard-compatible Sybil-resistant Pseudonym Extension for C-ITS},
author = {Ye Tao and Hongyi Wu and Ehsan Javanmardi and Manabu Tsukada and Hiroshi Esaki},
url = {https://arxiv.org/abs/2403.14020},
doi = {10.1109/IV55156.2024.10588511},
year = {2024},
date = {2024-06-02},
urldate = {2024-06-02},
booktitle = {35th IEEE Intelligent Vehicles Symposium (IV2024)},
address = {Jeju Island, Korea},
abstract = {Pseudonyms are widely used in Cooperative Intelligent Transport Systems (C-ITS) to protect the location privacy of vehicles. However, the unlinkability nature of pseudonyms also enables Sybil attacks, where a malicious vehicle can pretend to be multiple vehicles at the same time. In this paper, we propose a novel protocol called zero-knowledge Proof of Distinct Identity (zk-PoDI,) which allows a vehicle to prove that it is not the owner of another pseudonym in the local area, without revealing its actual identity. Zk-PoDI is based on the Diophantine equation and zk-SNARK, and does not rely on any specific pseudonym design or infrastructure assistance. We show that zk-PoDI satisfies all the requirements for a practical Sybil-resistance pseudonym system, and it has low latency, adjustable difficulty, moderate computation overhead, and negligible communication cost. We also discuss the future work of implementing and evaluating zk-PoDI in a realistic city-scale simulation environment.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Maruta2024,
title = {Millimeter-Wave Fast Beam Tracking Enabled by RAN/V2X Cooperation},
author = {Kazuki Maruta and Jin Nakazato and Kengo Suzuki and Dou Hu and Ryo Iwaki and Sojin Ozawa and Yuki Sasaki and Hideya So and Manabu Tsukada},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/376818826_Millimeter-Wave_Fast_Beam_Tracking_Enabled_by_RANV2X_Cooperation/links/658ac62e6f6e450f19a60664/Millimeter-Wave-Fast-Beam-Tracking-Enabled-by-RAN-V2X-Cooperation.pdf},
doi = {10.1109/ICAIIC60209.2024.10463482},
year = {2024},
date = {2024-02-19},
urldate = {2024-02-19},
booktitle = {International Conference on Artificial Intelligence in Information and Communication (ICAIIC 2024)},
address = {Osaka, Japan},
abstract = {Only the chairs can edit Automated driving has the same limitations as human drivers because it functions as a replacement for humans and operates based on local information using onboard sensors and computers. Cooperative automated driving is expected to achieve both safety and efficiency, which could not be achieved by imitating human driving, by sharing sensor information from roadside equipment and other vehicles. Since such sensor information is enormous, it is desirable to utilize millimeter-waves, which are capable of high-capacity transmission. However, wireless communication systems for cooperative automated driving have the challenge of radio quality degradation due to vehicle movement. Our research project aims to realize stable millimeter-wave transmission by incorporating Open RAN (O-RAN) and vehicle-to-everything (V2X) functions. This paper presents the overall proposed concept and an example of validation; we show the results of evaluating our previously proposed fast beam following scheme in a handover environment with multiple roadside units.},
note = {Excellent Paper Award},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Jiang2023,
title = {Roadside LiDAR Assisted Cooperative Localization for Connected Autonomous Vehicles},
author = {Yuze Jiang and Ehsan Javanmard and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
url = {https://arxiv.org/abs/2311.07913},
year = {2023},
date = {2023-12-14},
urldate = {2023-12-14},
booktitle = {ACM Intelligent Computing and its Emerging Applications (ICEA 2023)},
abstract = {Advancements in LiDAR technology have led to more cost-effective production while simultaneously improving precision and resolution. As a result, LiDAR has become integral to vehicle localization, achieving centimeter-level accuracy through techniques like Normal Distributions Transform (NDT) and other advanced 3D registration algorithms. Nonetheless, these approaches are reliant on high-definition 3D point cloud maps, the creation of which involves significant expenditure. When such maps are unavailable or lack sufficient features for 3D registration algorithms, localization accuracy diminishes, posing a risk to road safety. To address this, we proposed to use LiDAR-equipped roadside unit and Vehicle-to-Infrastructure (V2I) communication to accurately estimate the connected vehicle's position and help the vehicle when its self-localization is not accurate enough. Our simulation results indicate that this method outperforms traditional NDT scan matching-based approaches in terms of localization accuracy.},
note = {Best paper award (Silver)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Matsumoto2023b,
title = {Localizability Estimation for Autonomous Driving: A Deep Learning-Based Place Recognition Approach},
author = {Kazuto Matsumoto and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada},
doi = {10.1109/IRC59093.2023.00052},
year = {2023},
date = {2023-12-11},
urldate = {2023-12-11},
booktitle = {IEEE Robotic Computing 2023},
address = {California, USA},
abstract = {In recent years, research and development aimed at the societal implementation of autonomous driving have attracted increasing attention. Localization, which involves obtaining in- formation about the surrounding environment from sensor data and estimating the vehicle’s position, is necessary for realizing autonomous driving. Localization is commonly performed with 3D LiDAR as a sensor owing to its high measurement accuracy and immunity to ambient light conditions, which allow for precise localization. However, localization accuracy may decrease when the surrounding area does not have distinctive features. In this study, we proposed a method based on deep learning to estimate localization accuracy for autonomous driving. The overall localization accuracy can be improved by estimating the accuracy of localization using other sensors, such as GNSS and IMU, or pavement markings in areas with poor accuracy. We created a dataset for estimating localization accuracy using an open-source autonomous driving simulator. In an experiment, we applied the proposed method to the created dataset. Estimations with low MSE were obtained. The results indicate that the proposed method can accurately estimate localization accuracy.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Lin2023c,
title = {Potential Field-based Path Planning with Interactive Speed Optimization for Autonomous Vehicles},
author = {Pengfei Lin and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada},
url = {https://arxiv.org/abs/2306.06987},
doi = {10.1109/IECON51785.2023.10311890},
year = {2023},
date = {2023-10-16},
urldate = {2023-10-16},
booktitle = {49th Annual Conference of the IEEE Industrial Electronics Society (IECON 2023)},
abstract = {Path planning is critical for autonomous vehicles (AVs) to determine the optimal route while considering constraints and objectives. The potential field (PF) approach has become prevalent in path planning due to its simple structure and computational efficiency. However, current PF methods used in AVs focus solely on the path generation of the ego vehicle while assuming that the surrounding obstacle vehicles drive at a preset behavior without the PF-based path planner, which ignores the fact that the ego vehicle’s PF could also impact the path generation of the obstacle vehicles. To tackle this problem, we propose a PF-based path planning approach where local paths are shared among ego and obstacle vehicles via vehicle-to- vehicle (V2V) communication. Then by integrating this shared local path into an objective function, a new optimization function called interactive speed optimization (ISO) is designed to allow driving safety and comfort for both ego and obstacle vehicles. The proposed method is evaluated using MATLAB/Simulink in the urgent merging scenarios by comparing it with conventional methods. The simulation results indicate that the proposed method can mitigate the impact of other AVs’ PFs by slowing down in advance, effectively reducing the oscillations for both ego and obstacle AVs.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Chauhan2023,
title = {Keep Calm and Cross: Smart Pole Interaction Unit for Easing Pedestrian Cognitive Load},
author = {Vishal Chauhan and Chia-Ming Chang and Ehsan Javanmardi and Jin Nakazato and Koki Toda and Pengfei Lin and Takeo Igarashi and Manabu Tsukada},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/374582122_Keep_Calm_and_Cross_Smart_Pole_Interaction_Unit_for_Easing_Pedestrian_Cognitive_Load/links/6525681eb32c91681fb2e1b5/Keep-Calm-and-Cross-Smart-Pole-Interaction-Unit-for-Easing-Pedestrian-Cognitive-Load.pdf},
doi = {10.1109/WF-IoT58464.2023.10539511},
year = {2023},
date = {2023-10-12},
urldate = {2023-10-12},
booktitle = {The 9th IEEE World Forum on Internet of Things (IEEE WFIoT2023)},
address = {Aveiro, Portugal},
abstract = {Recently, there has been a growing emphasis on autonomous vehicles (AVs), and as they coexist with pedestrians, ensuring pedestrian safety at crosswalks has become paramount. While AVs exhibit commendable performance on traditional roads with established traffic infrastructure, their interaction in different environments, such as shared spaces lacking traffic lights or sign rules (also known as naked streets), can present significant challenges, including right-of-way and accessibility concerns. To address these challenges, this study proposes a novel approach to enhance pedestrian safety in shared spaces, focusing on the proposed smart pole interaction unit (SPIU) combined with an external human-machine interface (eHMI). By evaluating the proposal of SPIU developed by a virtual reality system, we explore its usability and effectiveness in facilitating vehicle-to-pedestrian (V2P) interactions at crosswalks. Our findings from this study showed that SPIU facilitates safe, quicker decision-making to stop and pass at crosswalks in shared space and reduces cognitive load compared to scenarios where an SPIU is absent for pedestrians and reduce the need for eHMI to see on multiple AVs. The SPIU addition with the eHMI in vehicles yields a noteworthy 21 % improvement in response time, enhancing efficiency during pedestrian stops. In both scenarios, whether with a single AV (1-way) or multiple AVs (2-way), SPIU has a positive impact on interaction dynamics and statistically demonstrates a significant improvement (p = 0.001). },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Bao2023,
title = {Personalized Causal Factor Generalization for Subjective Risky Scene Understanding with Vision Transformer},
author = {Naren Bao and Alexander Carballo and Manabu Tsukada and Kazuya Takeda},
doi = {10.1109/ITSC57777.2023.10422148},
year = {2023},
date = {2023-09-24},
urldate = {2023-09-24},
booktitle = {The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)},
address = {Bilbao, Bizkaia, Spain},
abstract = {This paper presents a framework to understanding subjective driving scene perception by Vision Transformer for Environmental Feature Extraction within a Causal Modeling Analysis method. By leveraging vision transformer models, informative features are extracted from video camera images capturing the surrounding environment. Through the causal analysis, the causal effects of these variables on subjective risk perception are explored, shedding light on the factors influencing individuals' perception of driving risk. The findings demonstrate understanding of environmental features and individual difference on risk perception, providing a deeper understanding of risky scene perception. The paper concludes with this approach unifies selective attentional phenomena can improve the scene understanding for subjective perception in real-world driving scenarios aiming to enhance driving safety based on the identified causal factors. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Yamazaki2023,
title = {ToST: Tokyo SUMO traffic scenario },
author = {Yuji Yamazaki and Yasumasa Tamura and Xavier Defago and Ehsan Javanmardi and Manabu Tsukada},
url = {https://github.com/dfg-lab/ToSTScenario},
doi = {10.1109/ITSC57777.2023.10422517},
year = {2023},
date = {2023-09-24},
urldate = {2023-09-24},
booktitle = {The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)},
address = {Bilbao, Bizkaia, Spain},
abstract = {In recent years, research, development, and demonstrations aimed at the societal implementation of autonomous driving have attracted increasing attention. Localization, which involves obtaining information of the surrounding environment from sensor data and estimating the vehicle's position, is necessary for realizing autonomous driving. Localization is commonly performed with 3D LiDAR as a sensor owing to its high measurement accuracy and immunity to ambient light conditions, which allow for precise localization. However, when the surrounding area has distinctive features, localization accuracy may decrease. In this study, we proposed a method based on deep learning to predict the localization accuracy for autonomous driving. The overall localization accuracy can be improved by predicting the accuracy of localization using other sensors, such as GNSS and IMU, or pavement markings in areas with poor accuracy. We created a dataset for predicting the localization accuracy using an open-source autonomous driving simulator. In an experiment, we applied the proposed method to the created dataset. Thresholds were set for errors in the x-direction, y-direction, and distance for localization. Predictions with high accuracy and F-values were obtained. The results indicate that the proposed method can accurately predict the localization accuracy. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Lin2023b,
title = {Occlusion-Aware Path Planning for Collision Avoidance: Leveraging Potential Field Method with Responsibility-Sensitive Safety},
author = {Pengfei Lin and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada},
url = {https://arxiv.org/abs/2306.06981},
doi = {10.1109/ITSC57777.2023.10422621},
year = {2023},
date = {2023-09-24},
urldate = {2023-09-24},
booktitle = {The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)},
series = {Bilbao, Bizkaia, Spain},
abstract = {Collision avoidance (CA) has always been the foremost task for autonomous vehicles (AVs) under safety criteria. And path planning is directly responsible for generating a safe path to accomplish CA while satisfying other commands. Due to the real-time computation and simple structure, the potential field (PF) has emerged as one of the mainstream path-planning algorithms. However, the current PF is primarily simulated in ideal CA scenarios, assuming complete obstacle information while disregarding occlusion issues where obstacles can be partially or entirely hidden from the AV's sensors. During the occlusion period, the occluded obstacles do not possess a PF. Once the occlusion is over, these obstacles can generate an instantaneous virtual force that impacts the ego vehicle. Therefore, we propose an occlusion-aware path planning (OAPP) with the responsibility-sensitive safety (RSS)-based PF to tackle the occlusion problem for non-connected AVs. We first categorize the detected and occluded obstacles, and then we proceed to the RSS violation check. Finally, we can generate different virtual forces from the PF for occluded and non-occluded obstacles. We compare the proposed OAPP method with other PF-based path planning methods via MATLAB/Simulink. The simulation results indicate that the proposed method can eliminate instantaneous lateral oscillation or sway and produce a smoother path than conventional PF methods.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tao2023c,
title = {Flowsim: A Modular Simulation Platform for Microscopic Behavior Analysis of City-Scale Connected Autonomous Vehicles},
author = {Ye Tao and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
url = {https://github.com/tlab-wide/flowsim
https://arxiv.org/abs/2306.05738},
doi = {10.1109/ITSC57777.2023.10421900},
year = {2023},
date = {2023-09-24},
urldate = {2023-09-24},
booktitle = {The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)},
address = {Bilbao, Bizkaia, Spain},
abstract = {As connected autonomous vehicles (CAVs) become increasingly prevalent, there is a growing need for simulation platforms that can accurately evaluate CAV behavior in large-scale environments. In this paper, we propose Flowsim, a novel simulator specifically designed to meet these requirements. Flowsim offers a modular and extensible architecture that enables the analysis of CAV behaviors in large-scale scenarios. It provides researchers with a customizable platform for studying CAV interactions, evaluating communication and networking protocols, assessing cybersecurity vulnerabilities, optimizing traffic management strategies, and developing and evaluating policies for CAV deployment. Flowsim is implemented in pure Python in approximately 1,500 lines of code, making it highly readable, understandable, and easily modifiable. We verified the functionality and performance of Flowsim via a series of experiments based on realistic traffic scenarios. The results show the effectiveness of Flowsim in providing a flexible and powerful simulation environment for evaluating CAV behavior and data flow. Flowsim is a valuable tool for researchers, policymakers, and industry professionals who are involved in the development, evaluation, and deployment of CAVs. The code of Flowsim is publicly available on GitHub under the MIT license. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Yosodipuro2023,
title = {Mixed-traffic Intersection Management using Traffic-load-responsive Reservation and V2X-enabled Speed Coordination},
author = {Nicholaus Danispadmanaba Yosodipuro and Ehsan Javanmardi and Jin Nakazato and Yasumasa Tamura and Xavier Defago and Manabu Tsukada},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/374470825_Mixed-traffic_Intersection_Management_using_Traffic-load-responsive_Reservation_and_V2X-enabled_Speed_Coordination/links/651ee8d63ab6cb4ec6bde79a/Mixed-traffic-Intersection-Management-using-Traffic-load-responsive-Reservation-and-V2X-enabled-Speed-Coordination.pdf},
doi = {10.1109/ITSC57777.2023.10422248},
year = {2023},
date = {2023-09-24},
urldate = {2023-09-24},
booktitle = {The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)},
address = {Bilbao, Bizkaia, Spain},
abstract = {Vehicle-to-everything (V2X) communication enables connected autonomous vehicles (CAVs) to share information and generate optimal decisions. The networking abilities of CAVs have led to the development of unsignalized autonomous intersection management (AIM) methods that leverage CAVs to significantly improve traffic flows. However, AIM methods assume 100% CAV market penetration, which is currently unrealistic owing to the gradual adoption of CAVs. Therefore, CAVs must share road usage with nonconnected vehicles (NCVs). Thus, we propose a mixed-traffic intersection management method that considers NCVs while ensuring high traffic flow, called traffic-load-responsive reservation for intersection management (TLRRIM). In TLRRIM, the roadside unit (RSU) first classifies vehicles and groups them into clusters before selecting a reservation cluster to cross an intersection. The reservation cluster selection considers both traffic load and crossing urgency. In addition, the RSU utilizes V2X-enabled speed coordination (VESC) for CAVs within the reservation cluster to further improve traffic flow, while utilizing traffic lights to guide NCVs. Simulation-based experiments using OpenCDA and CARLA showed that TLRRIM can increase throughput and reduce waiting time by up to 89.63% and 60.71%, respectively, compared with the fixed-time signaling method. Moreover, adding VESC can increase throughput by 12.21% and reduce waiting time by 10.80%, further enhancing traffic flow. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Wang2023,
title = {Overcoming Environmental Challenges in CAVs Through MEC-Based Federated Learning},
author = {Zekun Wang and Jin Nakazato and Muhammad Asad and Ehsan Javanmardi and Manabu Tsukada},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/371685830_Overcoming_Environmental_Challenges_in_CAVs_through_MEC-based_Federated_Learning/links/64a420ea8de7ed28ba7465c7/Overcoming-Environmental-Challenges-in-CAVs-through-MEC-based-Federated-Learning.pdf},
doi = {10.1109/ICUFN57995.2023.10200688},
year = {2023},
date = {2023-07-04},
urldate = {2023-07-04},
booktitle = {14th International Conference on Ubiquitous and Future Networks (ICUFN2023)},
pages = {1-6},
address = {Paris, France},
abstract = {Connected autonomous vehicles (CAVs), through vehicle-to-everything communication and computing resources, enable the vital exchange of information. Although deep learning is crucial in this landscape, it requires extensive and intricate datasets covering all potential scenarios. Furthermore, this situation poses a hazard, as the likelihood of accidents associated with imbalanced datasets increases, particularly in scenarios where processing analysis is compromised due to fluctuating weather conditions. We propose a Federated Learning (FL) framework undergirded by Multi-Access Edge Computing (MEC) to counter these challenges. This local device-focused framework enhances task-specific models' caching and continual updating across various conditions. In a more specific sense, edge nodes (ENs) operate as MEC, each caching multiple dedicated models and serving as the aggregator as part of the FL process. Additionally, we have engineered two innovative algorithms that categorize various states into multiple classes, thereby ensuring the efficient utilization of computing resources in ENs. Simulation results substantiate the effectiveness of our approach, showing that the proposed dedicated model consistently outperforms a general model designed for all situations.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Asabe2023b,
title = {AutowareV2X: Reliable V2X Communication and Collective Perception for Autonomous Driving},
author = {Yu Asabe and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
url = {https://github.com/tlab-wide/AutowareV2X
https://tlab-wide.github.io/AutowareV2X/main/
https://www.youtube.com/watch?v=57fx3-gUNxU},
doi = {10.1109/VTC2023-Spring57618.2023.10199425},
year = {2023},
date = {2023-06-20},
urldate = {2023-06-20},
booktitle = {The 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)},
address = {Florence, Italy},
abstract = {For cooperative intelligent transport systems (C-ITS), vehicle-to-everything (V2X) communication is utilized to allow autonomous vehicles to share critical information with each other. We propose AutowareV2X, an implementation of a V2X communication module that is integrated into the autonomous driving (AD) software, Autoware. AutowareV2X provides external connectivity to the entire AD stack, enabling the end-to-end (E2E) experimentation and evaluation of connected autonomous vehicles (CAV). The Collective Perception Service was also implemented, allowing the transmission of Collective Perception Messages (CPMs). A dual-channel mechanism that enables wireless link redundancy on the critical object information shared by CPMs is also proposed. Performance evaluation in field experiments has indicated that the E2E latency of perception information is around 30 ms, and shared object data can be used by the AD software to conduct collision avoidance maneuvers. Dual-channel delivery of CPMs enabled the CAV to dynamically select the best CPM from CPMs received from different links, depending on the freshness of their information.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Lin2023,
title = {Time-To-Collision-Aware Lane-Change Strategy Based on Potential Field and Cubic Polynomial for Autonomous Vehicles},
author = {Pengfei Lin and Ehsan Javanmardi and Ye Tao and Vishal Chauhan and Jin Nakazato and Manabu Tsukada},
url = {https://arxiv.org/abs/2306.06981},
year = {2023},
date = {2023-06-04},
urldate = {2023-06-04},
booktitle = {2023 IEEE Intelligent Vehicles Symposium (IEEE IV 2023)},
address = {Anchorage, Alaska, USA},
abstract = {Making safe and successful lane changes (LCs) is one of the many vitally important functions of autonomous vehicles (AVs) that are needed to ensure safe driving on expressways. Recently, the simplicity and real-time performance of the potential field (PF) method have been leveraged to design decision and planning modules for AVs. However, the LC trajectory planned by the PF method is usually lengthy and takes the ego vehicle laterally parallel and close to the obstacle vehicle, which creates a dangerous situation if the obstacle vehicle suddenly steers. To mitigate this risk, we propose a time-to-collision-aware LC (TTCA-LC) strategy based on the PF and cubic polynomial in which the TTC constraint is imposed in the optimized curve fitting. The proposed approach is evaluated using MATLAB/Simulink under high-speed conditions in a comparative driving scenario. The simulation results indicate that the TTCA-LC method performs better than the conventional PF-based LC (CPF-LC) method in generating shorter, safer, and smoother trajectories. The length of the LC trajectory is shortened by over 27.1%, and the curvature is reduced by approximately 56.1% compared with the CPF-LC method.
