Publication
2025
Dongyang Li, Ehsan Javanmardi, Manabu Tsukada, "State-Guided Spatial Cross-Attention for Enhanced End-to-End Autonomous Driving", In: IEEE International Automated Vehicle Validation Conference (IAVVC 2025), Baden-Baden, Germany, 2025.Proceedings Article | Abstract | BibTeX
@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}
}
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.
松澤力, Naren Bao, Ehsan Javanmardi, 塚田 学 , "V2X通信における占有格子地図の共有とAutowareを用いた検証", マルチメディア，分散，協調とモバイル(DICOMO2025)シンポジウム, 福島、母畑温泉, 2025.Conference | BibTeX
@conference{松澤力2025,
title = {V2X通信における占有格子地図の共有とAutowareを用いた検証},
author = {松澤力 and Naren Bao and Ehsan Javanmardi and 塚田 学 },
year = {2025},
date = {2025-06-25},
booktitle = {マルチメディア，分散，協調とモバイル(DICOMO2025)シンポジウム},
address = {福島、母畑温泉},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Yuze Jiang, Ehsan Javanmardi, Manabu Tsukada, Hiroshi Esaki, "Towards Efficient Roadside LiDAR Deployment: A Fast Surrogate Metric Based on Entropy-Guided Visibility", In: 36th IEEE Intelligent Vehicles Symposium (IV2025), Cluj-Napoca, Romania, 2025.Proceedings Article | Abstract | BibTeX | Links:
@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}
}
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.
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.
Yun Li, Ehsan Javanmardi, Simon Thompson, Kai Katsumata, Alex Orsholits, Manabu Tsukada, "PrefDrive: Enhancing Autonomous Driving through Preference-Guided Large Language Models", In: 36th IEEE Intelligent Vehicles Symposium (IV2025), Cluj-Napoca, Romania, 2025.Proceedings Article | Abstract | BibTeX | Links:
@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}
}
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.
Manabu Tsukada, Ehsan Javanmardi, "End-to-End自動運転時代のV2X通信技術", 自動車技術会フォーラム2025年度「フォーラムYOKOHAMA」、【25-Y7】社会変革と次世代のモビリティ, パシフィコ横浜, Japan, 22.05.2025.Presentation | Abstract | BibTeX | Links:
@misc{Tsukada2025,
title = {End-to-End自動運転時代のV2X通信技術},
author = {Manabu Tsukada and Ehsan Javanmardi},
url = {https://www.jsae.or.jp/assoc/event/gakkai/forum/2025ver/prog_25-Y7/},
year = {2025},
date = {2025-05-22},
urldate = {2025-05-22},
address = {パシフィコ横浜, Japan},
abstract = {自動運転はルールベースからEnd-to-End(E2E)へ移行し、V2X通信も進化が求められる。本講演では、E2E時代のV2Xにおける課題、特に特徴共有と異種混合性(Heterogeneity)に焦点を当てる。解決策としてデータ基盤(Co3SOP)、適応的協調認識(PHCP)、嗜好学習(PrefDrive)の研究アプローチを紹介する。},
howpublished = {自動車技術会フォーラム2025年度「フォーラムYOKOHAMA」、【25-Y7】社会変革と次世代のモビリティ},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
自動運転はルールベースからEnd-to-End(E2E)へ移行し、V2X通信も進化が求められる。本講演では、E2E時代のV2Xにおける課題、特に特徴共有と異種混合性(Heterogeneity)に焦点を当てる。解決策としてデータ基盤(Co3SOP)、適応的協調認識(PHCP)、嗜好学習(PrefDrive)の研究アプローチを紹介する。
Muhammad Asad, Saima Shaukat, Jin Nakazato, Ehsan Javanmardi, Manabu Tsukada, "Federated Learning for Secure and Efficient Vehicular Communications in Open RAN", In: Cluster Computing, vol. 28, no. 211, 2025, ISSN: 1386-7857.Journal Article | Abstract | BibTeX | Links:
@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}
}
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.
2024
Pengfei Lin, Ehsan Javanmardi, Yuze Jiang, Manabu Tsukada, "A Rule-Compliance Path Planner for Lane-Merge Scenarios Based on Responsibility-Sensitive Safety", In: 2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV), Dubai, UAE, 2024.Proceedings Article | Abstract | BibTeX | Links:
@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}
}
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.
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.
