@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},

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},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Grosset2024,

title = {Generation of V2X messages from Carla Simulator for cooperative perception: Application to pedestrian safety},

author = {Juliette Grosset and Jean-Marie Bonnin and Alain-Jérôme Fougères and Manabu Tsukada and Moise Djoko-Kouam},

year  = {2024},

date = {2024-10-07},

booktitle = {The IEEE 100th Vehicular Technology Conference (VTC2024-Fall)},

address = {Washington DC, USA},

abstract = {Despite advancements in connected and autonomous vehicles (CAVs), vulnerable road users (VRUs) face a challenge as they lack Communication-Intelligent Transport System (C-ITS) equipment. This deficiency impedes their interaction with CAVs. We underscore the significance of Vehicle-to-Everything (V2X) communication in enhancing road safety with VRUs by facilitating information exchange between CAVs and the infrastructure. This communication is pivotal for reintegrating VRUs into the environmental awareness of CAVs. The Carla Simulator, used for autonomous vehicle training, currently lacks comprehensive V2X communication capabilities. In response, we propose an architecture for Carla, integrating OpenCDA and ROS2 to establish a simulated V2X network communication system for CAVs and roadside units (RSUs) within the Carla environment. This setup allows for the generation of V2X datasets and the refinement of algorithms for Advanced Driver Assistance Systems (ADAS). To illustrate and assess our proposed architecture, we present a scenario involving a pedestrian concealed in a blind spot for a connected vehicle.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Yun2024,

title = {Large Language Models for Human-like Autonomous Driving Decision Making: A Survey},

author = {Li Yun and Kai Katsumata and Ehsan Javanmardi and Manabu Tsukada},

year  = {2024},

date = {2024-09-24},

urldate = {2024-09-24},

booktitle = {27th IEEE International Conference on Intelligent Transportation Systems (ITSC 2024)},

address = {Edmonton, Canada},

abstract = {Large Language Models (LLMs), AI models trained on massive text corpora with remarkable language understanding and generation capabilities, are transforming the field of Autonomous Driving (AD). As AD systems evolve from rule-based and optimization-based methods to learning-based techniques like deep reinforcement learning, they are now poised to embrace a third and more advanced category: knowledge-based AD empowered by LLMs. This shift promises to bring AD closer to human-like AD. However, integrating LLMs into AD systems poses challenges in real-time inference, safety assurance, and deployment costs. This survey provides a comprehensive and critical review of recent progress in leveraging LLMs for AD, focusing on their applications in modular AD pipelines and end- to-end AD systems. We highlight key advancements, identify pressing challenges, and propose promising research directions to bridge the gap between LLMs and AD, thereby facilitating the development of more human-like AD systems. The survey first introduces LLMs’ key features and common training schemes, then delves into their applications in modular AD pipelines and end-to-end AD, respectively, followed by discussions on open challenges and future directions. Through this in-depth analysis, we aim to provide insights and inspiration for researchers and practitioners working at the intersection of AI and autonomous vehicles, ultimately contributing to safer, smarter, and more human-centric AD technologies.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Jiang2024b,

title = {Accurate Cooperative Localization Utilizing LiDAR-equipped Roadside Infrastructure for Autonomous Driving},

author = {Yuze Jiang and Ehsan Javanmardi and Manabu Tsukada and Hiroshi Esaki},

url = {https://arxiv.org/abs/2407.08384 },

year  = {2024},

date = {2024-09-24},

urldate = {2024-09-24},

booktitle = {27th IEEE International Conference on Intelligent Transportation Systems (ITSC 2024)},

address = {Edmonton, Canada},

abstract = {Recent advancements in LiDAR technology have significantly lowered costs and improved both its precision and resolution, thereby solidifying its role as a critical component in autonomous vehicle localization. Using sophisticated 3D reg- istration algorithms, LiDAR now facilitates vehicle localization with centimeter-level accuracy. However, these high-precision techniques often face reliability challenges in environments devoid of identifiable map features. To address this limitation, we propose a novel approach that utilizes road side units (RSU) with vehicle-to-infrastructure (V2I) communications to assist vehicle self-localization. By using RSUs as stationary reference points and processing real-time LiDAR data, our method enhances localization accuracy through a cooperative localization framework. By placing RSUs in critical areas, our proposed method can improve the reliability and precision of vehicle localization when the traditional vehicle self-localization technique falls short. Evaluation results in an end-to-end autonomous driving simulator AWSIM show that the proposed method can improve localization accuracy by up to 80% under vulnerable environments compared to traditional localization methods. Additionally, our method also demonstrates robust resistance to network delays and packet loss in heterogeneous network environments.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Gui2024,