},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tao2023,
title = {zk-PoT: Zero-Knowledge Proof of Traffic for Privacy Enabled Cooperative Perception},
author = {Ye Tao and Yuze Jiang and Pengfei Lin and Manabu Tsukada and Hiroshi Esaki},
url = {http://arxiv.org/abs/2211.07875},
doi = {10.1109/CCNC51644.2023.10059601},
year = {2023},
date = {2023-01-08},
urldate = {2023-01-08},
booktitle = {2023 IEEE 20th Annual Consumer Communications & Networking Conference (CCNC)},
address = {Las Vegas, NV, USA},
abstract = {Cooperative perception is an essential and widely discussed application of connected automated vehicles. However, the authenticity of perception data is not ensured, because the vehicles cannot independently verify the event they did not see. Many methods, including trust-based (i.e., statistical) approaches and plausibility-based methods, have been proposed to determine data authenticity. However, these methods cannot verify data without a priori knowledge. In this study, a novel approach of constructing a self-proving data from the number plate of target vehicles was proposed. By regarding the pseudonym and number plate as a shared secret and letting multiple vehicles prove they know it independently, the data authenticity problem can be transformed to a cryptography problem that can be solved without trust or plausibility evaluations. Our work can be adapted to the existing works including ETSI/ISO ITS standards while maintaining backward compatibility. Analyses of common attacks and attacks specific to the proposed method reveal that most attacks can be prevented, whereas preventing some other attacks, such as collusion attacks, can be mitigated. Experiments based on realistic data set show that the rate of successful verification can achieve 70% to 80% at rush hours.
},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Lin2022b,
title = {Cooperative Path Planning Using Responsibility-Sensitive Safety (RSS)-based Potential Field with Sigmoid Curve},
author = {Pengfei Lin and Manabu Tsukada},
url = {https://youtu.be/AhgptWUyzSc},
doi = {10.1109/VTC2022-Spring54318.2022.9860508},
year = {2022},
date = {2022-06-19},
urldate = {2022-06-19},
booktitle = {The 2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring)},
address = {Helsinki, Finland},
abstract = {Potential field (PF)-based path planning is reported to be highly efficient for autonomous vehicles because it performs risk-aware computation and has a simple structure. However, the inherent limitations of the PF make it vulnerable in some specific traffic scenarios, such as local minima and oscillations in close obstacles. Therefore, a hybrid path planning with the sigmoid curve has recently been presented to generate better trajectories than those generated by the PF for collision avoidance. However, it is time-consuming and less applicable in complex dynamic environments, especially in traffic emergencies. To address these limitations, we propose a cooperative hybrid path planning (CHPP) approach that involves collaboration with adjacent vehicles for emergency collision avoidance via V2V communication. Moreover, the responsibility-sensitive safety (RSS) model is introduced to enhance the PF and sigmoid curve for safe-critical and time-saving requirements. The effectiveness of the proposed CHPP method compared with the state-of-the-art methods is studied through simulation of both static and dynamic traffic emergency scenarios. The simulation results prove that the CHPP approach performs better in terms of computation time (0.02 s faster) and driving safety (avoiding collision) than other methods, which are more supportive for emergency cooperative driving.
},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Lin2022c,
title = {Adaptive Potential Field with Collision Avoidance for Connected Autonomous Vehicles},
author = {Pengfei Lin and Manabu Tsukada},
doi = {10.23919/ASCC56756.2022.9828160},
year = {2022},
date = {2022-05-03},
urldate = {2022-05-03},
booktitle = {13th Asian Control Conference (ASCC) 2022},
address = {Jeju, Korea},
abstract = {Potential field (PF), as a risk assessment method, is proposed to enhance autonomous vehicles’ (AVs) safety in collision avoidance. However, current PF targets mainly standalone-mode AVs (SAVs) by evaluating their relative position and velocity. In addition, the risk energy of the PF is usually assigned an infinite value along the z-axis. Therefore, this study presents an adaptive potential field (APF) for connected autonomous vehicles (CAVs). Valuable information (heading angle, steering wheel angle, etc.) other than relative position and velocity is supplemented to PF. Furthermore, we separate the APF from the cost function of the model predictive controller (MPC) to compute the desired reference signals directly, saving more computation time. The proposed APF-MPC is co-simulated in a comparative driving scenario via MATLAB/Simulink and CarSim simulator compared with the latest PF-MPC method.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tsukada2022,
title = {Misbehavior Detection Using Collective Perception under Privacy Considerations},
author = {Manabu Tsukada and Shimpei Arii and Hideya Ochiai and Hiroshi Esaki},
url = {https://arxiv.org/abs/2111.03461
https://youtu.be/UeHoSv5OAuc},
doi = {10.1109/CCNC49033.2022.9700564},
year = {2022},
date = {2022-01-08},
urldate = {2022-01-08},
booktitle = {2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC)},
address = {Online},
abstract = {In cooperative ITS, security and privacy protection are essential. Cooperative Awareness Message (CAM) is a basic V2V message standard, and misbehavior detection is critical for protection against attacking CAMs from the inside system, in addition to node authentication by Public Key Infrastructure (PKI). On the contrary, pseudonym IDs, which have been introduced to protect privacy from tracking, make it challenging to perform misbehavior detection. In this study, we improve the performance of misbehavior detection using observation data of other vehicles. This is referred to as collective perception message (CPM), which is becoming the new standard in European countries. We have experimented using realistic traffic scenarios and succeeded in reducing the rate of rejecting valid CAMs (false positive) by approximately 15 percentage points while maintaining the rate of correctly detecting attacks (true positive).},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Hirata2021b,
title = {Roadside-assisted Cooperative Planning using Future Path Sharing for Autonomous Driving},
author = {Mai Hirata and Manabu Tsukada and Keisuke Okumura and Yasumasa Tamura and Hideya Ochiai and Xavier Défago},
url = {https://arxiv.org/abs/2108.04629
https://youtu.be/xaBIQC0SClE},
doi = {10.1109/VTC2021-Fall52928.2021.9625324},
year = {2021},
date = {2021-09-27},
urldate = {2021-09-27},
booktitle = {IEEE 94th Vehicular Technology Conference (VTC2021-Fall)},
address = {Online},
abstract = {Cooperative intelligent transportation systems (ITS) are used by autonomous vehicles to communicate with surrounding autonomous vehicles and roadside units (RSU). Current C-ITS applications focus primarily on real-time information sharing, such as cooperative perception. In addition to real-time information sharing, self-driving cars need to coordinate their action plans to achieve higher safety and efficiency. For this reason, this study defines a vehicle’s future action plan/path and designs a cooperative path-planning model at intersections using future path sharing based on the future path information of multiple vehicles. The notion is that when the RSU detects a potential conflict of vehicle paths or an acceleration opportunity according to the shared future paths, it will generate a coordinated path update that adjusts the speeds of the vehicles. We implemented the proposed method using the open-source Autoware autonomous driving software and evaluated it with the LGSVL autonomous vehicle simulator. We conducted simulation experiments with two vehicles at a blind intersection scenario, finding that each car can travel safely and more efficiently by planning a path that reflects the action plans of all vehicles involved. The time consumed by introducing the RSU is 23.0 % and 28.1 % shorter than that of the stand-alone autonomous driving case at the intersection.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Mizutani2021b,
title = {AutoMCM: Maneuver Coordination Service with Abstracted Functions for Autonomous Driving},
author = {Masaya Mizutani and Manabu Tsukada and Hiroshi Esaki},
url = {https://arxiv.org/abs/2107.06627
https://youtu.be/eC7L0R_1Ybo
https://youtu.be/s3l5zypizxQ
https://youtu.be/XBpZeT-apGE},
doi = {10.1109/ITSC48978.2021.9564556},
year = {2021},
date = {2021-09-19},
urldate = {2021-09-19},
booktitle = {24th IEEE International Conference on Intelligent Transportation (ITSC)},
address = {Indianapolis, IN, United States},
abstract = {A cooperative intelligent transport system (C-ITS) uses vehicle-to-everything (V2X) technology to make self-driving vehicles safer and more efficient. Current C-ITS applications have mainly focused on real-time information sharing, such as for cooperative perception. In addition to better real-time perception, self-driving vehicles need to achieve higher safety and efficiency by coordinating action plans. This study designs a maneuver coordination (MC) protocol that uses seven messages to cover various scenarios and an abstracted MC support service. We implement our proposal as AutoMCM by extending two open-source software tools: Autoware for autonomous driving and OpenC2X for C-ITS. The results show that our system effectively reduces the communication bandwidth by limiting message exchange in an event-driven manner. Furthermore, it shows that the vehicles run 15% faster when the vehicle speed is 30 km/h and 28% faster when the vehicle speed is 50 km/h using our scheme. Our system shows robustness against packet loss in experiments when the message timeout parameters are appropriately set.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tsukada2020,
title = {AutoC2X: Open-source software to realize V2X cooperative perception among autonomous vehicles},
author = {Manabu Tsukada and Takaharu Oi and Akihide Ito and Mai Hirata and Hiroshi Esaki},
url = {https://github.com/esakilab/AutoC2X-AW
https://hal.archives-ouvertes.fr/hal-02942051/document?.pdf
https://youtu.be/kyv0sTyCIgU},
doi = {10.1109/VTC2020-Fall49728.2020.9348525},
year = {2020},
date = {2020-11-18},
urldate = {2020-11-18},
booktitle = {The 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall)},
address = {Victoria, B.C., Canada},
abstract = {The realization of vehicle-to-everything (V2X) communication enhances the capabilities of autonomous vehicles in terms of safety efficiency and comfort. In particular, sensor data sharing, known as cooperative perception, is a crucial technique to accommodate vulnerable road users in a cooperative intelligent transport system (ITS). In this regard, open-source software plays a significant role in prototyping, validation, and deployment. Specifically, in the developer community, Autoware is a popular open-source software for self-driving vehicles, and OpenC2X is an open-source experimental and prototyping platform for cooperative ITS. This paper reports on a system named AutoC2X to enable cooperative perception by using OpenC2X for Autoware-based autonomous vehicles. The developed system is evaluated by conducting field experiments involving real hardware. The results demonstrate that AutoC2X can deliver the cooperative perception message within 100 ms in the worst case. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tsukada2019b,
title = {Cooperative awareness using roadside unit networks in mixed traffic},
author = {Manabu Tsukada and Masahiro Kitazawa and Takaharu Oi and Hideya Ochiai and Hiroshi Esaki
},
url = {https://hal.archives-ouvertes.fr/hal-02335068/?.pdf},
doi = {10.1109/VNC48660.2019.9062773},
year = {2019},
date = {2019-12-04},
booktitle = {2019 IEEE Vehicular Networking Conference (VNC)},
pages = {9-16},
abstract = {Vehicle-to-vehicle (V2V) messaging is an indispensable component of connected autonomous vehicle systems. Although V2V standards have been specified by the European Union, United States, and Japan, the deployment phase represents mixed traffic in which connected and legacy vehicles co-exist. To enhance cooperative awareness in this mixed traffic, we assessed the special roadside unit that we developed in our previous work that generates required V2V messages on behalf of sensed target vehicles. In this paper, we extend our earlier work to propose a system called “Grid Proxy Cooperative Awareness Message to broaden the cooperative awareness message dissemination area by connecting infrastructure using high-speed roadside networks. To minimize delay in message delivery, we designed the proposed system to use edge computing. The proposed scheme delivers cooperative messages to a wider area with a low delay and a high packet delivery ratio by prioritizing packets by their respective safety contributions. Our simulation results indicate that the proposed scheme efficiently delivers messages in heavy road traffic conditions modeled on real maps of Tokyo and Paris. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Kitazawa2018,
title = {Wide transmission of Proxy Cooperative Awareness Message},
author = {Masahiro Kitazawa and Manabu Tsukada and Hideya Ochiai and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01879100/document?.pdf},
year = {2018},
date = {2018-06-10},
booktitle = {The Seventh International Conference on Advances in Vehicular Systems, Technologies and Applications (VEHICULAR 2018)},
pages = {54-59},
address = {Venice, Italy},
note = {Best Paper Award},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Azuma2018b,
title = {Improvement of False Positives in Misbehavoir Detection},
author = {Shuntaro Azuma and Manabu Tsukada and Kennya Sato},
url = {https://hal.archives-ouvertes.fr/hal-01879101/document?.pdf},
year = {2018},
date = {2018-06-10},
booktitle = {The Seventh International Conference on Advances in Vehicular Systems, Technologies and Applications (VEHICULAR 2018)},
pages = {78-83},
address = {Venice, Italy},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tsukada2017b,
title = {Roadside-Assisted V2V Messaging for Connected Autonomous Vehicle},
author = {Manabu Tsukada},
url = {https://hal.archives-ouvertes.fr/hal-01558066v2/document?.pdf},
year = {2017},
date = {2017-07-20},
booktitle = {The Thirteenth International Conference on Wireless and Mobile Communications (ICWMC 2017)},
pages = {89-94},
address = {Nice, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Azuma2017,
title = {A Method of Detecting Camouflage Data with Mutual Vehicle Position Monitoring},
author = {Shuntaro Azuma and Manabu Tsukada and Teruaki Nomura and Kenya Sato},
url = {https://hal.archives-ouvertes.fr/hal-01879103/document?.pdf},
year = {2017},
date = {2017-07-20},
booktitle = {The Sixth International Conference on Advances in Vehicular Systems, Technologies and Applications (VEHICULAR 2017)},
pages = {48-53},
address = {Nice, France},
note = {Best Paper Award},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tao2017b,
title = {Positioning and Perception in cooperative ITS application simulator},
author = {Ye Tao and Manabu Tsukada and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01879102/document?.pdf},
year = {2017},
date = {2017-07-20},
booktitle = {The Sixth International Conference on Advances in Vehicular Systems, Technologies and Applications (VEHICULAR 2017)},
pages = {54-59},
address = {Nice, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Kitazawa2017,
title = {Remote Proxy V2V Messaging using IPv6 and GeoNetworking},
author = {Masahiro Kitazawa and Manabu Tsukada and Kai Morino and Hideya Ochiai and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01578410/document?.pdf},
year = {2017},
date = {2017-07-10},
booktitle = {The Sixth International Conference on Advances in Vehicular Systems, Technologies and Applications (VEHICULAR 2017)},
pages = {74-80},
address = {Nice, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Kitazato2016,
title = {Proxy Cooperative Awareness Message: An Infrastructure-Assisted V2V Messaging},
author = {Tomoya Kitazato and Manabu Tsukada and Hideya Ochiai and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01341850/document?.pdf},
doi = {10.1109/ICMU.2016.7742092},
year = {2016},
date = {2016-10-04},
booktitle = {The Ninth International Conference on Mobile Computing and Ubiquitous Networking (ICMU2016)},