Vishal Chauhan, Anubhav Anubhav, Chia-Ming Chang, Jin Nakazato, Ehsan Javanmardi, Alex Orsholits, Takeo Igarashi, Kantaro Fujiwara, Manabu Tsukada, "Connected Shared Spaces: Expert Insights into the Impact of eHMI and SPIU for Next-Generation Pedestrian-AV Communication", In: International Conference on Intelligent Computing and its Emerging Applications (ICEA2024), Tokyo, Japan, 2024.Proceedings Article | BibTeX
@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}
}
Koichi Kambara, Ehsan Javanmardi, Jin Nakazato, Shunya Yamada, Hiroaki Takada, Yousuke Watanabe, Kenya Sato, Manabu Tsukada, "Geographic-Aware Network Analysis and Visualization System for CAVs", In: International Conference on Intelligent Computing and its Emerging Applications (ICEA2024), Tokyo, Japan, 2024.Proceedings Article | BibTeX
@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}
}
Romina Zakerian, Ehsan Javanmardi, Manabu Tsukada, Mahdi Javanmardi, Mohammad Rahmati, "Unsupervised Person re-identification Using Generative Adversarial Networks", In: International Conference on Intelligent Computing and its Emerging Applications (ICEA2024), Tokyo, Japan, 2024.Proceedings Article | BibTeX
@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}
}
Dongyang Li, Ehsan Javanmardi, Naren Bao, Manabu Tsukada, "Cross-Attention Enhanced Imitation Learning for End-to-end Autonomous Driving in Unprotected Turns", In: International Conference on Intelligent Computing and its Emerging Applications (ICEA2024), Tokyo, Japan, 2024, (Best Paper Award (Silver)).Proceedings Article | Abstract | BibTeX
@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}
}
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.
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.
Fardin Ayar, Ehsan Javanmardi, Manabu Tsukada, Mahdi Javanmardi, Mohammad Rahmati, "LiDAR-Camera Fusion for Video Panoptic Segmentation without Video Training", In: International Conference on Intelligent Computing and its Emerging Applications (ICEA2024), Tokyo, Japan, 2024, (Best Paper Award (Bronze)).Proceedings Article | Abstract | BibTeX | Links:
@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}
}
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.
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.
Minoo Dolatabadi, Fardin Ayar, Ehsan Javanmardi, Manabu Tsukada, Mahdi Javanmardi, "Neural Error Covariance Estimation for Precise LIDAR Localization", In: International Conference on Intelligent Computing and its Emerging Applications (ICEA2024), Tokyo, Japan, 2024.Proceedings Article | BibTeX | Links:
@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}
}
Mohammad Ali Rezaei, Fardin Ayar, Ehsan Javanmardi, Manabu Tsukada, Mahdi Javanmardi, "Where Do You Go? Pedestrian Trajectory Prediction using Scene Features", In: International Conference on Intelligent Computing and its Emerging Applications (ICEA2024), Tokyo, Japan, 2024.Proceedings Article | BibTeX | Links:
@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}
}
Xinyue Gui, Ehsan Javanmardi, Stela Hanbyeol Seo, Vishal Chauhan, Chia-Ming Chang, Manabu Tsukada, Takeo Igarashi, ""Text + Eye" on Autonomous Taxi to Provide Geospatial Instructions to Passenger", In: Proceedings of the 12th International Conference on Human-Agent Interaction(HAI 2024), pp. 429-431, Swansea University, UK, 2024.Proceedings Article | Abstract | BibTeX | Links:
@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}
}
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.
Yun Li, Kai Katsumata, Ehsan Javanmardi, Manabu Tsukada, "Large Language Models for Human-like Autonomous Driving Decision Making: A Survey", In: 27th IEEE International Conference on Intelligent Transportation Systems (ITSC 2024), Edmonton, Canada, 2024.Proceedings Article | Abstract | BibTeX | Links:
@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}
}
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.
Yuze Jiang, Ehsan Javanmardi, Manabu Tsukada, Hiroshi Esaki, "Accurate Cooperative Localization Utilizing LiDAR-equipped Roadside Infrastructure for Autonomous Driving", In: 27th IEEE International Conference on Intelligent Transportation Systems (ITSC 2024), Edmonton, Canada, 2024.Proceedings Article | Abstract | BibTeX | Links:
@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}
}
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.