address = {DFKI Kaiserslautern, Kaiserslautern, Germany},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tao2016,
title = {DUPE: Duplicated Unicast Packet Encapsulation in Position-Based Routing VANET},
author = {Ye Tao and Xin Li and Manabu Tsukada and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01321553/document?.pdf},
doi = {10.1109/WMNC.2016.7543979},
year = {2016},
date = {2016-07-20},
booktitle = {9th IFIP Wireless and Mobile Networking Conference (WMNC 2016)},
address = {Colmar, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tao2015,
title = {Reproducing and Extending Real Testbed Evaluation of GeoNetworking Implementation in Simulated Networks},
author = {Ye Tao and Manabu Tsukada and Xin Li and Masatoshi Kakiuchi and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01317104/document?.pdf},
doi = {10.1145/2775088.2775092},
year = {2015},
date = {2015-06-20},
booktitle = {The 10th International Conference on Future Internet Technologies (CFI 2015)},
address = {Seoul, Korea},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tsukada2014b,
title = {AnaVANET: an experiment and visualization tool for vehicular networks},
author = {Manabu Tsukada and José Santa and Satoshi Matsuura and Thierry Ernst and Kazutoshi Fujikawa},
url = {https://hal.inria.fr/hal-00983479/document?.pdf
https://youtu.be/NamJUd-_0jw},
doi = {10.1007/978-3-319-13326-3_13},
year = {2014},
date = {2014-05-20},
booktitle = {9th International Conference on Testbeds and Research Infrastructures for the Development of Networks & Communities (TRIDENTCOM 2014)},
address = {Guangzhou, China},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Noguchi2011,
title = {Location-aware service discovery on IPv6 GeoNetworking for VANET},
author = {Satoru Noguchi and Manabu Tsukada and Thierry Ernst and Atsuo Inomata and Kazutoshi Fujikawa},
url = {https://hal.inria.fr/inria-00625796/document?.pdf},
doi = {10.1109/ITST.2011.6060058},
year = {2011},
date = {2011-08-20},
booktitle = {11th International Conference on Intelligent Transport System Telecommunications (ITST 2011)},
address = {Saint-Petersburg, Russia},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Toukabri2011,
title = {Experimental evaluation of an open source implementation of IPv6 GeoNetworking in VANETs},
author = {Thouraya Toukabri and Manabu Tsukada and Thierry Ernst and Lamjed Bettaieb},
url = {https://hal.inria.fr/inria-00625789/document?.pdf},
doi = {10.1109/ITST.2011.6060060},
year = {2011},
date = {2011-08-20},
booktitle = {11th International Conference on Intelligent Transport System Telecommunications (ITST 2011)},
address = {Saint-Petersburg, Russia},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Noguchi2011b,
title = {Real-vehicle integration of driver support application with IPv6 GeoNetworking},
author = {Satoru Noguchi and Manabu Tsukada and Ines Ben Jemaa and Thierry Ernst},
url = {https://hal.inria.fr/inria-00567852/document?.pdf},
doi = {10.1109/VETECS.2011.5956756},
year = {2011},
date = {2011-05-20},
booktitle = {2011 IEEE 73rd Vehicular Technology Conference (VTC2011-Spring)},
address = {Budapest, Hungary},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Jemaa2010,
title = {Validation and evaluation of NEMO in VANET using geographic routing},
author = {Ines Ben Jemaa and Manabu Tsukada and Hamid Menouar and Thierry Ernst},
url = {https://hal.inria.fr/inria-00567786/document?.pdf},
year = {2010},
date = {2010-11-20},
booktitle = {10th International Conference on Intelligent Transport System Telecommunications (ITST 2010)},
address = {Kyoto, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tsukada2010b,
title = {Experimental Evaluation for IPv6 over VANET Geographic routing},
author = {Manabu Tsukada and Ines Ben Jemaa and Hamid Menouar and Wenhui Zhang and Maria Goleva and Thierry Ernst},
url = {https://hal.inria.fr/inria-00505921/document?.pdf
https://youtu.be/MFo12Nxik94},
doi = {10.1145/1815396.1815565},
year = {2010},
date = {2010-06-10},
booktitle = {6th International Wireless Communications and Mobile Computing Conference, IWCMC 2010},
address = {Caen, France},
note = {Best Paper Award},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Khaled2009b,
title = {Geographical information extension for IPv6: application to VANET},
author = {Yacine Khaled and Manabu Tsukada and Thierry Ernst},
url = {https://hal.inria.fr/inria-00567786/document?.pdf},
doi = {10.1109/ITST.2009.5399339},
year = {2009},
date = {2009-10-20},
booktitle = {9th International Conference on Intelligent Transport System Telecommunications (ITST 2009)},
address = {Lille, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Khaled2009c,
title = {Application of IPv6 multicast to VANET},
author = {Yacine Khaled and Ines Ben Jemaa and Manabu Tsukada and Thierry Ernst},
url = {https://ieeexplore.ieee.org/document/5399356/},
doi = {10.1109/ITST.2009.5399356},
year = {2009},
date = {2009-10-20},
booktitle = {9th International Conference on Intelligent Transport System Telecommunications (ITST 2009)},
address = {Lille, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Khaled2009d,
title = {On the Design of efficient Vehicular Applications},
author = {Yacine Khaled and Manabu Tsukada and José Santa and Thierry Ernst.},
url = {https://hal.inria.fr/inria-00355878/document?.pdf},
doi = {10.1109/VETECS.2009.5073727},
year = {2009},
date = {2009-04-06},
booktitle = {2009 IEEE 69th Vehicular Technology Conference (VTC2009-Spring)},
address = {Barcelona, Spain},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Choi2008,
title = {IPv6 support for VANET with geographical routing},
author = {JinHyeock Choi and Yacine Khaled and Manabu Tsukada and Thierry Ernst},
url = {https://hal.inria.fr/inria-00336450/document?.pdf},
doi = {10.1109/ITST.2008.4740261},
year = {2008},
date = {2008-10-22},
booktitle = {8th International Conference on Intelligent Transport System Telecommunications (ITST 2008)},
journal = {8th International Conference on Intelligent Transport System Telecommunications (ITST 2008)},
address = {Phuket, Thailand},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Montavont2008,
title = {Anemone: A ready-to-go testbed for IPv6 compliant Intelligent Transport Systems},
author = {Nicolas Montavont and Antoine Boutet and Tanguy Ropitault and Manabu Tsukada and Thierry Ernst and Jari Korva and Cesar Viho and Laszlo Bokor},
url = {https://ieeexplore.ieee.org/document/4740262/},
doi = {10.1109/ITST.2008.4740262},
year = {2008},
date = {2008-10-22},
booktitle = {8th International Conference on Intelligent Transport System Telecommunications (ITST 2008)},
address = {Phuket, Thailand},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Dhraief2007,
title = {E-Bicycle Demonstration on the Tour De France},
author = {Amine Dhraief and Nicolas Montavont and Romain Kuntz and Manabu Tsukada},
doi = {10.1109/ICCGI.2007.23},
year = {2007},
date = {2007-03-04},
booktitle = {International Multi-Conference on Computing in the Global Information Technology (ICCGI '07)},
address = {Guadeloupe, French Caribbean},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tsukada2005,
title = {Dynamic Management of Multiple Mobile Routers},
author = {Manabu Tsukada and Thierry Ernst and Ryuji Wakikawa and Koshiro Mitsuya},
url = {https://www.nautilus6.org/doc/paper/20051116-ICON-NEMO-MMRM-ManabuT.pdf
https://youtu.be/fcKOUsYC6ro},
doi = {10.1109/ICON.2005.1635682},
year = {2005},
date = {2005-11-20},
booktitle = {IEEE Malaysia International Conference on Communications and IEEE International Conference on Networks (MICC & ICON 2005)},
volume = {2},
pages = {1108-1113},
address = {Kuala Lumpur, Malaysia},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@mastersthesis{松本和人2024,
title = {点群データの特徴抽出を用いた深層学習による自己位 置推定精度の予測 (Localizability Estimation for Autonomous Driving: A Deep Learning-Based Feature Extraction Approach)},
author = {松本和人},
year = {2024},
date = {2024-03-30},
urldate = {2024-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科},
abstract = {近年，自動運転の社会実装を目指した研究開発が進んでいる.例えば，サンフランシスコで は，テック企業が無人商用ロボタクシーを公道で走行させた実証実験が行われている. また， 中国では Baidu が武漢市で一般人向けのロボタクシーを走行させ，2023 年 1 月末までの累計 乗車件数は 140 万件を超えており，今後 200 台の自動運転タクシーを追加する予定である.自 動運転を実現するにあたって，車両に取り付けたセンサデータから周囲の環境に関する情報を 取得し，車両の位置を推定することが必要である.これを自己位置推定という.自己位置推定 の手法の中で，3DLiDAR と HD 地図を用いた手法が近年注目されている.この手法はマップ マッチングといい，3DLiDAR からの入力を高解像度マップと照合することで車両の正確な位 置を取得する.マップマッチングは高い精度で自己位置推定が可能であるが，田舎道やトンネ ルなど，周囲に特徴が乏しい場所では自己位置推定精度が低下するという問題がある.
本論文では，深層学習を用いて 3DLiDAR データと点群地図から自己位置推定精度を予測 する手法を提案する.入力点群データを MinkLoc3D と MinkLoc3D-SI という点群データの 特徴を抽出する深層学習モデルを用いて特徴抽出を行い，特徴ベクトルに特徴を埋め込む.そ して，得られた特徴ベクトルから，自己位置推定誤差を予測する.自己位置推定誤差を予測す ることで，誤差が大きい場所に対して事前に対策を施し，安全に走行できるようになる.例え ば，センサを切り替えや，舗装マーカーの設置，ドライバーに運転を促すことなどある.また， 誤差が小さいと予測される場所では，安全に自動運転走行ができるとわかる.深層学習モデル の学習のために，自動運転走行シミュレータの AWSIM と自動運転ソフトウェアの autoware を用いてデータセットを作成した.実験結果によると，94 %以上のケースで自己位置推定誤 差を 0.1m 以下の誤差で予測することができることがわかった.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Cheng2024,
title = {Pedestrian-centric Augmented Reality Visualization of Real-time Autonomous Vehicle Intentions},
author = {Yiwei Cheng},
year = {2024},
date = {2024-03-30},
urldate = {2024-03-31},
school = {Master Thesis, Graduate School of Information Science and Technology, The University of Tokyo},
abstract = {Connected Autonomous Vehicles (CAVs) produce a variety of information within their systems. With the advancement of communication and V2X (Vehicle-to-Everything) com- munication technology, there is a growing challenge to effectively convey this information to pedestrians and enhance their sense of safety when encountering such vehicles. Efforts to communicate this information to pedestrians have been made through various means, with Augmented Reality (AR) emerging as a notable approach. We found that previous studies have yet to integrate a functional AR application with a real-world autonomous driving system. In response to this gap, we proposed an AR application that visualizes real-time data from an active CAV and subsequently developed the system. Furthermore, we conducted field experiments using this developed system and conducted user studies to gather insights into the public’s perception of the system. Our results showed that the system can effectively transmit information from the CAV, and when provided with additional information, people tend to feel safer and confident regarding understanding the vehicle’s intentions.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Jiang2024,
title = {Cooperative Localization for Connected Autonomous Vehicles Utilizing LiDAR-Equipped Infrastructure},
author = {Yuze Jiang},
year = {2024},
date = {2024-03-30},
urldate = {2024-03-31},
address = {Master Thesis, Graduate School of Information Science and Technology, The University of Tokyo (Esaki Lab)},
abstract = {Advancements in LiDAR technology have led to more cost-effective production while simultaneously improving precision and resolution. As a result, LiDAR has become in- tegral to vehicle localization, achieving centimeter-level accuracy through techniques like Normal Distributions Transform (NDT) and other advanced 3D registration algorithms. Nonetheless, these approaches are reliant on high-definition 3D point cloud maps, the cre- ation of which involves significant expenditure. When such maps are unavailable or lack sufficient features for 3D registration algorithms, localization accuracy diminishes, posing a risk to road safety. To address this, we proposed to use LiDAR-equipped roadside unit and Vehicle-to-Infrastructure (V2I) communication to accurately estimate the connected autonomous vehicle’s position and help the vehicle when its self-localization is not accu- rate enough. Our simulation results indicate that this method outperforms traditional NDT scan matching-based approaches in terms of localization accuracy.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{伊藤広記2024,
title = {リアルタイムメディアストリーミングにおけるGENEVEを用いたマルチパス冗長通信フレームワーク(A Framework for Multipath Redundant Communication with GENEVE in Real-Time Media Streaming)},
author = {伊藤広記},
year = {2024},
date = {2024-03-30},
urldate = {2024-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科 (江崎研究室)},
abstract = {インターネットが現代社会の基盤として機能するなか，そこで動くアプリケーションのネットワークへの要求は様々なものになっている．特に近年では，リアルタイムストリーミングをはじめとする超低遅延な通信の需要が増している．
広範なカバレッジをもつモバイルネットワークによって，通信を行う環境も多様になった．しかしながらその安定性は場所や時間帯によって変動し，常に十分とはいえない．通信の信頼性を向上させる手法の一つに冗長通信があり，これまで多くの研究がなされてきた．しかしその多くはTCPによる通信を対象としており，UDPを用いるリアルタイムストリーミングには適用することができない．そこで本研究ではリアルタイムメディアストリーミングの通信品質を向上させることを目的とした，冗長通信フレームワークを提案した．従来の冗長通信システムの課題であったトランスポート層のプロトコルの制限を解決するためにGENEVEによるIPレイヤーでのトンネリングを行った．またリルタイムストリーミングにおいてはアプリケーションがメディア通信に最適化したネットワークの状態に敏感な輻輳制御をおこなっており，これが冗長通信によって引き起こされるパケットの順序の不整合と干渉することが知られている．本研究では冗長通信の受信側においてバッファリングを行うことで，この課題を解決した．実装したシステムを用いて，都心部で走行する車両から複数のモバイルネットワークを利用して冗長通信の実証を行った．実証にはWebRTCを用いたリアルタイムストリーミングアプリケーションを使用し，本フレームワークによってパケットの損失を大幅に減少させられること，アプリケーションの輻輳制御との干渉を回避し既存の冗長通信システムと比較してビットレートが向上することを示した．},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@article{Nakazato2023,
title = {WebRTC over 5G: A Study of Remote Collaboration QoS in Mobile Environment},
author = {Jin Nakazato and Kousuke Nakagawa and Koki Itoh and Romain Fontugne and Manabu Tsukada and Hiroshi Esaki},
url = {https://link.springer.com/content/pdf/10.1007/s10922-023-09778-5.pdf},
doi = {10.1007/s10922-023-09778-5},
issn = {1573-7705},
year = {2023},
date = {2023-10-24},
urldate = {2023-10-24},
journal = {Journal of Network and Systems Management},
volume = {32},
issue = {1},
abstract = {The increasing demand for remote collaboration and remote working has become crucial to daily life owing to the Covid-19 pandemic and the development of internet-based video distribution services. Furthermore, low-latency remote collaboration, such as teleoperation and support applications designed for in-vehicle environments, has gained considerable attention. The 5G technology is considered as a key infrastructure for remote collaboration. This study aimed to evaluate the actual 5G capability to achieve high quality of service (QoS) for remote collaboration. We designed and implemented a measurement tool to monitor the QoS of remote collaboration under real-world 5G conditions. We performed measurements encompassing the various 5G frequency bands. During these experiments, we employed various tools to obtain detailed mobile signal conditions to analyze the relationship between various environmental factors (e.g. signal quality, band, handoff, geographic conditions, and mobility) and the QoS performance of remote collaboration in a real-world 5G environment. This study elucidated the correlation between the WebRTC performance and various environmental factors as well as the performance improvement potential by leveraging the communication technologies of multiple mobile carriers. The collected data has been made publicly available to foster research on QoS and 5G.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Sone2023,
title = {An Ontology for Spatio-Temporal Media Management and an Interactive Application},
author = {Takuro Sone and Shin Kato and Ray Atarashi and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
url = {https://github.com/sdm-wg/web360square-vue
https://tlab.hongo.wide.ad.jp/sdmo/},
doi = {10.3390/fi15070225},
issn = {1999-5903},
year = {2023},
date = {2023-06-23},
urldate = {2023-06-23},
journal = {Future Internet},
volume = {15},
number = {225},
issue = {7},
abstract = {In addition to traditional viewing media, metadata that record the physical space from multiple perspectives will become extremely important in realizing interactive applications such as Virtual Reality(VR), Augmented Reality(AR). This paper proposes the Software Defined Media (SDM) Ontology designed to describe spatio-temporal media and the systems that handle them comprehensively. Spatio-temporal media refers to video, audio, and various sensor values recorded together with time and location information. The SDM Ontology can flexibly and precisely represent spatio-temporal media, equipment, and functions that record, process, edit, and play them and related semantic information. In addition, we recorded classical and jazz concerts using many video cameras and audio microphones, and then processed and edited the video and audio data with related metadata. Then, we created a dataset using the SDM Ontology and published it as linked open data(LOD). Furthermore, we developed "Web360^2" an application that enables users to interactively view and experience 360-degree video and spatial acoustic sounds by referring to this dataset. We conducted a subjective evaluation by using a user questionnaire. Web360^2 is a data-driven web application that obtains video and audio data and related metadata by querying the Dataset.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@bachelorthesis{kou2021,
title = {WebRTC による宅内環境間P2P ラウンドトリップタイム計測},
author = {中川紘輔(Kousuke Nakagawa)},
year = {2021},
date = {2021-03-31},
urldate = {2021-03-31},
organization = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
school = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
@bachelorthesis{Inokuchi2020,
title = {co-Sound: Web ARによるインタラクティブな視聴メディア及び空間同期},
author = {井口 和真 (Kazuma Inokuchi)},
year = {2020},
date = {2020-03-31},
urldate = {2020-03-31},
organization = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
school = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
@bachelorthesis{Unno2020,
title = {指向性のある音声の到達範囲を可視化するARシステム},
author = {海野亮 (Ryo Unno)},
year = {2020},
date = {2020-03-31},
urldate = {2020-03-31},
organization = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
school = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
@bachelorthesis{明日香2016,
title = {複数ユーザで共有される3次元仮想空間の同期プラットフォームの一提案},
author = {戸間 明日香 (Asuka Toma)},
year = {2016},
date = {2016-03-31},
urldate = {2016-03-31},
organization = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
school = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
@inproceedings{Orsholits2025,
title = {Context-Rich Interactions in Mixed Reality through Edge AI Co-Processing},
author = {Alex Orsholits and Manabu Tsukada},
url = {https://link.springer.com/chapter/10.1007/978-3-031-87772-8_3},
doi = {10.1007/978-3-031-87772-8_3},
isbn = {978-3-031-87771-1},
year = {2025},
date = {2025-04-09},
urldate = {2025-04-09},
booktitle = {The 39-th International Conference on Advanced Information Networking and Applications (AINA 2025)},
address = {Barcelona, Spain},
abstract = {Spatial computing is evolving towards leveraging data streaming for computationally demanding applications, facilitating a shift to lightweight, untethered, and standalone devices. These devices are therefore ideal candidates for co-processing, where real-time context understanding and low-latency data streaming are fundamental for seamless, general-purpose Mixed Reality (MR) experiences. This paper demonstrates and evaluates a scalable approach to augmented contextual understanding in MR by implementing multi-modal edge AI co-processing through a Hailo-8 AI accelerator, a low-power ARM-based single board computer (SBC), and the Magic Leap 2 AR headset. The proposed system utilises the native WebRTC streaming capabilities of the Magic Leap 2 to continuously stream camera data to the edge co-processor, where a collection of vision AI models-object detection, pose estimation, face recognition, and depth estimation-are executed. The resulting inferences are then streamed back to the headset for spatial re-projection and transmitted to cloud-based systems for further integration with large-scale AI models, such as LLMs and VLMs. This seamless integration enhances real-time contextual understanding in MR while facilitating advanced multi-modal, multi-device collaboration, supporting richer, scalable spatial cognition across distributed systems.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Sugizaki2024,
title = {Digital Twin Based Open Platform for IoT Offloading Control: Enabling System Transparency and User Participation},
author = {Yusuke Sugizaki and Jin Nakazato and Manabu Tsukada},
year = {2024},
date = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Orsholits2024,
title = {PLATONE: An Immersive Geospatial Audio Spatialization Platform},
author = {Alex Orsholits and Yiyuan Qian and Eric Nardini and Yusuke Obuchi and Manabu Tsukada},
doi = {10.1109/MetaCom62920.2024.00020},
year = {2024},
date = {2024-08-12},
urldate = {2024-08-12},
booktitle = {The 2nd Annual IEEE International Conference on Metaverse Computing, Networking, and Applications (MetaCom 2024)},
address = {Hong Kong, China},
abstract = {In the rapidly evolving landscape of mixed reality (MR) and spatial computing, the convergence of physical and virtual spaces is becoming increasingly crucial for enabling immersive, large-scale user experiences and shaping inter-reality dynamics. This is particularly significant for immersive audio at city-scale, where the 3D geometry of the environment must be considered, as it drastically influences how sound is perceived by the listener. This paper introduces PLATONE, a novel proof-of-concept MR platform designed to augment urban contexts with environment-dependent spatialized audio. It leverages custom hardware for localization and orientation, alongside a cloud-based pipeline for generating real-time binaural audio. By utilizing open-source 3D building datasets, sound propagation effects such as occlusion, reverberation, and diffraction are accurately simulated. We believe that this work may serve as a compelling foundation for further research and development.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Takada2024,
title = {Design of Digital Twin Architecture for 3D Audio Visualization in AR},
author = {Tokio Takada and Jin Nakazato and Alex Orsholits and Manabu Tsukada and Hideya Ochiai and Hiroshi Esaki},
doi = {10.1109/MetaCom62920.2024.00044},
year = {2024},
date = {2024-08-12},
urldate = {2024-08-12},
booktitle = {The 2nd Annual IEEE International Conference on Metaverse Computing, Networking, and Applications (MetaCom 2024)},
address = {Hong Kong, China},
abstract = {Digital twins have recently attracted attention from academia and industry as a technology connecting physical space and cyberspace. Digital twins are compatible with Augmented Reality (AR) and Virtual Reality (VR), enabling us to understand information in cyberspace. In this study, we focus on music and design an architecture for a 3D representation of music using a digital twin. Specifically, we organize the requirements for a digital twin for music and design the architecture. We establish a method to perform 3D representation in cyberspace and map the recorded audio data in physical space. In this paper, we implemented the physical space representation using a smartphone as an AR device and employed a visual positioning system (VPS) for self-positioning. For evaluation, in addition to system errors in the 3D representation of audio data, we conducted a questionnaire evaluation with several users as a user study. From these results, we evaluated the effectiveness of the implemented system. At the same time, we also found issues we need to improve in the implemented system in future works.},
key = {CREST},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Furuta2023,
title = {Web-Based BIM Platform for Building Digital Twin},
author = {Satoru Furuta and Jin Nakazato and Manabu Tsukada},
doi = {10.1109/DTPI59677.2023.10365476},
year = {2023},
date = {2023-11-07},
urldate = {2023-11-07},
booktitle = {3rd Annual IEEE International Conference on Digital Twins and Parallel Intelligence (DTPI 2023)},
address = {Florida, USA},
abstract = {Building digital twin (BDT) is utilized throughout the lifecycle of a building. It serves for efficient operations during the design and construction phases, and during the operational and maintenance phases, it’s used for asset management and as field maps for robots. Building Information Modeling (BIM), which contains both semantics and geometry data of building elements, holds promise as a data source for BDT. We have extracted four key technical challenges of the digital twin, particularly vital during the operational and maintenance phases: software, visualization, update, and real-scene reconstruction. Due to the exclusive and static nature of BIM, these challenges also pose significant issues in the context of BDT. To address these challenges, we designed and implemented a Web-based BIM platform. The implemented application shows not only geometry data but also semantics data, enables easy overlay with the latest indoor conditions, and provides updating functionality. The developed system is essential for the continuous operation of BDT in dynamic indoor environments. We evaluated the application through a questionnaire survey. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Inokuchi2023b,
title = {Semantic Digital Twin for interoperability and comprehensive management of data assets},
author = {Kazuma Inokuchi and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/371903091_Semantic_digital_twin_for_interoperability_and_Comprehensive_Management_of_Data_Assets/links/649b13318de7ed28ba5ca665/Semantic-digital-twin-for-interoperability-and-Comprehensive-Management-of-Data-Assets.pdf},
doi = {10.1109/MetaCom57706.2023.00049},
year = {2023},
date = {2023-06-26},
urldate = {2023-06-26},
booktitle = {IEEE International Conference on Metaverse Computing, Networking and Applications (IEEE MetaCom 2023)},
address = {Kyoto, Japan},
abstract = {Fusion of the real and virtual worlds is essential for applying digital technology to the infrastructure of human life. A digital twin is one of the technologies that aim to integrate real and virtual space. It creates a digital world with high fidelity to reality by accumulating exhaustive information from sensors to improve simulation and prediction accuracy. However, traditional digital twins have data asset management challenges owing to the physical, temporal, and structural heterogeneity of their objects. In this paper, we propose two metadata schemas that leverage semantics to construct a designer-oriented digital twin. Moreover, we implemented a viewer that reproduced the office-like demonstration field to verify the application of the proposed ontology. The proposed method enables a generic description of the dynamic behaviors of any entity by integrating physical twins faithful to the real world with virtual models expected by designers. We compared the proposed ontologies with existing techniques, conducted user evaluations, and discussed possible approaches for further enhancements for widespread use.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Nakagawa2021,
title = {WebRTC-based measurement tool for peer-to-peer applications and preliminary findings with real users},
author = {Kosuke Nakagawa and Manabu Tsukada and Keiichi Shima and Hiroshi Esaki},
url = {http://arxiv.org/abs/2112.02163
https://youtu.be/4XeCpuBLa7E},
doi = {10.1145/3497777.3498544},
year = {2021},
date = {2021-12-14},
urldate = {2021-12-14},
booktitle = {16th Asian Internet Engineering Conference (AINTEC)},
address = {Online},
abstract = {Direct peer-to-peer (P2P) communication is often used to minimize the end-to-end latency for real-time applications that require accurate synchronization, such as remote musical ensembles. However, there are few studies on the performance of P2P communication between home network environments, thus hindering the deployment of services that require synchronization. In this study, we developed a P2P performance measurement tool using the Web Real-Time Communication (WebRTC) statistics application programming interface. Using this tool, we can easily measure P2P performance between home network environments on a web browser without downloading client applications. We also verified the reliability of round-trip time (RTT) measurements using WebRTC and confirmed that our system could provide the necessary measurement accuracy for RTT and jitter measurements for real-time applications. In addition, we measured the performance of a full mesh topology connection with 10 users in an actual environment in Japan. Consequently, we found that only 66% of the peer connections had a latency of 30 ms or less, which is the minimum requirement for high synchronization applications, such as musical ensembles.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Chen2021,
title = {Reinforcement Learning Based Optimal Camera Placement for Depth Observation of Indoor Scenes},
author = {Yichuan Chen and Manabu Tsukada and Hiroshi Esaki},
url = {https://arxiv.org/abs/2110.11106},
doi = {10.1109/ICNSC52481.2021.9702214},
year = {2021},
date = {2021-12-03},
urldate = {2021-12-03},
booktitle = {The 2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)},
address = {Xiamen, China},
abstract = {Exploring the most task-friendly camera setting---optimal camera placement (OCP) problem---in tasks that use multiple cameras is of great importance. However, few existing OCP solutions specialize in depth observation of indoor scenes, and most versatile solutions work offline. To this problem, an OCP online solution to depth observation of indoor scenes based on reinforcement learning is proposed in this paper. The proposed solution comprises a simulation environment that implements scene observation and reward estimation using shadow maps and an agent network containing a soft actor-critic (SAC)-based reinforcement learning backbone and a feature extractor to extract features from the observed point cloud layer-by-layer. Comparative experiments with two state-of-the-art optimization-based offline methods are conducted. The experimental results indicate that the proposed system outperforms seven out of ten test scenes in obtaining lower depth observation error. The total error in all test scenes is also less than 90% of the baseline ones. Therefore, the proposed system is more competent for depth camera placement in scenarios where there is no prior knowledge of the scenes or where a lower depth observation error is the main objective.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Inokuchi2020b,
title = {co-Sound: An interactive medium with WebAR and spatial synchronization },
author = {Kazuma Inokuchi and Manabu Tsukada and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-02942505/document?.pdf
https://youtu.be/Bn1yPlbgqaA},
doi = {10.1007/978-3-030-65736-9_22},
year = {2020},
date = {2020-11-10},
booktitle = {The 19th IFIP International Conference on Entertainment Computing (ICEC) 2020},
pages = {255-263},
publisher = {Springer, Cham},
address = {Xi'an, China},
abstract = {An Internet-based media service platform can control recording processes and manage video and audio data interconnected by an IP network. Furthermore, the design and implementation of an object-based system for recording enable the flexible playback of the viewing contents. Augmented Reality (AR) is a three-dimensional video projection technology that allows us to interact with both elements in real space and digital space information. However, there are few examples of its use as a method for audio-visual media platforms. In this study, we propose co-Sound, which is an interactive audio-visual playback application for music events, using WebAR. co-Sound was designed as a multimodal interface that dynamically renders object-based AR in response to various actions from viewers on a web browser with low entry costs. Furthermore, by sharing AR objects among multiple devices in real time and bidirectionally, the relationship between users and contents was extended, and interaction among multiple users in the same AR space was possible. We implemented a prototype application, measured the performance of the AR spatial synchronization, and conducted a questionnaire-based evaluation. For subjective evaluation, 25 people experienced co-Sound and completed a questionnaire. We confirmed that the system was developed by object-based method with AR, achieved low-latency synchronization to accept operations from multiple users in real time, and the general acceptance of the system was very high.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Kato2020,
title = {Web360^{2}: An Interactive Web Application for viewing 3D Audio-visual Contents},
author = {Shin Kato and Tomohiro Ikeda and Mitsuaki Kawamorita and Manabu Tsukada and Hiroshi Esaki},
url = {https://zenodo.org/record/3898664/files/SMCCIM_2020_paper_102.pdf
https://github.com/sdm-wg/web360square
https://youtu.be/qg7aGhzO2Nc},
doi = {10.5281/zenodo.3898664},
year = {2020},
date = {2020-06-25},
booktitle = {17th Sound and Music Computing Conference (SMC)},
pages = {32-39},
address = {Torino, Italy},
abstract = {The use of video streaming services is expanding, and currently accounts for the majority of downstream Internet traffic. With the availability of virtual reality (VR) services and 360-degree cameras for consumer use, 3D services are also gaining in popularity. In recent years, the technology supporting for 3D representation on the Web has advanced. Users can easily utilize this technology without installing dedicated applications. In this study, we design and implement a Web application, called “Web360$^2$,” which plays 360-degree video and object-based 3D sounds interactively on the Web. We also evaluated Web360$^2$ through a questionnaire survey.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Kasuya2019,