Vishal Chauhan, Anubhav Anubhav, Chia-Ming Chang, Jin Nakazato, Ehsan Javanmardi, Alex Orsholits, Takeo Igarashi, Kantaro Fujiwara, Manabu Tsukada
, "Transforming Pedestrian and Autonomous Vehicles Interactions in Shared Spaces: A Think-Tank Study on Exploring Human-Centric Designs", In: 16th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI 2024), Work in Progress (WiP), pp. 1-8, California, USA, 2024.Proceedings Article | Abstract | BibTeX | Links:
@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}
}
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.
Raphael Trumpp, Ehsan Javanmardi, Jin Nakazato, Manabu Tsukada, Marco Caccamo, "RaceMOP: Mapless Online Path Planning for Multi-Agent Autonomous Racing using Residual Policy Learning", In: The 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024), Abu Dhabi ,UAE, 2024.Proceedings Article | Abstract | BibTeX | Links:
@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}
}
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.
Yu Asabe, Ehsan Javanmardi, Jin Nakazato, Manabu Tsukada, Hiroshi Esaki, "Enhancing Reliability in Infrastructure-based Collective Perception: A Dual-Channel Hybrid Delivery Approach with Real-Time Monitoring", In: IEEE Open Journal of Vehicular Technology, vol. 5, pp. 1124-1138, 2024, ISSN: 2644-1330.Journal Article | Abstract | BibTeX | Links:
@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}
}
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.
Pengfei Lin, Ehsan Javanmardi, Manabu Tsukada, "Clothoid Curve-based Emergency Stopping Path-Planning with Adaptive Potential Field for Autonomous Vehicles", In: IEEE Transactions on Vehicular Technology, vol. 73, iss. 7, pp. 9747-9762, 2024, ISSN: 0018-9545.Journal Article | Abstract | BibTeX | Links:
@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}
}
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.
Ye Tao, Hongyi Wu, Ehsan Javanmardi, Manabu Tsukada, Hiroshi Esaki, "Zero-Knowledge Proof of Distinct Identity: a Standard-compatible Sybil-resistant Pseudonym Extension for C-ITS", In: 35th IEEE Intelligent Vehicles Symposium (IV2024), Jeju Island, Korea, 2024.Proceedings Article | Abstract | BibTeX | Links:
@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}
}
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.
Dou Hu, Jin Nakazato, Ehsan Javanmardi, Muhammad Asad, Maruta Kazuki, Manabu Tsukada, "A Research of Kalman Filter enabled Beam Tracking for Multiple Vehicles", ASPIRE Workshop 2024 in conjunction with the IEICE General Conference, Hiroshima, Japan, 2024, ISBN: 2188-5079.Workshop | Abstract | BibTeX | Links:
@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}
}
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.
Muhammad Asad, Saima Shaukat, Ehsan Javanmardi, Jin Nakazato, Naren Bao, Manabu Tsukada, "Secure and Efficient Blockchain-based Federated Learning Approach For VANETs", In: IEEE Internet of Things Journal, vol. 11, iss. 5, pp. 9047-9055, 2024, ISSN: 2327-4662.Journal Article | Abstract | BibTeX | Links:
@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}
}
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.
鈴木健吾, 中里仁, 丸田一輝, エッサン ジャワーンマーディ, 塚田学, 江崎浩, "協調型自動運転における複数路側機からの安定したビーム送信の検討", 無線通信システム研究会（RCS）, 東北大学 青葉記念会館, 2024.Conference | BibTeX
@conference{鈴木健吾2024,
title = {協調型自動運転における複数路側機からの安定したビーム送信の検討},
author = {鈴木健吾 and 中里仁 and 丸田一輝 and エッサン ジャワーンマーディ and 塚田学 and 江崎浩},
year = {2024},
date = {2024-01-18},
urldate = {2024-01-18},
booktitle = {無線通信システム研究会（RCS）},
address = {東北大学 青葉記念会館},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Ryo Iwaki, Jin Nakazato, Muhammad Asad, Ehsan Javanmardi, Kazuki Maruta, Manabu Tsukada, Hideya Ochiai, Hiroshi Esaki, "Optimizing mmWave Beamforming for High-Speed Connected Autonomous Vehicles: An Adaptive Approach", 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC), Poster, 2024.Miscellaneous | Abstract | BibTeX | Links:
@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}
}
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.
2023
Ye Tao, Ehsan Javanmardi, Pengfei Lin, Yuze Jiang, Jin Nakazato, Manabu Tsukada, Hiroshi Esaki, "Zero-Knowledge Proof of Traffic: A Deterministic and Privacy-Preserving Cross Verification Mechanism for Cooperative Perception Data", In: IEEE Access, vol. 11, pp. 142846-142861, 2023, ISSN: 2169-3536.Journal Article | Abstract | BibTeX | Links:
@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}
}
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.