title = {LiVRation: Remote VR live platform with interactive 3D audio-visual service},
author = {Takashi Kasuya and Manabu Tsukada and Yu Komohara and Shigeki Takasaka and Takuhiro Mizuno and Yoshitaka Nomura and Yuta Ueda and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-02136247/document?.pdf
https://youtu.be/4MElM4_t2Io},
doi = {10.1109/GEM.2019.8811549},
year = {2019},
date = {2019-06-19},
booktitle = {IEEE Games Entertainment & Media Conference (IEEE GEM) 2019},
pages = {1-7},
address = {Yale University, New Haven, CT, U.S.},
abstract = {Of late, various audio-visual services based on the internet are being deployed extensively. Among these, object- based audio-visual services are attracting more attention. In 2014, we had established the software defined media (SDM) consortium to investigate object-based and internet-based audio- visual services. Despite the increasing demand and popularity of live concert events, the placement of the microphone and camera limit the free-viewpoint watching of the contents of package media, such as DVDs. In this study, we design and implement an interactive 3D audio-visual service system called LiVRation, with a free-view-listen point. For subjective evaluation, 211 people were made to experience LiVRation and answer a questionnaire, subsequently. In addition, we demonstrated the system in the ”Billboard Live Hackasong 2017” hosted by Billboard Japan and received the first prize, based on the votes of the judges as well as the audience.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tsukada2017c,
title = {Software Defined Media: Virtualization of Audio-Visual Services},
author = {Manabu Tsukada and Keiko Ogawa and Masahiro Ikeda and Takuro Sone and Kenta Niwa and Shoichiro Saito and Takashi Kasuya and Hideki Sunahara and Hiroshi Esaki},
url = {https://arxiv.org/pdf/1702.07452.pdf},
doi = {10.1109/ICC.2017.7996610},
isbn = {1938-1883},
year = {2017},
date = {2017-05-21},
booktitle = {IEEE International Conference on Communications (ICC2017)},
pages = {1-7},
address = {Paris, France},
abstract = {Internet-native audio-visual services are witnessing rapid development. Among these services, object-based audio-visual services are gaining importance. In 2014, we established the Software Defined Media (SDM) consortium to target new research areas and markets involving object-based digital media and Internet-by-design audio-visual environments. In this paper, we introduce the SDM architecture that virtualizes networked audio-visual services along with the development of smart buildings and smart cities using Internet of Things (IoT) devices and smart building facilities. Moreover, we design the SDM architecture as a layered architecture to promote the development of innovative applications on the basis of rapid advancements in software-defined networking (SDN). Then, we implement a prototype system based on the architecture, present the system at an exhibition, and provide it as an SDM API to application developers at hackathons. Various types of applications are developed using the API at these events. An evaluation of SDM API access shows that the prototype SDM platform effectively provides 3D audio reproducibility and interactiveness for SDM applications.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Ikeda2016,
title = {New Recording Application for Software Defined Media},
author = {Masahiro Ikeda and Takuro Sone and Kenta Niwa and Shoichiro Saito and Manabu Tsukada and Hiroshi Esaki},
url = {http://www.aes.org/e-lib/browse.cfm?elib=18414},
year = {2016},
date = {2016-09-10},
booktitle = {Audio Engineering Society Convention Paper, 141st AES Convention},
address = {Los Angeles, USA},
abstract = {In recent years, hardware-based systems are becoming software-based and networked. From IP based media networks, the notion of Software Defined Media (SDM) has arisen. SDM is an architectural approach to media as a service by virtualization and abstraction of networked infrastructure. With this approach, it would be possible to provide more flexible and versatile systems. To test this concept, a baroque orchestra was recorded by various methods with 82 channels of microphones in total. All the data was organized based on the object-based concept and we applied advanced signal processing to the data based on array signal processing technology to produce a content matching various purposes of possible applications. Through this study, the value of SDM concept is verified.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@mastersthesis{古田悟2024,
title = {動的な屋内環境のためのBIMベースの建物デジタルツイン基盤の研究 (BIM-based building digital twin platform for dynamic indoor environment)},
author = {古田悟},
year = {2024},
date = {2024-03-30},
urldate = {2024-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科},
abstract = {近年，デジタルツインと呼ばれる技術が注目を集めている.デジタルツインは，物理的なオ ブジェクトやプロセス，システムをデジタル空間で正確に複製する技術である.この技術は， 実世界のデータを用いて，そのデジタル複製物をリアルタイムで更新し，分析や予測，問題解 決に活用することができる.また計算結果を元に現実世界の制御を行うこともできる.製造業 から都市計画、医療まで幅広い分野での応用が期待されている.本研究では，建築・エンジニ アリング・建設 (AEC: Architecture, Engineering, and Construction)分野におけるデジタルツ イン技術の応用に焦点を当て，特に建物の運用・保守段階で利用する目的での，デジタルツイ ンの汎用アーキテクチャの設計と，アプリケーションシステム実装について取り組んだ.AEC 産業では，個別のユースケースに特化したデジタルツイン実装に留まり，汎用的な参照アーキ テクチャに関する研究が不足している.本研究では，この課題に対応するために，AEC 分野で の建物の運用・保守段階におけるデジタルツインの汎用アーキテクチャを提案した.提案され たアーキテクチャは，AEC 分野で広く使用されている BIM(Building Information Modeling) と統合されることを前提に，建物独自の要件なども考慮して設計されている.また，建物の運 用・保守段階でのデジタルツインの運用における課題を抽出し，これを解決するためのアプリ ケーションシステムを設計・実装した.具体的には，提案された汎用アーキテクチャに合わせ 設計された BIM のメタデータ，ジオメトリデータを扱うバックエンドサーバ群と，Web ブラ ウザ上で動作するフロントエンドアプリケーションとして，BIM をベースにしたデジタルモ デルのデータ認識性を向上させるためのビューアと，現実の屋内状態と同期させるための重畳 表示・編集機能を備えたエディタを開発した.提案されたシステムは既存研究と比較した優位 性を持ち，ユーザスタディや性能測定などによって評価された.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Inokuchi2023,
title = {デジタルツインの相互運用性向上に向けたデータ資産の包括的管理手法(Comprehensive management approach for data assets to improve interoperability of the Digital Twin)},
author = {井口 和真 (Kazuma Inokuchi)},
year = {2023},
date = {2023-03-31},
urldate = {2023-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科},
abstract = {近年、デジタル技術を人々の生活基盤に援用することを目的として、現実世界とデジタル世 界の高度な融合による情報の利活用が目指されている。特にスマートビルディングと呼ばれる 分野は、エネルギーとコストの最適化や賑わい創出に向けた空間利活用などを目的とする。そ のため、建物要素や什器といった施設内の主要設備に加え、制御プロセスや移動ロボットなど の情報を含めた一元的な状態管理が求められる。その要求を満たす基盤となる概念として、デ ジタルツインが注目を集めている。デジタルツインは、センサから取得した現実世界のあらゆ る情報から、実空間全体をデジタル世界上に複製し、忠実度の高いシミュレーションをおこな う。一方で、デジタルツインはその対象の物理的・時間的・構造的複雑さを原因として、デー タ資産管理が課題となっている。統一的な管理手法が整っていないため、データ間の関係性が 不明瞭となり、プロジェクト間の相互運用性も低下する。そこで本研究では、デジタルツイン のデータ資産を包括的に管理するためのオントロジーである Digital Twin Ontology (DTO) 及 びデータ編集・加工プロセスを定式化するための Data Conversion Ontology (DCO) を提案す る。初めに、DTO では、現実世界のエンティティの状態を柔軟に記述可能なだけでなく、設 計者視点に着目することで、エンティティが抽象化された仮想モデルとの相互連携を満たすフ レームワークとなっている。次に、DCO では、ある “系” から別の “系” への変換をオントロ ジーとして設計することで、データの編集・加工プロセスを定式化する。提案オントロジーの 活用例として、オフィスを模したビル内環境に対して 2 種類の実装をおこなった。DTO に準 拠するリンクトグラフデータセット及びクエリ結果を元に環境を再現するビューアアプリケー ションと、DCO に基づく座標変換システムを実装した。実装を通して、最小限の先験的知識 によりデータ資産を取得可能であることを検証・考察した。また、既存手法との比較やドメイ ンの専門家へのインタビューによる提案オントロジーの内容評価もおこない、提案手法である DTO 及び DCO の有用性を評価した。},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Nakagawa2023,
title = {5G環境下における WebRTC リアルタイム遠隔コラボレーションのQoE計測(QoE measurement of WebRTC real-time remote collaboration under 5G environment)},
author = {中川 紘輔(Kosuke Nakagawa)},
year = {2023},
date = {2023-03-31},
urldate = {2023-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科 (江崎研究室)},
abstract = {昨今，より低遅延な遠隔コラボレーションへの注目が集まっており，特に自動運転分野にお いても，遠隔監視などのユースケースが提案されている.また，2019 年より世界中でサービス 開始された 5G は，大容量・低遅延通信が期待されているため，遠隔コラボレーションの需要 に対して重要なインフラであると考えられている.しかし，現状の 5G はカバレッジエリアの 拡大やスループットの改善に焦点が当てられており，3GPP などの標準化にて策定された機能 が全て動作しているわけではない. そのため，遠隔コラボレーションの実現に向けた性能・用 途の詳細な検討が必要不可欠である.そこで，本研究では WebRTC を用いた遠隔コラボレー ションを用いて，様々な 5G 環境下での遅延・映像音声品質の計測と分析を行った.具体的に は，WebRTC 通信品質と同時に 5G 通信の電波状況を詳細に調査し，それらの結果データを 統合した. 次に本統合データを分析し，可視化した結果をもとに遠隔コラボレーションが実現 可能なユースケース検討を評価した.本評価において，5 G ミリ波の不安定性が遠隔コラボ レーションにもたらす影響，交通手段(自動車/山手線)と WebRTC 通信の性能の関係，お よび車載環境のシナリオにて同時に 4 つの通信事業者を活用した性能向上の可能性の計 3 つ の観点について，分析により得られた複数の新たな知見を与える.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Aotani2022,
title = {デジタルツインのための 3D マップとネットワーク型認識},
author = {青谷和真(Kazuma Aotani)},
year = {2022},
date = {2022-03-31},
urldate = {2022-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科 (江崎・落合研究室)},
abstract = {現実世界を正確に再現し幅広く管理することができるデジタルツインは近年注目を集めている。しかし、デ ジタルツインの新たな作成には、対象となる建物の 3D データの取得や、詳細なメタデータの付与、さらには 広くアクセス可能になることが求められるなど要求事項が多岐にわたる。そこで、3D スキャンにより取得し た 3D データに対し、オントロジーを活用してメタデータを付与し、これらの情報を取得する API を作成し た。また、デジタルツインの作成後にはその活用が重要となるが、その一つとして屋内でのロボットの走行な どが挙げられる。他のセンサを活用し協調的な認識を行うことで効率的な経路計画が期待できるが、点群デー タは容量が大きく、ネットワークリソースを逼迫させるため、そのまま送信することは難しい。そこで、ネッ トワーク状況に応じて送信する点群のエリアを動的に変更することで点群のリアルタイムでの送信を試みた。 結果として、ネットワーク帯域を狭めてもリアルタイムで点群を送信することに成功した。},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Kawamura2022,
title = {工場・ビル管理におけるBIMに基づくデジタルツイン基盤の研究(BIM-based digital twin platform for factory and building management)},
author = {川村地平(Chihei Kawamura)},
year = {2022},
date = {2022-03-31},
urldate = {2022-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科},
abstract = {昨今のコロナ禍では、IoT(Internet of Things) 化の進行による影響も相まって、工場やビ ルなど、現実世界での施設を遠隔で管理することが多く望まれている。このような IoT デバ イスなどの先進技術を用いて、工場やビルの稼働効率向上や管理コスト削減を実現することを スマートファクトリーやスマートビルディングと呼ぶ。これら施設の管理には、工作機械や サーバなどと言った施設内の主要設備に限らず、空調、照明などを始めとした環境操作設備な ども必要とされている。そして、施設の管理には当然施設の状態の可視化も含まれており、こ れには工作機械やサーバ、ロボットなどの状態などに加え、流動的な要素である人の情報など も取れることが望ましく、現実世界での各種要素を管理するため、オブジェクトとして仮想空 間に反映できることが求められている。これらのニーズを満たす基盤はデジタルツインと呼ば れる。我々は、このデジタルツインを活用し、特定のデジタルツインアーキテクチャに基づい たデジタルツインアプリケーションを構築した。本論文では、工場とビルという二つの異なる 基盤に基づいて、それぞれ課題点を定めた上で、その課題点を解決するため、このデジタルツ インアプリケーションを用い、実証実験と検証を行った。それぞれの基盤について、具体的な 課題点として以下の二つを挙げた。まず工場では、施設の状態監視を主目的とし、工作機械の 三次元ジオメトリ、電力消費や稼働状況、周辺の環境情報などを含めた各種情報を、三次元ジ オメトリを用い仮想空間上に再現した工場内にて、立体的に再現することで直感的な工場施設 の可視化を行うという課題を設定した。そして、ビルでは、運搬ロボットや照明、空調設備を 操作するためのシステムを構築し、BIM(Building Information Modeling) や LiDAR(Light Detection And Ranging) といった高精度な環境情報を扱うことでロボットの行動計画をデジ タルツインアプリケーション上から生成することを課題とし、これを解決した。},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Chen2021b,
title = {Reinforcement Learning Based Optimal Camera Placement for Depth Observation of Indoor Scenes},
author = {Yichuan Chen},
year = {2021},
date = {2021-09-30},
urldate = {2021-09-30},
school = {Master Thesis, Graduate School of Information Science and Technology, The University of Tokyo (Esaki Lab)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Kato2021,
title = {時空間メディアを管理するオントロジーとインタラクティブ視聴体験への応用 (An Ontology for Spatio-Temporal Media Management and Application to Interactive 3D Audio-Visual Viewer)},
author = {加藤 慎 (Shin Kato)},
year = {2021},
date = {2021-03-31},
urldate = {2021-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科 (江崎・落合研究室)},
abstract = {メディアコンテンツの制作は収録，編集・加工，配信，再生の四段階に分割して考えることが できる.著者が所属する SDM (Software Defined Media) コンソーシアムでは，三次元映像・ 音声メディアを管理するための構造フレームワークとして SDM Ontology の提案を進めてき た.SDM Ontology は制作フローの収録段階を対象として，様々かつ膨大なメタデータを階 層構造に整理し，RDF (Resource Description Framework) による記述，LOD (Linked Open Data) の作成によってオープンデータとしてアクセスを可能にした.本研究では，メディア 収録段階より先の制作フローに焦点を当て，特にメディア編集・加工段階を SDM Ontology の記述対象範囲に加えるとともに，従来の構造について考察し直し，音楽イベントに限らず 時空間情報をもつメディアを管理する，より汎用的なフレームワークとして SDM Ontology Version 2 を提案する.また，SDM Ontology Version 2 の活用例として，インタラクティブ に 360◦ 動画および立体音響を視聴体験できる Web アプリケーション「Web3602」を開発し た.Web3602 は SDM Ontology Version 2 に準拠するクラシックコンサートおよびジャズ セッションイベントのデータセットを問い合わせて動作する.このアプリケーションについ て，Interop Tokyo 2019 での対面アンケート，2020 年 12 月より開始した Web アンケートの 2 種類の主観評価を実施し，インタラクティブ視聴体験の有効性や異なる視聴環境が及ぼす影 響を検証・考察した.さらに，SDM Ontology Version 2 の普及活動の一環として，オントロ ジーの語彙体系を説明する Web ドキュメントを作成および公開し，LOD の普及促進を目的に 毎年開催される「LOD チャレンジ」のアプリケーション部門に Web3602，データセット部門 に SDM Ontology Version 2 準拠のデータセットを提出した.LOD チャレンジにおいては， データセット部門への応募作品で最優秀賞およびスポンサー賞を受賞した.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{琢郎2019,
title = {立体音響を用いる小型サラウンドスピーカー環境の研究（Research of Compact Surround Speakers for 3D Audio）},
author = {庄子 琢郎 (Takuro Shoji)},
url = {http://koara.lib.keio.ac.jp/xoonips/modules/xoonips/detail.php?koara_id=KO40001001-00002018-0679},
year = {2019},
date = {2019-03-31},
school = {修士論文, 慶應義塾大学大学院 メディアデザイン研究科},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{裕2018,
title = {Software Defined Media Ontology : 分散収録環境のための視聴空間の再現フレームワーク（Software Defined Media Ontology : A framework for reproduction of audio-visual contents by distributed recording environment）},
author = {菰原 裕 (Yu Komohara)},
url = {http://hdl.handle.net/2261/00074653?.pdf},
year = {2018},
date = {2018-03-31},
school = {修士論文, 情報理工学系研究科電子情報学専攻（江崎・落合研究室）},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Ogawa2016,
title = {Tangible sound : a tangible interface for object-based sound system},
author = {Keiko Ogawa},
url = {http://koara.lib.keio.ac.jp/xoonips/modules/xoonips/detail.php?koara_id=KO40001001-00002015-0432},
year = {2016},
date = {2016-03-31},
school = {Master’s thesis, KMD:Graduate School of Media Design, Keio University},
note = {研究科委員長表彰 (Dean's List Award)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@misc{Orsholits2025b,
title = {Edge Vision AI Co-Processing for Dynamic Context Awareness in Mixed Reality},
author = {Alex Orsholits and Manabu Tsukada},
url = {https://www.youtube.com/watch?v=xxahKZl4K9w
https://ieeevr.org/2025/awards/conference-awards/#poster-honorable},
doi = {10.1109/VRW66409.2025.00293},
year = {2025},
date = {2025-03-08},
urldate = {2025-03-08},
booktitle = {2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)},
address = {Saint-Malo, France},
abstract = {Spatial computing is evolving towards leveraging data streaming for computationally demanding applications, facilitating a shift to lightweight, untethered, and standalone devices. These devices are ideal candidates for co-processing, where real-time scene context understanding and low-latency data streaming are fundamental for general-purpose Mixed Reality (MR) experiences. This poster demonstrates and evaluates a scalable approach to augmented contextual understanding in MR by implementing edge AI co-processing through a Hailo-8 AI accelerator, a low-power ARM-based single board computer (SBC), and the Magic Leap 2 AR headset. The resulting inferences are streamed back to the headset for spatial reprojection into the user’s vision.},
howpublished = {IEEE VR 2025, Poster},
note = {Honorable mention},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
@phdthesis{Kasuya2020,
title = {BIMを用いたSoftware Defined BACSの実現に関する研究 (The design and implementation of Software Defined BACS based on shared BIM repository)},
author = {粕谷 貴司(Takashi Kasuya)},
year = {2020},
date = {2020-09-30},
urldate = {2020-09-30},
school = {Ph.D Thesis, The University of Tokyo (Esaki Lab)},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
@workshop{Bao2023b,
title = {Towards a Trusted Inter-Reality: Exploring System Architectures for Digital Identification},
author = {Naren Bao and Jin Nakazato and Muhammad Asad and Ehsan Javanmardi and Manabu Tsukada},
doi = {10.1145/3627050.3631566},
year = {2023},
date = {2023-11-07},
urldate = {2023-11-07},
booktitle = {The 1st International Workshop on Internet of Realities (IoR-WS 2023) at International Conference on the Internet of Things},
address = {Nagoya, Japan},
abstract = {The concept of a trusted inter-reality, where physical and virtual worlds seamlessly converge, represents a paradigm shift in how digital identities are formed and managed. This paper explores the complex landscape of system architectures designed to enable secure and user-centric digital identification within interconnected realities. Our survey focuses on user-centric security, recognizing the prevalence of wearable devices and immersive technologies in inter-reality environments. We advocate for user-friendly authentication methods and privacy-preserving techniques that prioritize user control within the trust model. Furthermore, we delve into the influence of social and cultural factors, particularly age and gender, on the shaping of digital identity within interconnected realities. We argue in favor of adaptable system architectures that respect generational and gender diversity. In conclusion, we emphasize the alignment of system architectures with these principles to promote a secure, user-centric, and culturally sensitive digital identity experience. This research contributes to the ongoing discourse on digital identification in interconnected realities, providing actionable guidance for stakeholders in the evolving landscape of trusted inter-reality.
},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@workshop{Sugizaki2023b,
title = {Umpire Assistance System in Baseball Game},
author = {Yusuke Sugizaki and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
doi = {10.1145/3627050.3631569},
year = {2023},
date = {2023-11-07},
urldate = {2023-11-07},
booktitle = {The 1st International Workshop on Internet of Realities (IoR-WS 2023) at International Conference on the Internet of Things},
address = {Nagoya, Japan},
abstract = {In recent years, information technology has become an important element in the pursuit of a more prosperous society, and this extends to the realm of sports. Events across various domains are undergo- ing digitalization. Within the context of baseball, the introduction of automated technology for umpiring is garnering significant at- tention. Conversely, the frequent misjudgments by human referees have become a contentious issue. Both Major League Baseball (MLB) and Nippon Professional Baseball (NPB) have introduced a chal- lenge system that requires replay verification in instances where there’s disagreement with an umpire’s decision. However, this sys- tem is not permissible in venues lacking the necessary facilities. A consequential shortage of umpires intensifies their workload, potentially leading to more misjudgments. This paper proposes an architecture for an umpire assistance system designed to ad- dress these challenges in baseball games. Our proposed system architecture facilitates the filming of a baseball game from multiple angles, rendering the challenge system independent of the venue’s infrastructure. Moreover, the system autonomously makes judg- ments using footage from multiple cameras, thereby supporting both human umpires and automated officiating. Looking ahead, we also discuss the potential advancements when integrating this with digital twins and explore its applicability to other sports.},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@workshop{Cheng2023,
title = {Pedestrian-centric Augmented Reality Visualization of Real-time Autonomous Vehicle Dynamics},
author = {Yiwei Cheng and Jin Nakazato and Ehsan Javanmardi and Chia-Ming Chang and Manabu Tsukada},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/374387897_Pedestrian-centric_Augmented_Reality_Visualization_of_Real-time_Autonomous_Vehicle_Dynamics/links/651bda961e2386049df3c4ee/Pedestrian-centric-Augmented-Reality-Visualization-of-Real-time-Autonomous-Vehicle-Dynamics.pdf},
doi = {10.1109/CloudNet59005.2023.10490048},
year = {2023},
date = {2023-11-04},
urldate = {2023-11-04},
booktitle = {The Workshop on Intelligent Cloud Continuum for B5G Services in the IEEE International Conference on Cloud Networking (CloudNet) 2023},
address = {New York City, USA},
abstract = {Connected Autonomous Vehicles (CAVs) produce a variety of information within their systems. With the advancement of communication and V2X (Vehicle-to-Everything) communication technology, there is a growing challenge to effectively convey this information to pedestrians and enhance their sense of safety when encountering such vehicles. Efforts to communicate this information to pedestrians have been made through various means, with Augmented Reality (AR) emerging as a notable approach. However, previous studies have yet to integrate a functional AR application with a real-world autonomous driving system. In response to this gap, we proposed an architecture for an AR application that visualizes real-time data from an active CAV and subsequently developed the system. Furthermore, we conducted field experiments using this developed system and conducted user surveys during exhibitions to gather insights into the public’s perception of the system. Our results showed that the system can effectively transmit information from the CAV, and when provided with additional information, people tend to feel safer regarding the vehicle. },
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@workshop{Atarashi2018,
title = {The Software Defined Media Ontology for Music Events},
author = {Ray Atarashi and Takuro Sone and Yu Komohara and Manabu Tsukada and Takashi Kasuya and Hiraku Okumura and Masahiro Ikeda and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01879099/document?.pdf},
doi = {10.1145/3243907.3243915},
year = {2018},
date = {2018-10-08},
urldate = {2018-10-08},
booktitle = {Workshop on Semantic Applications for Audio and Music (SAAM) held in conjunction with ISWC 2018},
pages = {15-23},
address = {Monterey, California, USA.},
abstract = {With the advent of viewing services based on the Internet, the importance of object-based viewing services for interpreting objects existing in space and utilizing them as the content is increasing. Since 2014, the Software Defined Media Consortium has been researching object-based media and Internet-based viewing spaces. This paper defines a framework in event participants and professional recorders each freely share recorded data, and a third party can create an application based on the data. This study aims to provide an SDM ontology-based contents management mechanism with a detailed description of the object-based audio and video data and the recording environment. The data can be shared via the Internet and is highly reusable. We implemented this management mechanism and have developed and validated applications that are capable of interactively playing 3D content from any viewpoints freely.},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@mastersthesis{杉崎勇介2025,
title = {IoT デバイスの効率的運用に向けたイベント駆動プロトコルとオープン基盤 (An Event-Driven Protocol and Open Platform for Efficient IoT Device Management)},
author = {杉崎勇介},
year = {2025},
date = {2025-03-31},
urldate = {2025-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科},
abstract = {Society 5.0 は，先進技術を活用した社会の情報化を通じて，経済発展と社会課題の同時解決
を目指す日本の未来社会のビジョンである．この社会インフラでは，全てのステークホルダが
データの利用やシステム管理に主体的に参加できる環境の構築が推進されている．その中で，
物理空間とサイバー空間をつなぐ役割を担うものとして IoT (Internet of Things) が注目されて
いる．しかし，IoT の普及に伴い，IoT デバイス本体のバッテリーや計算リソースの制約，IoT
システム間の相互運用性欠如によるサイロ化など，いくつかの課題が浮上している．本研究で
は，IoT の効率的な運用と，相互運用性の向上を目指して，イベント駆動プロトコルの提案と
オープン基盤の設計・実装を行った．イベント駆動プロトコルは，デバイスの消費電力を抑え
るための間欠的デバイス制御と，クラウドおよびエッジ処理を動的に選択するオフローディン
グ最適化の手法を組み合わせたものである．このプロトコルにより，デバイスの持続可能な運
用と通信効率の向上を実現した．さらに，Web ベースで汎用性の高いオープン基盤を設計し，
デジタルツイン技術を活用して物理空間とサイバー空間をシームレスに接続する環境を構築し
た．この基盤は専用機器を必要とせず，汎用的な Web ブラウザからアクセス可能であるほか，
IoT データの視覚的かつ直感的な可視化と，リアルタイムでの情報反映を実現する機能を備え
ている．本研究の提案手法は，人数検知シナリオを用いて評価を行い，その有効性を確認し
た．また，具体的なユースケースとして，「スポーツにおけるリプレー検証アプリ」と「Web
ベースデジタルツインによる XR 脱出ゲーム」の 2 つを開発し，それぞれの実証実験を通じ
て，提案手法の実用性と汎用性を検証した．},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{菊池龍翔2025,
title = {遠隔ライブ視聴のための音声遅延が与える影響の調査(A Study on the Impact of Audio Latency for Remote Live Viewing)},
author = {菊池龍翔},
year = {2025},
date = {2025-03-31},
urldate = {2025-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科},
abstract = {デジタルツインの概念があらゆる業界で注目を浴びており，様々なシミュレーションのため
に現実の世界をそのまま仮想世界上に再現したモデルである．一方で，メタバースは，同様に
仮想空間を構築するためデジタルツインと似た概念であるものの，アバターを介した人々の交
流や経済活動が行われる場であり，メタバース空間上には現実空間で実在するもの，あるいは
しないものの両方が混在する．また，仮想空間でアバターを使って交流するメタバースは，近
年注目されているサービスであり，スポーツ観戦や音楽ライブなどのイベントも開催される．
しかしメタバース空間における音楽ライブ (例:VR ライブ) などは，ネットワーク遅延による
問題で演者は一箇所に集まらなければならない．また，観客は録音されたスレテオの音源を聞
くため，生のライブよりも臨場感に欠けることが多い．そこで，演者，観客ともにどこにいて
も実現できる臨場感の高い VR ライブシステムの需要があるのではないかと考えられる． 本
システムの実現のためには，ネットワーク遅延の影響による遠隔演奏の精度を確かめる必要が
ある．そこで本稿では，まず，SYNCROOM を用いた事前検証において，遠隔セッションに
おける音声遅延を測定した．実環境での検証により，今回の実験環境であれば演奏者間の往復
遅延は最大で 32.5 ms 程度に収まることが確認できた．ただし，特定の条件下では遅延が大き
くばらつく場合もあり，演奏の質に影響を与える可能性が示唆された．次に，音声遅延シミュ
レーション実験を通じて，遅延が演奏者および聴講者の知覚に与える影響を評価した．演奏者
においては，片道遅延が 20 ms を超えた時点で遅延を感じ始め，60 ms を超えると演奏が困難
になることが明らかとなった．さらに，楽器の種類や組み合わせによる遅延の影響の違いもみ
られた．例えば，ドラムは遅延に対して特に敏感である一方，ボーカルは比較的遅延を感じに
くいという結果となった．さらに第三者評価実験では，録音した演奏を聴講者に提示し，遅延
が音楽全体の印象や成立性に与える影響を検討した．その結果，遅延が 40 ms を超えると，演
奏のずれを感じる人が多くなることが判明した．逆に遅延が 40 ms 以下であれば，演奏がほ
ぼ自然に聞こえることが示された．これらの結果から遠隔演奏をエンターテイメントとして第
三者に配信する際，聴講者に対して違和感のない演奏をすることは十分可能であると考えられ
る．今後の課題として 3 人以上で演奏した時や，音声遅延が行きと帰りで異なっている状況で
の調査，長時間の演奏や複雑な楽曲における遅延の影響の検討，さらに演奏のずれの知覚に関
して音源再生機器による差を明確にし原因を特定することが挙げられる．},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Li2025,
title = {Cross-Attention Enhanced End-to-End Autonomous Driving in Unprotected Turns},
author = {Dongyang Li},
year = {2025},
date = {2025-03-31},
urldate = {2025-03-31},
school = {Master Thesis, Graduate School of Information Science and Technology, The University of Tokyo},
abstract = {Performing an unprotected turn at the intersection is a complex scenario for autonomous
vehicles. It requires a comprehensive understanding of the surrounding environment, while
also highly relying on the current state of the ego vehicle to make safer decisions. A conventional way to learn end-to-end autonomous driving is imitation learning, which stands
for learning from expert demonstrations. While most imitation learning methods focus on
imitating the expert action, they often fail to imitate a complex policy efficiently when the
ego vehicle’s states are crucial to the scenario because there might be arbitrary optimal
actions under different states. To address this issue and investigate how vehicle states
affect autonomous driving, we present a novel cross-attention enhanced imitation learning
approach for end-to-end autonomous driving in unprotected turns, focusing on capturing
the relationships between the ego vehicle’s states and its perception of the environment.
We evaluated our model in the AWSIM simulator, an open source autonomous driving
simulator, and the results demonstrate that our model outperformed conventional imitation learning-based baselines in performing unprotected turn scenarios, showcasing its
ability to imitate a complex policy efficiently.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Zhu2025b,
title = {A Distributed Content Subscription Mechanism with Revision Discovery to Decouple Content Sharing Platform and Creator ID},
author = {Zhihai Zhu},
year = {2025},
date = {2025-03-31},
school = {Master Thesis, Graduate School of Information Science and Technology, The University of Tokyo (Esaki Lab)},
abstract = {This thesis introduces an innovative distributed content subscription mechanism designed to address the challenges of platform independence, anonymity, and censorship resistance for content creators and their audiences. By enhancing the Kademlia Distributed
Hash Table (DHT) protocol with revision numbers and republication timestamps, this
mechanism enables subscribers to discover content updates through heuristic revision
queries while maintaining resilience in dynamic network environments.
The proposed mechanism leverages public key cryptography to ensure content authenticity and creator identification, thus establishing a secure and trust-enhanced environment for content sharing. Integration with peer-to-peer protocols, such as BitTorrent,
further ensures scalable and efficient content distribution. This system is particularly
valuable for creators operating under strict surveillance or content control, offering them
the freedom to publish and share without reliance on centralized platforms.
Through a combination of theoretical analysis and experimental validation, the mechanism’s effectiveness has been demonstrated. Simulations involving a network of 1000
nodes reveal the system’s ability to maintain content availability and facilitate update
discovery, even in the absence of the original content creator. These results highlight
the potential of the system to provide a robust alternative to existing centralized and
decentralized solutions.
The research contributes to the field of distributed systems by addressing the pressing
need for decentralized content sharing mechanisms that prioritize user autonomy, privacy,
and resilience. By incorporating advancements in cryptographic verification, DHT protocol extensions, and peer-to-peer integration, the proposed solution offers a comprehensive
framework for secure, scalable, and censorship-resistant communication.
Future work for this study encompasses several promising directions, including the
implementation of a Peer Exchange (PEX)-like mechanism to enhance update dissemination efficiency by leveraging the shared interests of peers already engaged in downloading
and seeding content. Further evaluation of the mechanism under real-world networking conditions, such as geographically distributed Kubernetes deployments, could assess
its robustness in scenarios involving packet loss, NAT traversal, and other complexities.
Optimizing system configurations, such as Linux kernel parameters, could also enable efficient resource utilization for large-scale deployments. Additionally, comprehensive threat
analyses, including resilience against DDoS attacks, and empirical testing of mitigation
strategies, are vital for strengthening security.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{鈴木健吾2025,
title = {O-RAN による ITS 情報を利用した C-V2X モビリティ制御アーキテクチャ (A Mobility Management Architecture for C-V2X using ITS information with O-RAN)},
author = {鈴木健吾},
year = {2025},
date = {2025-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科 (江崎研究室)},
abstract = {移動通信システムにおけるモビリティ制御は，移動する端末に対して通信を途絶えさせない
ための重要な技術である．特に高速に移動する自動車を通信対象とする C-V2X では，より高
度なモビリティ制御が要求される．3GPP によって標準化されているビーム制御やハンドオー
バー制御手法は受信電波強度の評価がベースのシンプルなものであり，より性能の高いモビリ
ティ制御を目指して様々な研究が行われている．そのアプローチの 1 つが基地局が周囲の環
境をセンシングすることで端末の位置や速度などの情報を取得する方法であるが，この手法に
は高コストやリソースの負担といった課題も残されている．そこで本研究で路上情報をやり取
りする仕組みを持つ ITS に注目し，ITS の持つ様々な路上情報を O-RAN を導入することで
C-V2X のモビリティ制御に利用するためのシステムアーキテクチャを考案した．また，3GPP
標準によるビーム制御の課題を克服するために提案システムを利用したビーム制御手法を提案
し，シミュレーション評価によってその性能のポテンシャルを明らかにした．},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{吉村厚紀2025,
title = {強化学習を用いた V2X 向け協調制御によるブロック チェーンの実装と評価 (Implementation and Evaluation of Blockchain for V2X Cooperative Control Using Reinforcement Learning)},
author = {吉村厚紀},
year = {2025},
date = {2025-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科(江崎研究室)},
abstract = {近年、V2X（Vehicle-to-Everything）ネットワークにおけるブロックチェーン技術の活用が注
目されている。ブロックチェーンの改ざん耐性や透明性を活かすことで、交通事故のフォレン
ジック分析や、信頼性の高いデータ共有が可能となる。しかし、V2X 環境においては、車両が
頻繁に移動することでブロックチェーンの合意形成メンバーが変化しやすいといった問題や、
渋滞などが発生し特定のブロックチェーンに負荷が集中するといった問題が発生している。
本研究では、この課題を解決するために、強化学習を活用した V2X 向けの協調型ブロック
チェーンシステムを提案する。特に、RSU（Road Side Unit）間の協調によってメンバー管理
を動的に行う IMMU（Integrated Membership Management Unit） を導入し、車両の移動に伴
う合意形成メンバーの変化に柔軟に対応できる仕組みを構築した。また、IMMU 内の Booth
Management Unit に強化学習を適用し、各 RSU の負荷を均一化するとともに、車両が最も近
い RSU に割り当てられるよう最適化を行った。これにより、従来のブロックチェーンシステ
ムにおける負荷集中や通信オーバーヘッドの問題を軽減し、より効率的な合意形成を実現する
ことを目指した。
提案手法の評価には、V2X シミュレーションツールである Artery と、ブロックチェーン
ベースの合意形成システム V-Guard を組み合わせたシミュレーション環境を構築し、実験を
行った。その結果、IMMU を導入することで合意形成の成功率が向上し、特に強化学習を適用
することで合意形成時間の偏りを低減できることが確認された。},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{櫻井晴基2025,
title = {RAN シミュレータを用いた O-RAN アプリケーションのコンフリクトの再現と評価 (Evaluation of O-RAN Application Conflicts Using RAN Simulator)},
author = {櫻井晴基},
year = {2025},
date = {2025-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科 (江崎研究室)},
abstract = {近年，Radio Access Network（RAN）では，従来の固定ベンダーによる市場寡占を防ぎ，
柔軟かつスケーラブルなモバイルネットワークを実現しようとする Open RAN の思想が注
目されており，O-RAN Alliance によって標準化が進められている．O-RAN における RAN
Intelligent Controller（RIC）は，ネットワークの効率的な管理を可能にする重要なコンポー
ネントであり，その上で動作するアプリケーション（xApp および rApp）は，AI/ML の技術
を活用して RAN の制御を行う．しかし，それぞれのアプリケーションは複数のベンダーに
よって開発される可能性があり，それぞれの目的関数に基づいて RAN リソースを制御するた
め，同時に動作させた場合にコンフリクトを生じさせる可能性があることが指摘されている．
PACIFISTA は O-RAN アプリケーションのコンフリクトを管理するフレームワークであ
り，アプリケーションをサンドボックス環境で統計的に解析して特性を比較することでコンフ
リクトを定量的に評価する．このサンドボックス環境での解析はコンフリクトの評価において
重要な役割を果たすが，現状の概念実証においては小規模なシナリオでの評価がされており，
十分とは言えない．そこで本研究では，PACIFISTA を拡張し，大規模 RAN シミュレータを
用いてアプリケーションを解析するシステムを提案する．また，実験として商用の RAN シ
ミュレータである VIAVI Tera VM RIC Test を用いて，エネルギーセービングと負荷分散の
2 つの擬似的な xApp を開発し，コンフリクトが発生する状況を再現した．
評価結果から，RAN シミュレータにおいてもコンフリクトの発生が確認され，提案シス
テムがコンフリクトの検出と管理に有効であることが示された．特に，本研究では，RAN
シミュレータを活用して O-RAN アプリケーション間のコンフリクトを再現し，これにより
PACIFISTA の現実環境への適用可能性を実証した．また，RAN シミュレータをコンフリク
ト研究の基盤として活用できる可能性を示し，今後の O-RAN におけるアプリケーション管理
の研究を進展させる一助となることが期待される．},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@phdthesis{Lin2024,
title = {Emergency-Aware Path Planning for Non-connected and Connected Autonomous Vehicles},
author = {Pengfei Lin},
year = {2024},
date = {2024-03-31},
urldate = {2024-03-30},
school = {Ph.D Thesis, The University of Tokyo},
abstract = {Automotive safety is a paramount concern in the ever-evolving world of transportation. With millions of vehicles on the road, ensuring the well-being of drivers, passengers, and pedestrians is a critical priority. Specifically, traffic injuries remain a significant and ongoing threat to human properties, result- ing in a substantial number of casualties every year. Therefore, autonomous vehicles, also known as self-driving cars, are revolutionizing transportation by offering the potential to enhance road safety, reduce congestion, and increase mobility, while reshaping the future of urban and personal mobility.
An autonomous vehicle is equipped with advanced sensors and algorithms to navigate and operate without human intervention. However, despite years of research and development, autonomous vehicles continue to face safety challenges, with a series of traffic accidents still occurring during road tests. Three main challenges are summarized behind those tragedies: sensor failure that false detection or malfunctions lead to incorrect perception; software glitches that incorrect decision or planning paralysis leads to a fatal trajectory; communication Issues that fail to communicate with other road users.
Planning paralysis refers to a situation where the autonomous vehicle struggles to make decisions or take action due to complex, ambiguous, or unforeseen scenarios on the road. This can occur when the vehicle’s algorithms and sensors are overwhelmed by a multitude of factors, such as traffic emergencies, adverse weather conditions, or unfamiliar environments. Address- ing planning paralysis is a significant challenge, as it requires sophisticated decision-making capabilities and robust planning algorithms to ensure safe and efficient navigation in real-world conditions.
In this thesis, I present emergency-aware path planning for autonomous vehicles by incorporating optimization-based planning with two categories: non-connected and connected vehicles. The proposed planning module can stably generate a collision-free path under different traffic emergencies and can adapt to the collaboration intention of surrounding vehicles. This thesis work is composed of three steps that address the planning malfunctions in facing emergencies: (i) construct the risk map from the current traffic by potential field (PF); (ii) plan a collision-free path based on the collaboration intention of surrounding vehicles; (iii) monitor the PF and prepare for emer- gency navigation in the control layer based on the model predictive control (MPC).
Besides, a significant problem for PF-based path planning is that there are four inherent limitations to stop the planner from generating a safe path. In specific traffic scenarios, those limitations can be triggered, leading to plan- ning paralysis. Therefore, monitoring and foreseeing the possible appearance of those limitations is necessary for ensuring driving safety. At the same time, if the planning paralysis becomes irreversible, it is imperative to implant an emergency navigation function in the control module. The experiments also show that our proposed path planning performs better in collision avoidance (stable path generation, curvature, safety, etc.) than previous methods.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
@phdthesis{Tao2024,
title = {Let the Truth Tell: Zero-Knowledge Proof Mechanisms to Realize Fact-Based Cooperative ITS},
author = {Ye Tao },
year = {2024},
date = {2024-03-31},
school = {Ph.D Thesis, The University of Tokyo (Esaki Lab)},
abstract = {The Cooperative Intelligent Transportation System (C-ITS) represents a communication-based technological framework designed to facilitate the ex- change of information among vehicles. This exchange encompasses details pertaining to their presence, observations, and intentions, thereby contribut- ing to the realization of shared objectives, primarily safety and efficiency. Functioning as a distributed and ad-hoc system, C-ITS relies on both direct vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication protocols.
Given its dynamic topology and intermittent global connectivity, C-ITS is susceptible to various cybersecurity threats. Threats such as data ma- nipulation, the creation of ghost vehicles, and the abuse of pseudonymous identities pose significant challenges. These threats can potentially lead to erroneous decision-making by the affected entities.
Misbehavior detection (MBD) is an endeavor to detect and mitigate cyber-attacks in C-ITS. MBD is categorized into two types: data-centric approaches leverage plausibility and consistency to identify potential cyber threats; node-based approaches rely on the past behavior and reputation of a vehicle to assess its likelihood of engaging in dishonest activities. However, both approaches exhibit limitations. Data-centric approaches are constrained to detecting predefined misbehaviors and are susceptible to false positives and negatives. On the other hand, node-centric approaches suffer from high latency in establishing and updating trust assessments, including blocking ongoing attacks.