Kazuto Matsumoto, Ehsan Javanmardi, Jin Nakazato, Manabu Tsukada, "Localizability Estimation for Autonomous Driving: A Deep Learning-Based Place Recognition Approach", In: IEEE Robotic Computing 2023, California, USA, 2023.Proceedings Article | Abstract | BibTeX | Links:
@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}
}
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.
岩城燎, 中里仁, 小澤爽仁, 丸田一輝, ムハマド アサード, エッサン ジャワーンマーディ, 塚田学, 落合秀也, 江崎浩, "一般道路環境における高速ビーム追従の適応的アルゴリズムの提案", 無線通信システム研究会（RCS）, 熊本県, 2023.Conference | Abstract | BibTeX | Links:
@conference{Iwaki2023,
title = {一般道路環境における高速ビーム追従の適応的アルゴリズムの提案},
author = {岩城燎 and 中里仁 and 小澤爽仁 and 丸田一輝 and ムハマド アサード and エッサン ジャワーンマーディ and 塚田学 and 落合秀也 and 江崎浩},
url = {https://tlab.hongo.wide.ad.jp/papers/2023_RCS_Iwaki.pdf},
year = {2023},
date = {2023-11-15},
urldate = {2023-11-15},
booktitle = {無線通信システム研究会（RCS）},
address = {熊本県},
abstract = {2019年より世界中でサービスが開始された5Gでは，ミリ波が移動体通信として初めて導入された．ミリ波は直進性が強く，回り込みしない特徴からカバレッジが小さいスモールセルへの導入がされている．一方で，様々な交通システムが協調的に認知，判断，実行を担える協調型自動運転では，通信に常時繋がることが前提とされている．高速移動する車両に対して低遅延であるミリ波を用いるためには5Gにおけるビームフォーミングでは高速で追従できない課題がある．そこで本稿では，その課題の1つの解決策となる，高速で移動する車両に対し高速にビーム追従を行うビーム追従アルゴリズムについて，交通シミュレータと連携させることでより一般的な環境で評価することを行うことを可能とする．さらに，ビーム探索数の増加と探索次元の増加方式を提案し，一般的な道路環境にて評価し，適応的なアルゴリズムであることを示した．
},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
2019年より世界中でサービスが開始された5Gでは，ミリ波が移動体通信として初めて導入された．ミリ波は直進性が強く，回り込みしない特徴からカバレッジが小さいスモールセルへの導入がされている．一方で，様々な交通システムが協調的に認知，判断，実行を担える協調型自動運転では，通信に常時繋がることが前提とされている．高速移動する車両に対して低遅延であるミリ波を用いるためには5Gにおけるビームフォーミングでは高速で追従できない課題がある．そこで本稿では，その課題の1つの解決策となる，高速で移動する車両に対し高速にビーム追従を行うビーム追従アルゴリズムについて，交通シミュレータと連携させることでより一般的な環境で評価することを行うことを可能とする．さらに，ビーム探索数の増加と探索次元の増加方式を提案し，一般的な道路環境にて評価し，適応的なアルゴリズムであることを示した．
Naren Bao, Jin Nakazato, Muhammad Asad, Ehsan Javanmardi, Manabu Tsukada, "Towards a Trusted Inter-Reality: Exploring System Architectures for Digital Identification", The 1st International Workshop on Internet of Realities (IoR-WS 2023) at International Conference on the Internet of Things, Nagoya, Japan, 2023.Workshop | Abstract | BibTeX | Links:
@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}
}
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.
Yiwei Cheng, Jin Nakazato, Ehsan Javanmardi, Chia-Ming Chang, Manabu Tsukada, "Pedestrian-centric Augmented Reality Visualization of Real-time Autonomous Vehicle Dynamics", The Workshop on Intelligent Cloud Continuum for B5G Services in the IEEE International Conference on Cloud Networking (CloudNet) 2023, New York City, USA, 2023.Workshop | Abstract | BibTeX | Links:
@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}
}
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.
Pengfei Lin, Ehsan Javanmardi, Jin Nakazato, Manabu Tsukada, "Potential Field-based Path Planning with Interactive Speed Optimization for Autonomous Vehicles", In: 49th Annual Conference of the IEEE Industrial Electronics Society (IECON 2023), 2023.Proceedings Article | Abstract | BibTeX | Links:
@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}
}
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.