In contrast, emerging technologies such as blockchain and cryptocurren- cies establish consensus through verifiable facts and employ game theory to discourage dishonest behavior. These fact-based approaches differ markedly from traditional C-ITS methods, which often assume a prevailing honesty among individuals. Demonstrating efficacy across various industries, includ- ing healthcare, education, logistics, and government, fact-based approaches exhibit notable potential for enhancing C-ITS. Several works proposed in this dissertation leverage these fact-based methodologies.
In chapter 3, we introduced Flowsim, a novel simulator tailored for eval- uating Connected and Autonomous Vehicles (CAV) behavior and data flow in large-scale scenarios. The primary objective of Flowsim is to expedite the development and assessment of cybersecurity measures and protocols for CAVs, ensuring their safe and secure deployment. Beyond its focus on CAV evaluation, Flowsim demonstrates versatility in various use cases, including protocol evaluation, cybersecurity assessment, traffic optimization, and pol- icy development. The simulator’s extensible and playable open environment encourages the exploration of innovative algorithms and strategies for CAVs, fostering collaboration and innovation in research communities. Flowsim shows its significance in addressing the limitations of existing simulators for CAV behavior evaluation, specifically for its modularity, extensibility, and performance.
In chapter 4, we introduced a novel deterministic cross-verification method named zero-knowledge Proof of Traffic (zk-PoT) within the context of cooper- ative perception. The zk-PoT protocol utilizes zero-knowledge proofs (ZKPs) to enable vehicles to independently prove their observations’ existence, allow- ing remote parties to cross-verify these observations without relying on the ground truth. The zero-knowledge property ensures that no additional in- formation is disclosed during this process, thereby safeguarding the location privacy of observed vehicles. Integration into existing cooperative perception standards, such as ISO and ETSI, requires minimal architectural changes and ensures backward compatibility. The study concludes with quantitative simu- lation analyses based on Flowsim in the scenario of a full-scaled Luxemburg city, demonstrating zk-PoT’s high success rate, low decision latency, and moderate resource assumption. Threat analyses indicate resilience against common attacks, making it a valuable standalone method or complementary to existing standards and trust management models.
In chapter 5, we addressed the challenge of a type of Sybil attacks in C-ITS, caused by simultaneous usage of pseudonyms. We introduced a novel cryptographic protocol called zero-knowledge Proof of Distinct Identity (zk- PoDI). Based on the ”locality assumption,” wherein a vehicle need only prove its distinction from nearby vehicles rather than every vehicle globally, our proposed protocol zk-PoDI ensures that only a vehicle itself can prove it is not the owner of a given pseudonym locally, preventing Sybil attacks while pre- serving pseudonym unlinkability. The protocol’s practicality is highlighted by its zero latency, moderate computation overhead, and negligible commu- nication costs. zk-PoDI operates independently, without reliance on specific pseudonym system designs or additional knowledge. This feature together with the other practicality makes zk-PoDI ready to be integrated into C-ITS systems with any possible pseudonym designs.
Looking ahead, the exploration of fact-based approaches in C-ITS is poised to continue. Empowered by innovations like zero-knowledge proofs and other cutting-edge techniques, we can anticipate the resolution of nu- merous challenges, the validation of truth, and the safeguarding of privacy within this domain.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
@mastersthesis{古田悟2024,
title = {動的な屋内環境のためのBIMベースの建物デジタルツイン基盤の研究 (BIM-based building digital twin platform for dynamic indoor environment)},
author = {古田悟},
year = {2024},
date = {2024-03-30},
urldate = {2024-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科},
abstract = {近年，デジタルツインと呼ばれる技術が注目を集めている.デジタルツインは，物理的なオ ブジェクトやプロセス，システムをデジタル空間で正確に複製する技術である.この技術は， 実世界のデータを用いて，そのデジタル複製物をリアルタイムで更新し，分析や予測，問題解 決に活用することができる.また計算結果を元に現実世界の制御を行うこともできる.製造業 から都市計画、医療まで幅広い分野での応用が期待されている.本研究では，建築・エンジニ アリング・建設 (AEC: Architecture, Engineering, and Construction)分野におけるデジタルツ イン技術の応用に焦点を当て，特に建物の運用・保守段階で利用する目的での，デジタルツイ ンの汎用アーキテクチャの設計と，アプリケーションシステム実装について取り組んだ.AEC 産業では，個別のユースケースに特化したデジタルツイン実装に留まり，汎用的な参照アーキ テクチャに関する研究が不足している.本研究では，この課題に対応するために，AEC 分野で の建物の運用・保守段階におけるデジタルツインの汎用アーキテクチャを提案した.提案され たアーキテクチャは，AEC 分野で広く使用されている BIM(Building Information Modeling) と統合されることを前提に，建物独自の要件なども考慮して設計されている.また，建物の運 用・保守段階でのデジタルツインの運用における課題を抽出し，これを解決するためのアプリ ケーションシステムを設計・実装した.具体的には，提案された汎用アーキテクチャに合わせ 設計された BIM のメタデータ，ジオメトリデータを扱うバックエンドサーバ群と，Web ブラ ウザ上で動作するフロントエンドアプリケーションとして，BIM をベースにしたデジタルモ デルのデータ認識性を向上させるためのビューアと，現実の屋内状態と同期させるための重畳 表示・編集機能を備えたエディタを開発した.提案されたシステムは既存研究と比較した優位 性を持ち，ユーザスタディや性能測定などによって評価された.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{松本和人2024,
title = {点群データの特徴抽出を用いた深層学習による自己位 置推定精度の予測 (Localizability Estimation for Autonomous Driving: A Deep Learning-Based Feature Extraction Approach)},
author = {松本和人},
year = {2024},
date = {2024-03-30},
urldate = {2024-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科},
abstract = {近年，自動運転の社会実装を目指した研究開発が進んでいる.例えば，サンフランシスコで は，テック企業が無人商用ロボタクシーを公道で走行させた実証実験が行われている. また， 中国では Baidu が武漢市で一般人向けのロボタクシーを走行させ，2023 年 1 月末までの累計 乗車件数は 140 万件を超えており，今後 200 台の自動運転タクシーを追加する予定である.自 動運転を実現するにあたって，車両に取り付けたセンサデータから周囲の環境に関する情報を 取得し，車両の位置を推定することが必要である.これを自己位置推定という.自己位置推定 の手法の中で，3DLiDAR と HD 地図を用いた手法が近年注目されている.この手法はマップ マッチングといい，3DLiDAR からの入力を高解像度マップと照合することで車両の正確な位 置を取得する.マップマッチングは高い精度で自己位置推定が可能であるが，田舎道やトンネ ルなど，周囲に特徴が乏しい場所では自己位置推定精度が低下するという問題がある.
本論文では，深層学習を用いて 3DLiDAR データと点群地図から自己位置推定精度を予測 する手法を提案する.入力点群データを MinkLoc3D と MinkLoc3D-SI という点群データの 特徴を抽出する深層学習モデルを用いて特徴抽出を行い，特徴ベクトルに特徴を埋め込む.そ して，得られた特徴ベクトルから，自己位置推定誤差を予測する.自己位置推定誤差を予測す ることで，誤差が大きい場所に対して事前に対策を施し，安全に走行できるようになる.例え ば，センサを切り替えや，舗装マーカーの設置，ドライバーに運転を促すことなどある.また， 誤差が小さいと予測される場所では，安全に自動運転走行ができるとわかる.深層学習モデル の学習のために，自動運転走行シミュレータの AWSIM と自動運転ソフトウェアの autoware を用いてデータセットを作成した.実験結果によると，94 %以上のケースで自己位置推定誤 差を 0.1m 以下の誤差で予測することができることがわかった.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Cheng2024,
title = {Pedestrian-centric Augmented Reality Visualization of Real-time Autonomous Vehicle Intentions},
author = {Yiwei Cheng},
year = {2024},
date = {2024-03-30},
urldate = {2024-03-31},
school = {Master Thesis, Graduate School of Information Science and Technology, The University of Tokyo},
abstract = {Connected Autonomous Vehicles (CAVs) produce a variety of information within their systems. With the advancement of communication and V2X (Vehicle-to-Everything) com- munication technology, there is a growing challenge to effectively convey this information to pedestrians and enhance their sense of safety when encountering such vehicles. Efforts to communicate this information to pedestrians have been made through various means, with Augmented Reality (AR) emerging as a notable approach. We found that previous studies have yet to integrate a functional AR application with a real-world autonomous driving system. In response to this gap, we proposed an AR application that visualizes real-time data from an active CAV and subsequently developed the system. Furthermore, we conducted field experiments using this developed system and conducted user studies to gather insights into the public’s perception of the system. Our results showed that the system can effectively transmit information from the CAV, and when provided with additional information, people tend to feel safer and confident regarding understanding the vehicle’s intentions.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Jiang2024,
title = {Cooperative Localization for Connected Autonomous Vehicles Utilizing LiDAR-Equipped Infrastructure},
author = {Yuze Jiang},
year = {2024},
date = {2024-03-30},
urldate = {2024-03-31},
address = {Master Thesis, Graduate School of Information Science and Technology, The University of Tokyo (Esaki Lab)},
abstract = {Advancements in LiDAR technology have led to more cost-effective production while simultaneously improving precision and resolution. As a result, LiDAR has become in- tegral to vehicle localization, achieving centimeter-level accuracy through techniques like Normal Distributions Transform (NDT) and other advanced 3D registration algorithms. Nonetheless, these approaches are reliant on high-definition 3D point cloud maps, the cre- ation of which involves significant expenditure. When such maps are unavailable or lack sufficient features for 3D registration algorithms, localization accuracy diminishes, posing a risk to road safety. To address this, we proposed to use LiDAR-equipped roadside unit and Vehicle-to-Infrastructure (V2I) communication to accurately estimate the connected autonomous vehicle’s position and help the vehicle when its self-localization is not accu- rate enough. Our simulation results indicate that this method outperforms traditional NDT scan matching-based approaches in terms of localization accuracy.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{伊藤広記2024,
title = {リアルタイムメディアストリーミングにおけるGENEVEを用いたマルチパス冗長通信フレームワーク(A Framework for Multipath Redundant Communication with GENEVE in Real-Time Media Streaming)},
author = {伊藤広記},
year = {2024},
date = {2024-03-30},
urldate = {2024-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科 (江崎研究室)},
abstract = {インターネットが現代社会の基盤として機能するなか，そこで動くアプリケーションのネットワークへの要求は様々なものになっている．特に近年では，リアルタイムストリーミングをはじめとする超低遅延な通信の需要が増している．
広範なカバレッジをもつモバイルネットワークによって，通信を行う環境も多様になった．しかしながらその安定性は場所や時間帯によって変動し，常に十分とはいえない．通信の信頼性を向上させる手法の一つに冗長通信があり，これまで多くの研究がなされてきた．しかしその多くはTCPによる通信を対象としており，UDPを用いるリアルタイムストリーミングには適用することができない．そこで本研究ではリアルタイムメディアストリーミングの通信品質を向上させることを目的とした，冗長通信フレームワークを提案した．従来の冗長通信システムの課題であったトランスポート層のプロトコルの制限を解決するためにGENEVEによるIPレイヤーでのトンネリングを行った．またリルタイムストリーミングにおいてはアプリケーションがメディア通信に最適化したネットワークの状態に敏感な輻輳制御をおこなっており，これが冗長通信によって引き起こされるパケットの順序の不整合と干渉することが知られている．本研究では冗長通信の受信側においてバッファリングを行うことで，この課題を解決した．実装したシステムを用いて，都心部で走行する車両から複数のモバイルネットワークを利用して冗長通信の実証を行った．実証にはWebRTCを用いたリアルタイムストリーミングアプリケーションを使用し，本フレームワークによってパケットの損失を大幅に減少させられること，アプリケーションの輻輳制御との干渉を回避し既存の冗長通信システムと比較してビットレートが向上することを示した．},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Chauhan2023b,
title = {Optimizing Pedestrian Decision-Making: Leveraging Smart Pole Interaction Unit for Autonomous Vehicle-to-Pedestrian Communication},
author = {Vishal Chauhan },
year = {2023},
date = {2023-09-30},
school = {Master Thesis, Graduate School of Information Science and Technology, The University of Tokyo},
abstract = {Ensuring pedestrian safety remains critical in a world where autonomous vehicles (AVs) and pedestrians coexist. At the same time, AVs exhibit commendable performance on traditional roads with established traffic infrastructure. AVs need to modify their functionality and interactions to suit the unique characteristics of shared spaces, which differ significantly from conventional roads. The concept of shared spaces, often called naked streets, lacks traditional traffic cues, posing challenges in conveying AVs intentions to pedestrians. But, AVs need to be socially acceptable in such environments. Moreover, limited research has been conducted in this specific context of shared spaces with multiple AVs, as past studies have primarily focused on traditional road crossings with one AV interaction scenarios.
This research introduces a novel solution to address right-of-way and accessibility issues in shared spaces by proposing the adoption of a smart pole interaction unit (SPIU). Additionally, the study evaluates the results of integrating an external Human Machine Interface (eHMI) on AVs and SPIU to enhance crosswalk pedestrian safety.
The study demonstrates the efficacy of the SPIU in improving pedestrian safety through simulation scenarios that contrast 1-way and 2-way traffic situations with and without SPIU and eHMI integration. In the 1-way scenario, only one AV approaches, whereas in the 2-way scenario, multiple AVs approach simultaneously. Realistic virtual settings are created using the unreal engine open-source platform, and the proposed systems are evaluated through participants using HTC vive pro 2 headsets.
We conducted a paired t-test for statistical analysis and observed a significant improvement (p = 0.001) when utilizing AV eHMI with SPIU in the case of 1-way stop, 2-way pass and 2-way stop scenarios. However, when examining the 1-way pass scenario, the advantage of SPIU could not be statistically distinguished. The findings reveal that the SPIU significantly enhances pedestrians understanding of AVs approaching intentions in both 1-way and 2-way scenarios, leading to quicker decision-making and reduced cognitive load. Remarkably, with the SPIU addition with the eHMI on vehicles, the participants experienced a noteworthy 21% improvement in response time compared to the baseline, signifying substantial efficiency gains when pedestrians need to stop in 2-way stop. Even in the 1-way pass scenario, there was a modest yet positive 2.19% improvement in response time, highlighting the benefits of SPIU in enhancing interaction dynamics. The most significant improvement of 9.12%, 18.757% was observed in the 2-way pass, 1-way stop scenario, indicating the superior effectiveness of SPIU in 2-way scenarios, 1-way scenarios.
Therefore, these valuable insights shed light on augmenting pedestrian safety in shared spaces with AVs and lay the groundwork for the practical implementation of SPIU to enhance interactions between AVs and pedestrian.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Yosodipuro2023b,
title = {Traffic-load-responsive Intersection Management for Mixed-traffic Conditions},
author = {Nicholaus Danispadmanaba Yosodipuro},
year = {2023},
date = {2023-09-30},
urldate = {2023-09-30},
school = {Master Thesis, Graduate School of Information Science and Technology, The University of Tokyo},
abstract = {Vehicle-to-everything (V2X) communication enables connected autonomous vehicles (CAVs) to share information with other vehicles, road infrastructures, and other road users. In turn, information sharing between CAVs and various road users and infrastructures enables CAVs to generate optimal decisions by considering information previously unavailable to them. Consequently, safety and traffic flow can be improved with the utilization of V2X communication by CAVs. One cutting-edge development derived from the emergence of V2X communication is autonomous intersection management (AIM) methods, which leverage CAVs to simultaneously improve traffic flows and prevent collisions in intersections. In this case, a variety of AIM approaches were developed, such as sharing of future paths of vehicles, ADMM optimization for path generation, and platoon formation. However, these AIM methods assumed 100% CAV market penetration, which is currently unrealistic owing to the gradual adoption of CAVs. Therefore, CAVs must share road usage with nonconnected vehicles (NCVs).
To accommodate mixed-traffic conditions containing CAVs and NCVs, a variety of semi-autonomous intersection management (Semi-AIM) methods were introduced. These Semi-AIM methods can be categorized as signalized or unsignalized, depending on the utilization of traffic lights or lack thereof. Unfortunately, most Semi-AIM methods belonging to either category still have one or more of the following flaws: non-optimal scheduling, significant traffic flow degradation if the traffic has a high NCV ratio, inability to relay right-of-way instructions to NCVs, and inflexibility to variations in traffic loads.
Therefore, we propose a signalized mixed-traffic intersection management method, traffic-load-responsive reservation for intersection management (TLRRIM), which addresses common issues present in existing Semi-AIM methods. In TLRRIM, the roadside unit (RSU) first classifies vehicles and groups them into clusters before selecting a reservation cluster to cross an intersection. The reservation cluster selection considers both traffic load and crossing urgency. In addition, we propose the V2X-enabled speed coordination (VESC) method to be installed on RSU to further improve traffic flow. VESC enables CAVs within the reservation cluster to close the gap between themselves and the vehicles in front of them. Simultaneously, traffic lights are utilized to guide NCVs.