Vishal Chauhan, Chia-Ming Chang, Ehsan Javanmardi, Jin Nakazato, Koki Toda, Pengfei Lin, Takeo Igarashi, Manabu Tsukada, "Keep Calm and Cross: Smart Pole Interaction Unit for Easing Pedestrian Cognitive Load", In: The 9th IEEE World Forum on Internet of Things (IEEE WFIoT2023), Aveiro, Portugal, 2023.Proceedings Article | Abstract | BibTeX | Links:
@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}
}
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).
Vishal Chauhan, Chia-Ming Chang, Ehsan Javanmardi, Jin Nakazato, Pengfei Lin, Takeo Igarashi, Manabu Tsukada, "Fostering Fuzzy Logic in Enhancing Pedestrian Safety: Harnessing Smart Pole Interaction Unit for Autonomous Vehicle-to-Pedestrian Communication and Decision Optimization", In: Electronics, vol. 12, no. 20, 2023, ISSN: 2079-9292.Journal Article | Abstract | BibTeX | Links:
@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}
}
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.
Hu Dou, Jin Nakazato, Ehsan Javanmardi, Muhammad Asad, Manabu Tsukada, Kazuki Maruta
, "Extended Kalman filter based beam tracking for vehicle position and velocity estimation under intersection scenario", 革新的無線通信技術に関する横断型研究会（MIKA）, 沖縄県, 2023.Conference | Abstract | BibTeX
@conference{Dou2023,
title = {Extended Kalman filter based beam tracking for vehicle position and velocity estimation under intersection scenario},
author = {Hu Dou and Jin Nakazato and Ehsan Javanmardi and Muhammad Asad and Manabu Tsukada and Kazuki Maruta
},
year = {2023},
date = {2023-10-10},
urldate = {2023-10-10},
booktitle = {革新的無線通信技術に関する横断型研究会（MIKA）},
address = {沖縄県},
abstract = {As typified by the IoT, mobile traffic continues to increase with the spread of devices equipped with wireless communication functions. Deploying small cell base stations (BSs) is known to straight forward way to efficiently support such traffic. Meanwhile, the facility cost increases when large numbers of BSs are deployed in a fixed manner. It is possible to construct an efficient wireless network by installing BS functions in moving objects such as vehicles and UAVs, and allowing them to move autonomously or activate wireless functions to follow the traffic demand. This paper proposes a method for estimating propagation channels and vehicle position/velocity information at intersections based on vehicle-oriented beam control using a Kalman filter.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
As typified by the IoT, mobile traffic continues to increase with the spread of devices equipped with wireless communication functions. Deploying small cell base stations (BSs) is known to straight forward way to efficiently support such traffic. Meanwhile, the facility cost increases when large numbers of BSs are deployed in a fixed manner. It is possible to construct an efficient wireless network by installing BS functions in moving objects such as vehicles and UAVs, and allowing them to move autonomously or activate wireless functions to follow the traffic demand. This paper proposes a method for estimating propagation channels and vehicle position/velocity information at intersections based on vehicle-oriented beam control using a Kalman filter.
Pengfei Lin, Ehsan Javanmardi, Jin Nakazato, Manabu Tsukada, "Occlusion-Aware Path Planning for Collision Avoidance: Leveraging Potential Field Method with Responsibility-Sensitive Safety", In: The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023), 2023.Proceedings Article | Abstract | BibTeX | Links:
@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}
}
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.
Nicholaus Danispadmanaba Yosodipuro, Ehsan Javanmardi, Jin Nakazato, Yasumasa Tamura, Xavier Defago, Manabu Tsukada, "Mixed-traffic Intersection Management using Traffic-load-responsive Reservation and V2X-enabled Speed Coordination", In: The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023), Bilbao, Bizkaia, Spain, 2023.Proceedings Article | Abstract | BibTeX | Links:
@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}
}
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.
Ye Tao, Ehsan Javanmardi, Jin Nakazato, Manabu Tsukada, Hiroshi Esaki, "Flowsim: A Modular Simulation Platform for Microscopic Behavior Analysis of City-Scale Connected Autonomous Vehicles", In: The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023), Bilbao, Bizkaia, Spain, 2023.Proceedings Article | Abstract | BibTeX | Links:
@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}
}
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.
Yuji Yamazaki, Yasumasa Tamura, Xavier Defago, Ehsan Javanmardi, Manabu Tsukada, "ToST: Tokyo SUMO traffic scenario ", In: The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023), Bilbao, Bizkaia, Spain, 2023.Proceedings Article | Abstract | BibTeX | Links:
@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}
}
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.
Muhammad Asad, Saima Shaukat, Dou Hu, Zekun Wang, Ehsan Javanmardi, Jin Nakazato, Manabu Tsukada, "Limitations and Future Aspects of Communication Costs in Federated Learning: A Survey", In: Sensors, vol. 23, no. 17, 2023, ISSN: 1424-8220.Journal Article | Abstract | BibTeX | Links:
@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}
}
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.
松本 和人, Ehsan Javanmardi, 中里 仁, 塚田 学, "深層学習を用いた自動運転向け自己位置推定精度の予測", マルチメディア，分散，協調とモバイル(DICOMO2023)シンポジウム, 富山, 2023.Conference | Abstract | BibTeX | Links:
@conference{Matsumoto2023,
title = {深層学習を用いた自動運転向け自己位置推定精度の予測},
author = {松本 和人 and Ehsan Javanmardi and 中里 仁 and 塚田 学},
url = {https://tlab.hongo.wide.ad.jp/papers/2023_DICOMO_matsumoto.pdf},
year = {2023},
date = {2023-07-05},
urldate = {2023-07-05},
booktitle = {マルチメディア，分散，協調とモバイル(DICOMO2023)シンポジウム},
address = {富山},
abstract = {近年，自動運転の社会実装に向けた研究開発や実証実験が盛んに行われている．自動運転を実現するにあたって，センサ情報から周囲の環境の情報を取得し，車体の位置を推定する必要がある．これを自己位置推定という．自己位置推定のセンサには3DLiDARがよく用いられる．3DLiDARは測定精度が高く，周囲の明るさの影響を受けないため，高精度に自己位置推定を行えるが，周囲に特徴物が少ないところでは自己位置推定の精度が低下するという課題がある．本研究では，自己位置推定の精度を予測する手法を提案する．自己位置推定精度の予測を行い，精度が悪い場所に対してGNSSやIMUなど3DLiDAR以外のセンサを用いたり，舗装マーキングを用いたりすることで，全体的な自己位置推定の精度を向上させることができる．オープンソース自動運転シミュレータを用いて自己位置推定精度予測のためのデータセットを作成した．実験では作成したデータセットに対して提案手法を行った．結果として，自己位置推定精度を高精度で予測できたことを報告する．
},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
近年，自動運転の社会実装に向けた研究開発や実証実験が盛んに行われている．自動運転を実現するにあたって，センサ情報から周囲の環境の情報を取得し，車体の位置を推定する必要がある．これを自己位置推定という．自己位置推定のセンサには3DLiDARがよく用いられる．3DLiDARは測定精度が高く，周囲の明るさの影響を受けないため，高精度に自己位置推定を行えるが，周囲に特徴物が少ないところでは自己位置推定の精度が低下するという課題がある．本研究では，自己位置推定の精度を予測する手法を提案する．自己位置推定精度の予測を行い，精度が悪い場所に対してGNSSやIMUなど3DLiDAR以外のセンサを用いたり，舗装マーキングを用いたりすることで，全体的な自己位置推定の精度を向上させることができる．オープンソース自動運転シミュレータを用いて自己位置推定精度予測のためのデータセットを作成した．実験では作成したデータセットに対して提案手法を行った．結果として，自己位置推定精度を高精度で予測できたことを報告する．
Zekun Wang, Jin Nakazato, Muhammad Asad, Ehsan Javanmardi, Manabu Tsukada, "Overcoming Environmental Challenges in CAVs Through MEC-Based Federated Learning", In: 14th International Conference on Ubiquitous and Future Networks (ICUFN2023), pp. 1-6, Paris, France, 2023.Proceedings Article | Abstract | BibTeX | Links:
@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}
}
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.
Yu Asabe, Ehsan Javanmardi, Jin Nakazato, Manabu Tsukada, Hiroshi Esaki, "AutowareV2X: Reliable V2X Communication and Collective Perception for Autonomous Driving", In: The 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring), Florence, Italy, 2023.Proceedings Article | Abstract | BibTeX | Links:
@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}
}
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.