Simulation-based experiments using OpenCDA and CARLA showed that TLRRIM can increase throughput and reduce waiting time by up to 89.63% and 60.71%, respectively, compared to the fixed-time signaling method. Moreover, adding VESC can increase throughput by 12.21% and reduce waiting time by 10.80%. Hence, numerical results show that TLRRIM provides significant traffic flow improvements compared to the fixed-time signaling method, even for situations with 100% NCV. Furthermore, VESC can further enhance the performance of TLRRIM. },
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Inokuchi2023,
title = {デジタルツインの相互運用性向上に向けたデータ資産の包括的管理手法(Comprehensive management approach for data assets to improve interoperability of the Digital Twin)},
author = {井口 和真 (Kazuma Inokuchi)},
year = {2023},
date = {2023-03-31},
urldate = {2023-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科},
abstract = {近年、デジタル技術を人々の生活基盤に援用することを目的として、現実世界とデジタル世 界の高度な融合による情報の利活用が目指されている。特にスマートビルディングと呼ばれる 分野は、エネルギーとコストの最適化や賑わい創出に向けた空間利活用などを目的とする。そ のため、建物要素や什器といった施設内の主要設備に加え、制御プロセスや移動ロボットなど の情報を含めた一元的な状態管理が求められる。その要求を満たす基盤となる概念として、デ ジタルツインが注目を集めている。デジタルツインは、センサから取得した現実世界のあらゆ る情報から、実空間全体をデジタル世界上に複製し、忠実度の高いシミュレーションをおこな う。一方で、デジタルツインはその対象の物理的・時間的・構造的複雑さを原因として、デー タ資産管理が課題となっている。統一的な管理手法が整っていないため、データ間の関係性が 不明瞭となり、プロジェクト間の相互運用性も低下する。そこで本研究では、デジタルツイン のデータ資産を包括的に管理するためのオントロジーである Digital Twin Ontology (DTO) 及 びデータ編集・加工プロセスを定式化するための Data Conversion Ontology (DCO) を提案す る。初めに、DTO では、現実世界のエンティティの状態を柔軟に記述可能なだけでなく、設 計者視点に着目することで、エンティティが抽象化された仮想モデルとの相互連携を満たすフ レームワークとなっている。次に、DCO では、ある “系” から別の “系” への変換をオントロ ジーとして設計することで、データの編集・加工プロセスを定式化する。提案オントロジーの 活用例として、オフィスを模したビル内環境に対して 2 種類の実装をおこなった。DTO に準 拠するリンクトグラフデータセット及びクエリ結果を元に環境を再現するビューアアプリケー ションと、DCO に基づく座標変換システムを実装した。実装を通して、最小限の先験的知識 によりデータ資産を取得可能であることを検証・考察した。また、既存手法との比較やドメイ ンの専門家へのインタビューによる提案オントロジーの内容評価もおこない、提案手法である DTO 及び DCO の有用性を評価した。},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{nokey,
title = {協調型自動運転のための地理的特性を考慮したネットワーク分析・可視化(Geographic-Aware Network Analysis and Visualization for Cooperative Automated Driving)},
author = {神原滉一 (Koichi Kambara)},
year = {2023},
date = {2023-03-31},
urldate = {2023-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Asabe2023,
title = {Reliable Cooperative Perception for Connected Roadside Infrastructure},
author = {Yu Asabe},
year = {2023},
date = {2023-03-31},
urldate = {2023-03-31},
school = {Master Thesis, Graduate School of Information Science and Technology, The University of Tokyo (Esaki Lab)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Nakagawa2023,
title = {5G環境下における WebRTC リアルタイム遠隔コラボレーションのQoE計測(QoE measurement of WebRTC real-time remote collaboration under 5G environment)},
author = {中川 紘輔(Kosuke Nakagawa)},
year = {2023},
date = {2023-03-31},
urldate = {2023-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科 (江崎研究室)},
abstract = {昨今，より低遅延な遠隔コラボレーションへの注目が集まっており，特に自動運転分野にお いても，遠隔監視などのユースケースが提案されている.また，2019 年より世界中でサービス 開始された 5G は，大容量・低遅延通信が期待されているため，遠隔コラボレーションの需要 に対して重要なインフラであると考えられている.しかし，現状の 5G はカバレッジエリアの 拡大やスループットの改善に焦点が当てられており，3GPP などの標準化にて策定された機能 が全て動作しているわけではない. そのため，遠隔コラボレーションの実現に向けた性能・用 途の詳細な検討が必要不可欠である.そこで，本研究では WebRTC を用いた遠隔コラボレー ションを用いて，様々な 5G 環境下での遅延・映像音声品質の計測と分析を行った.具体的に は，WebRTC 通信品質と同時に 5G 通信の電波状況を詳細に調査し，それらの結果データを 統合した. 次に本統合データを分析し，可視化した結果をもとに遠隔コラボレーションが実現 可能なユースケース検討を評価した.本評価において，5 G ミリ波の不安定性が遠隔コラボ レーションにもたらす影響，交通手段(自動車/山手線)と WebRTC 通信の性能の関係，お よび車載環境のシナリオにて同時に 4 つの通信事業者を活用した性能向上の可能性の計 3 つ の観点について，分析により得られた複数の新たな知見を与える.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Hirose2022,
title = {地域に最適化された遺伝的アルゴリズムベースのポテンシャル場による走行計画(Regionally Optimized Path Planning with Genetic Algorithm-Based Potential Fields)},
author = {廣瀬稔彦(Toshihiko Hirose)},
year = {2022},
date = {2022-03-31},
urldate = {2022-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Aotani2022,
title = {デジタルツインのための 3D マップとネットワーク型認識},
author = {青谷和真(Kazuma Aotani)},
year = {2022},
date = {2022-03-31},
urldate = {2022-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科 (江崎・落合研究室)},
abstract = {現実世界を正確に再現し幅広く管理することができるデジタルツインは近年注目を集めている。しかし、デ ジタルツインの新たな作成には、対象となる建物の 3D データの取得や、詳細なメタデータの付与、さらには 広くアクセス可能になることが求められるなど要求事項が多岐にわたる。そこで、3D スキャンにより取得し た 3D データに対し、オントロジーを活用してメタデータを付与し、これらの情報を取得する API を作成し た。また、デジタルツインの作成後にはその活用が重要となるが、その一つとして屋内でのロボットの走行な どが挙げられる。他のセンサを活用し協調的な認識を行うことで効率的な経路計画が期待できるが、点群デー タは容量が大きく、ネットワークリソースを逼迫させるため、そのまま送信することは難しい。そこで、ネッ トワーク状況に応じて送信する点群のエリアを動的に変更することで点群のリアルタイムでの送信を試みた。 結果として、ネットワーク帯域を狭めてもリアルタイムで点群を送信することに成功した。},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Kawamura2022,
title = {工場・ビル管理におけるBIMに基づくデジタルツイン基盤の研究(BIM-based digital twin platform for factory and building management)},
author = {川村地平(Chihei Kawamura)},
year = {2022},
date = {2022-03-31},
urldate = {2022-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科},
abstract = {昨今のコロナ禍では、IoT(Internet of Things) 化の進行による影響も相まって、工場やビ ルなど、現実世界での施設を遠隔で管理することが多く望まれている。このような IoT デバ イスなどの先進技術を用いて、工場やビルの稼働効率向上や管理コスト削減を実現することを スマートファクトリーやスマートビルディングと呼ぶ。これら施設の管理には、工作機械や サーバなどと言った施設内の主要設備に限らず、空調、照明などを始めとした環境操作設備な ども必要とされている。そして、施設の管理には当然施設の状態の可視化も含まれており、こ れには工作機械やサーバ、ロボットなどの状態などに加え、流動的な要素である人の情報など も取れることが望ましく、現実世界での各種要素を管理するため、オブジェクトとして仮想空 間に反映できることが求められている。これらのニーズを満たす基盤はデジタルツインと呼ば れる。我々は、このデジタルツインを活用し、特定のデジタルツインアーキテクチャに基づい たデジタルツインアプリケーションを構築した。本論文では、工場とビルという二つの異なる 基盤に基づいて、それぞれ課題点を定めた上で、その課題点を解決するため、このデジタルツ インアプリケーションを用い、実証実験と検証を行った。それぞれの基盤について、具体的な 課題点として以下の二つを挙げた。まず工場では、施設の状態監視を主目的とし、工作機械の 三次元ジオメトリ、電力消費や稼働状況、周辺の環境情報などを含めた各種情報を、三次元ジ オメトリを用い仮想空間上に再現した工場内にて、立体的に再現することで直感的な工場施設 の可視化を行うという課題を設定した。そして、ビルでは、運搬ロボットや照明、空調設備を 操作するためのシステムを構築し、BIM(Building Information Modeling) や LiDAR(Light Detection And Ranging) といった高精度な環境情報を扱うことでロボットの行動計画をデジ タルツインアプリケーション上から生成することを課題とし、これを解決した。},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Chen2021b,
title = {Reinforcement Learning Based Optimal Camera Placement for Depth Observation of Indoor Scenes},
author = {Yichuan Chen},
year = {2021},
date = {2021-09-30},
urldate = {2021-09-30},
school = {Master Thesis, Graduate School of Information Science and Technology, The University of Tokyo (Esaki Lab)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Tsujio2021,
title = {自動運転車の交通流効率化のための公平性を考慮した協調経路計画 (Fairness-aware Cooperative Path Planning for Efficient Traffic Flow of Connected and Autonomous Vehicles)},
author = {辻尾 康平(Kohei Tsujio)},
year = {2021},
date = {2021-09-30},
urldate = {2021-09-30},
school = {修士論文, 東京大学大学院 情報理工学系研究科},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Hirata2021,
title = {自動運転車を支援する時空間予測を用いたエッジ型協調計画 (Edge Assisted Cooperative Planning Using Future Spatio-Temporal Prediction for Autonomous Driving)},
author = {平田 真唯 (Mai Hirata)},
year = {2021},
date = {2021-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科 (江崎・落合研究室)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Mizutani2021,
title = {協調型自動運転のための機能抽象化した走行調停サービス (Maneuver coordination service with abstracted functions for cooperative autonomous driving)},
author = {水谷 将也(Masaya mizutani)},
year = {2021},
date = {2021-03-31},
urldate = {2021-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科 (江崎・落合研究室)},
abstract = {近年、自律型自動運転の研究開発が盛んに行われている。自律型自動運転には死角の存在、 全体最適な経路計画の限界など課題が存在する。これを解決するために、車車間通信を利用す る協調型自動運転の研究も盛んである。協調型自動運転用のネットワークアーキテクチャや メッセージは各団体で標準化されているが、走行調停のためのメッセージは未だ議論の段階 で、標準化されていない。本研究では、安全性・汎用性・コーナケースを考慮した走行調停用 のプロトコルとサービスの設計を提案した。プロトコルは、コーナケースや安全性を考慮し、 メッセージを 7 種類に分け、各ステーションを状態で管理する設計とした。また、サービス は、汎用性を持たせるために、各シナリオが共通して使用する機能を抽象化する設計とした。
実験により、提案した走行調停用プロトコルが有用であることを示すために、自律型自動 運転用ソフトウェアである Autoware と協調運転用ソフトウェアの OpenC2X を拡張し、走 行調停サービスを実装した。シミュレータを用いて、4 台での走行調停が実現できることを示 し、通信量、各プロセスの実行時間、MCM による交通の快適性への効果を明らかにした。},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Yang2021,
title = {Blockchain based Distributed Trust Management with security enhancement inCooperative-ITS},
author = {Bodong Yang},
year = {2021},
date = {2021-03-31},
school = {Master Thesis, Graduate School of Information Science and Technology, The University of Tokyo (Mari Inaba Lab)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Masuda2021,
title = {協調型ITSにおけるV2Xセンサ統合のための車両特定フレームワーク (Vehicle Identification Platform for V2X-Sensor Fusion in Cooperative ITS)},
author = {増田 英孝 (Hidetaka Masuda)},
year = {2021},
date = {2021-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科 (江崎研究室)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Kato2021,
title = {時空間メディアを管理するオントロジーとインタラクティブ視聴体験への応用 (An Ontology for Spatio-Temporal Media Management and Application to Interactive 3D Audio-Visual Viewer)},
author = {加藤 慎 (Shin Kato)},
year = {2021},
date = {2021-03-31},
urldate = {2021-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科 (江崎・落合研究室)},
abstract = {メディアコンテンツの制作は収録，編集・加工，配信，再生の四段階に分割して考えることが できる.著者が所属する SDM (Software Defined Media) コンソーシアムでは，三次元映像・ 音声メディアを管理するための構造フレームワークとして SDM Ontology の提案を進めてき た.SDM Ontology は制作フローの収録段階を対象として，様々かつ膨大なメタデータを階 層構造に整理し，RDF (Resource Description Framework) による記述，LOD (Linked Open Data) の作成によってオープンデータとしてアクセスを可能にした.本研究では，メディア 収録段階より先の制作フローに焦点を当て，特にメディア編集・加工段階を SDM Ontology の記述対象範囲に加えるとともに，従来の構造について考察し直し，音楽イベントに限らず 時空間情報をもつメディアを管理する，より汎用的なフレームワークとして SDM Ontology Version 2 を提案する.また，SDM Ontology Version 2 の活用例として，インタラクティブ に 360◦ 動画および立体音響を視聴体験できる Web アプリケーション「Web3602」を開発し た.Web3602 は SDM Ontology Version 2 に準拠するクラシックコンサートおよびジャズ セッションイベントのデータセットを問い合わせて動作する.このアプリケーションについ て，Interop Tokyo 2019 での対面アンケート，2020 年 12 月より開始した Web アンケートの 2 種類の主観評価を実施し，インタラクティブ視聴体験の有効性や異なる視聴環境が及ぼす影 響を検証・考察した.さらに，SDM Ontology Version 2 の普及活動の一環として，オントロ ジーの語彙体系を説明する Web ドキュメントを作成および公開し，LOD の普及促進を目的に 毎年開催される「LOD チャレンジ」のアプリケーション部門に Web3602，データセット部門 に SDM Ontology Version 2 準拠のデータセットを提出した.LOD チャレンジにおいては， データセット部門への応募作品で最優秀賞およびスポンサー賞を受賞した.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@bachelorthesis{Shim2021,
title = {VANETにおける知覚情報共有を利用したPseudonym対応の不正行為検出},
author = {有井慎平(Shimpei Arii)},
year = {2021},
date = {2021-03-31},
urldate = {2021-03-31},
organization = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
school = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
@bachelorthesis{kou2021,
title = {WebRTC による宅内環境間P2P ラウンドトリップタイム計測},
author = {中川紘輔(Kousuke Nakagawa)},
year = {2021},
date = {2021-03-31},
urldate = {2021-03-31},
organization = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
school = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
@phdthesis{Kasuya2020,
title = {BIMを用いたSoftware Defined BACSの実現に関する研究 (The design and implementation of Software Defined BACS based on shared BIM repository)},
author = {粕谷 貴司(Takashi Kasuya)},
year = {2020},
date = {2020-09-30},
urldate = {2020-09-30},
school = {Ph.D Thesis, The University of Tokyo (Esaki Lab)},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
@bachelorthesis{Inokuchi2020,
title = {co-Sound: Web ARによるインタラクティブな視聴メディア及び空間同期},
author = {井口 和真 (Kazuma Inokuchi)},
year = {2020},
date = {2020-03-31},
urldate = {2020-03-31},
organization = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
school = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
@bachelorthesis{Ito2020,
title = {V2X協調型運転システムにおける通信性能のリアルタイム可視化ツール},
author = {伊藤 彰秀(Akihide Ito)},
year = {2020},
date = {2020-03-31},
urldate = {2020-03-31},
organization = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
school = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
@mastersthesis{Oi2020,
title = {路車間通信を利用した自動走行のための協調認識システムの提案と評価 (Infrastructure-aided cooperative perception for connected autonomous vehicles)},
author = {大井貴晴 (Takaharu Oi)},
url = {https://tlab.hongo.wide.ad.jp/papers/2020_M_Oi.pdf},
year = {2020},
date = {2020-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科 (江崎・落合研究室)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@bachelorthesis{Unno2020,
title = {指向性のある音声の到達範囲を可視化するARシステム},
author = {海野亮 (Ryo Unno)},
year = {2020},
date = {2020-03-31},
urldate = {2020-03-31},
organization = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
school = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
@mastersthesis{昌大2019b,
title = {路側装置ネットワークによる安全を優先した車両認識メッセージの広域送信（ Safety Oriented Wide Dissemination of Roadside-assisted Cooperative Awareness Message）},
author = {北沢 昌大 (Masahiro Kitazawa)},
url = {http://hdl.handle.net/2261/00077004},
year = {2019},
date = {2019-03-31},
school = {修士論文, 東京大学大学院 情報理工学系研究科 (江崎・落合研究室)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{琢郎2019,
title = {立体音響を用いる小型サラウンドスピーカー環境の研究（Research of Compact Surround Speakers for 3D Audio）},
author = {庄子 琢郎 (Takuro Shoji)},
url = {http://koara.lib.keio.ac.jp/xoonips/modules/xoonips/detail.php?koara_id=KO40001001-00002018-0679},
year = {2019},
date = {2019-03-31},
school = {修士論文, 慶應義塾大学大学院 メディアデザイン研究科},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{裕2018,
title = {Software Defined Media Ontology : 分散収録環境のための視聴空間の再現フレームワーク（Software Defined Media Ontology : A framework for reproduction of audio-visual contents by distributed recording environment）},
author = {菰原 裕 (Yu Komohara)},
url = {http://hdl.handle.net/2261/00074653?.pdf},
year = {2018},
date = {2018-03-31},
school = {修士論文, 情報理工学系研究科電子情報学専攻（江崎・落合研究室）},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@bachelorthesis{昌大2017b,
title = {協調型ITSにおける携帯網を併用した車車間通信の支援},
author = {北沢 昌大 (Masahiro Kitazawa)},
year = {2017},
date = {2017-03-31},
urldate = {2017-03-31},
organization = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
school = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
@mastersthesis{Ye2016,
title = {DUPE: Duplicated Unicasting Packet Encapsulation in Position Based Routing VANET},
author = {Ye Tao},
year = {2016},
date = {2016-03-31},
urldate = {2016-03-31},
school = {Master Thesis, Graduate School of Information Science and Technology, The University of Tokyo (Esaki, Ochiai Lab)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@mastersthesis{Ogawa2016,
title = {Tangible sound : a tangible interface for object-based sound system},
author = {Keiko Ogawa},
url = {http://koara.lib.keio.ac.jp/xoonips/modules/xoonips/detail.php?koara_id=KO40001001-00002015-0432},
year = {2016},
date = {2016-03-31},
school = {Master’s thesis, KMD:Graduate School of Media Design, Keio University},
note = {研究科委員長表彰 (Dean's List Award)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
@bachelorthesis{明日香2016,
title = {複数ユーザで共有される3次元仮想空間の同期プラットフォームの一提案},
author = {戸間 明日香 (Asuka Toma)},
year = {2016},
date = {2016-03-31},
urldate = {2016-03-31},
organization = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
school = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
@bachelorthesis{知也2016b,
title = {協調型ITS における車々間メッセージ代理生成・送信システムの設計},
author = {北里 知也 (Tomoya Kitazato)},
year = {2016},
date = {2016-03-30},
urldate = {2016-03-30},
organization = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
school = {卒業論文, 東京大学 電子情報工学科（江崎・落合研究室）},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
We are part of the University of Tokyo’s Graduate School of Information Science and Technology, Department of Creative Informatics and focuses on computer networks and cyber-physical systems
4F, I-REF building, Graduate School of Information Science and Technology, The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo, 113-8657 Japan
Room 91B1, Bld 2 of Engineering Department, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
(Contact)