Dou Hu, Jin Nakazato, Ehsan Javanmardi, Muhammad Asad, Manabu Tsukada
, "An Extended Kalman Filter Enabled Beam Tracking Framework in Intersection Management", European Conference on Networks and Communications (EuCNC) & 6G Summit Poster, 2023.Miscellaneous | Abstract | BibTeX | Links:
@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}
}
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.
Pengfei Lin, Ehsan Javanmardi, Ye Tao, Vishal Chauhan, Jin Nakazato, Manabu Tsukada, "Time-To-Collision-Aware Lane-Change Strategy Based on Potential Field and Cubic Polynomial for Autonomous Vehicles", In: 2023 IEEE Intelligent Vehicles Symposium (IEEE IV 2023), Anchorage, Alaska, USA, 2023.Proceedings Article | Abstract | BibTeX | Links:
@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}
}
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.
Muhammad Asad, Saima Shaukat, Ehsan Javanmardi, Jin Nakazato, Manabu Tsukada, "A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems", In: Applied Sciences , 2023, ISSN: 2076-3417.Journal Article | Abstract | BibTeX | Links:
@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}
}
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.
神原滉一, Ehsan Javanmardi, 中里仁, 山田俊也, 渡辺陽介, 高田広章, 佐藤健哉, 塚田学, "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補間", 電子情報通信学会 ITS研究会, 広島, 2023.Conference | Abstract | BibTeX | Links:
@conference{神原滉一2023,
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補間},
author = {神原滉一 and Ehsan Javanmardi and 中里仁 and 山田俊也 and 渡辺陽介 and 高田広章 and 佐藤健哉 and 塚田学},
url = {https://tlab.hongo.wide.ad.jp/papers/2023_IEICE_kambara.pdf
https://ken.ieice.org/ken/paper/20230222ZCRc/},
year = {2023},
date = {2023-02-21},
urldate = {2023-02-21},
booktitle = {電子情報通信学会 ITS研究会},
address = {広島},
abstract = {近年，協調型の自動運転が交通安全や交通流の効率化につながるとして注目されている．協調型自動運転とは，自動運転車が周囲の車両や道路に設置した路側機と通信を行い，自車の車載センサでは認識できなかった情報やタスクを共有するシステムのことである．協調型自動運転の重要な要件の一つは，すべての車両が適切なタイミングで適切なメッセージを受信することであり，そのためには，一定以上の通信性能の担保が必要である．事前に通信性能を把握しておくことによって，自動運転車は適切な認知方法の選択や，経路計画を行うことができる．そこで，本研究では，協調型自動運転のための地理的特性を考慮した通信性能分析，可視化システムを提案する．このシステムでは，車両が一度通過した場所の通信性能を分析，可視化し，クラウドに保存することによって，次に同じ場所を通過する車が，事前に通信性能を参照し，移動ルート決定ができるようになる．本提案システムを自動運転のためのオープンソースソフトウェアであるAutowareとダイナミックマッププラットフォームであるDM2.0PFを用いて実装し，東京大学柏キャンパステストコースにて，アウトドア実験，評価を行った結果を報告する．
},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
近年，協調型の自動運転が交通安全や交通流の効率化につながるとして注目されている．協調型自動運転とは，自動運転車が周囲の車両や道路に設置した路側機と通信を行い，自車の車載センサでは認識できなかった情報やタスクを共有するシステムのことである．協調型自動運転の重要な要件の一つは，すべての車両が適切なタイミングで適切なメッセージを受信することであり，そのためには，一定以上の通信性能の担保が必要である．事前に通信性能を把握しておくことによって，自動運転車は適切な認知方法の選択や，経路計画を行うことができる．そこで，本研究では，協調型自動運転のための地理的特性を考慮した通信性能分析，可視化システムを提案する．このシステムでは，車両が一度通過した場所の通信性能を分析，可視化し，クラウドに保存することによって，次に同じ場所を通過する車が，事前に通信性能を参照し，移動ルート決定ができるようになる．本提案システムを自動運転のためのオープンソースソフトウェアであるAutowareとダイナミックマッププラットフォームであるDM2.0PFを用いて実装し，東京大学柏キャンパステストコースにて，アウトドア実験，評価を行った結果を報告する．
浅部佑, エッサン ジャワーンマーディ, 中里仁, 塚田学, 江崎浩, "AutowareV2X：自動運転におけるV2X通信と協調認知の実現", 電子情報通信学会 ITS研究会, 広島, 2023.Conference | Abstract | BibTeX | Links:
@conference{浅部佑2023,
title = {AutowareV2X：自動運転におけるV2X通信と協調認知の実現},
author = {浅部佑 and エッサン ジャワーンマーディ and 中里仁 and 塚田学 and 江崎浩},
url = {https://tlab.hongo.wide.ad.jp/papers/2023_IEICE_asabe.pdf
https://ken.ieice.org/ken/paper/202302223CR2/},
year = {2023},
date = {2023-02-21},
urldate = {2023-02-21},
booktitle = {電子情報通信学会 ITS研究会},
address = {広島},
abstract = {近年，自律型自動運転の研究開発と社会実装が着々と進む中，その技術的な課題や限界点も指摘され始めている．そこで，最先端の無線通信技術やネットワーク技術を活かして様々な交通システムが協調的に認知，判断，実行を担える協調型自動運転の分野が注目されている．特に，多くのコネクテッドな交通参加者が自らのセンサーで認識した物標情報を共有することで，ネットワーク全体の環境の認識率の向上を図る「協調認知」の活用は大きく期待されている．本研究では，自動運転ソフトウェアに統合可能なV2X通信機能の要求事項を検討し，オープンソースの自動運転ソフトウェアである「Autoware」に外部接続性を提供できるV2Xモジュール「AutowareV2X」を提案した．本提案では，自律型自動運転の基本機能に加え，汎用性のある標準化されたV2Xメッセージでの通信を可能にすることにより，協調型自動運転アプリケーションを実装，実証実験できる基盤を実現した．さらに，協調認知のアプリケーションを実装し協調認知メッセージ（CPM）による物標情報の共有を可能とした．AutowareV2Xを活用することで路側機で認知した物標情報を自動運転車両に30ミリ秒以内で伝達することを実証実験により証明するができた．また，死角から歩行者や車両が飛び出てくるシナリオにおいては，道路脇に設置された路側機から物標情報をリアルタイムに共有されることにより，自動運転車両が減速・停止という危険回避動作を実現できた．本実証実験より，協調認知に限らず行動調停などの他のアプリケーションへのAutowareV2Xの活用も期待される．
},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
近年，自律型自動運転の研究開発と社会実装が着々と進む中，その技術的な課題や限界点も指摘され始めている．そこで，最先端の無線通信技術やネットワーク技術を活かして様々な交通システムが協調的に認知，判断，実行を担える協調型自動運転の分野が注目されている．特に，多くのコネクテッドな交通参加者が自らのセンサーで認識した物標情報を共有することで，ネットワーク全体の環境の認識率の向上を図る「協調認知」の活用は大きく期待されている．本研究では，自動運転ソフトウェアに統合可能なV2X通信機能の要求事項を検討し，オープンソースの自動運転ソフトウェアである「Autoware」に外部接続性を提供できるV2Xモジュール「AutowareV2X」を提案した．本提案では，自律型自動運転の基本機能に加え，汎用性のある標準化されたV2Xメッセージでの通信を可能にすることにより，協調型自動運転アプリケーションを実装，実証実験できる基盤を実現した．さらに，協調認知のアプリケーションを実装し協調認知メッセージ（CPM）による物標情報の共有を可能とした．AutowareV2Xを活用することで路側機で認知した物標情報を自動運転車両に30ミリ秒以内で伝達することを実証実験により証明するができた．また，死角から歩行者や車両が飛び出てくるシナリオにおいては，道路脇に設置された路側機から物標情報をリアルタイムに共有されることにより，自動運転車両が減速・停止という危険回避動作を実現できた．本実証実験より，協調認知に限らず行動調停などの他のアプリケーションへのAutowareV2Xの活用も期待される．
2022
Yu Asabe, Ehsan Javanmardi, Jin Nakazato, Manabu Tsukada, Hiroshi Esaki, "AutowareV2X: Enabling V2X Communication and Collective Perception for Autonomous Driving", Asian Internet Engineering Conference (AINTEC) 2022 Poster, 2022, (Best Poster Award).Miscellaneous | Abstract | BibTeX | Links:
@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}
}
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).
Koichi Kambara, Ehsan Javanmardi, Jin Nakazato, Yousuke Watanabe, Kenya Sato, Hiroaki Takada, Manabu Tsukada, "Towards Cooperative Automated Driving: Geographic-Aware Network Analysis and Visualization tool", Asian Internet Engineering Conference (AINTEC) 2022 Poster, 2022.Miscellaneous | Abstract | BibTeX | Links:
@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}
}
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.