title = {Shrinkable Arm-based eHMI on Autonomous Delivery Vehicle for Effective Communication with Other Road Users},

author = {Xinyue Gui and Mikiya Kusunoki and Bofei Huang and Stela Hanbyeol Seo and Chia-Ming Chang and Haoran Xie and Manabu Tsukada and Takeo Igarashi},

year  = {2024},

date = {2024-09-22},

booktitle = {16th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI 2024)},

address = {California, USA},

abstract = {When employing autonomous driving technology in logistics, small autonomous delivery vehicles (aka delivery robots) encounter challenges different from passenger vehicles when interacting with other road users. We conducted an online video survey as a pre-study and found that autonomous delivery vehicles need external human-machine interfaces (eHMIs) to ask for help due to their small size and functional limitations. Inspired by everyday human communication, we chose arms as eHMI to show their request through limb motion and gesture. We held an in-house workshop to identify the arm’s requirements for designing a specific arm with shrink-ability (conspicuous when delivering messages but not affect traffic at other times). We prototyped a small delivery robot with a shrinkable arm and filmed the experiment videos. We conducted two studies (a video-based and a 360-degree-photo VR-based) with 18 participants. We demonstrated that arm-on-delivery robots can increase interaction efficiency by drawing more attention and communicating specific information.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Chauhan2024,

title = {Transforming Pedestrian and Autonomous Vehicles Interactions in Shared Spaces: A Think-Tank Study on Exploring Human-Centric Designs},

author = {Vishal Chauhan and Anubhav Anubhav and Chia-Ming Chang and Jin Nakazato and Ehsan Javanmardi and Alex Orsholits and Takeo Igarashi and Kantaro Fujiwara and Manabu Tsukada



},

year  = {2024},

date = {2024-09-22},

booktitle = {16th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI 2024), Work in Progress (WiP)},

pages = {1-8},

address = {California, USA},

abstract = {Our research focuses on the smart pole interaction unit (SPIU) as an infrastructure external human-machine interface (HMI) to enhance pedestrian interaction with autonomous vehicles (AVs) in shared spaces. We extensively study SPIU with external human-machine interfaces (eHMI) on AVs as an integrated solution. To discuss interaction barriers and enhance pedestrian safety, we engaged 25 participants aged 18-40 to brainstorm design solutions for pedestrian-AV interactions, emphasising effectiveness, simplicity, visibility, and clarity. Findings indicate a preference for real-time SPIU interaction over eHMI on AVs in multiple AV scenarios. However, the combined use of SPIU and eHMI on AVs is crucial for building trust in decision-making. Consequently, we propose innovative design solutions for both SPIU and eHMI on AVs, discussing their pros and cons. This study lays the groundwork for future autonomous mobility solutions by developing human-centric eHMI and SPIU prototypes as ieHMI.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokey,

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},

year  = {2024},

date = {2024-09-14},

booktitle = {The 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)},

address = {Abu Dhabi ,UAE},

abstract = {The interactive decision-making in multi-agent

autonomous racing offers insights valuable beyond the domain of self-driving cars. Mapless online path planning is particularly of practical appeal but poses a challenge for safely overtaking opponents due to the limited planning horizon. Accordingly, this paper introduces RaceMOP, a novel method for mapless online path planning designed for multi-agent racing of F1TENTH cars. Unlike classical planners that depend on predefined racing lines, RaceMOP operates without a map, relying solely on local observations to overtake other race cars at high speed. Our approach combines an artificial potential field method as a base policy with residual policy learning to introduce long-horizon planning capabilities. We advance the field by introducing a novel approach for policy fusion with the residual policy directly in probability space. Our experiments for twelve simulated racetracks validate that RaceMOP is capable of long-horizon decision-making with robust collision avoidance during over- taking maneuvers. RaceMOP demonstrates superior handling over existing mapless planners while generalizing to unknown racetracks, paving the way for further use of our method in robotics. We make the open-source code for RaceMOP available at http://github.com/raphajaner/racemop.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Orsholits2024,

title = {PLATONE: An Immersive Geospatial Audio Spatialization Platform},

author = {Alex Orsholits and Yiyuan Qian and Eric Nardini and Yusuke Obuchi and Manabu Tsukada},

year  = {2024},

date = {2024-08-12},

booktitle = {The 2nd Annual IEEE International Conference on Metaverse Computing, Networking, and Applications (MetaCom 2024)},

address = {Hong Kong, China},

abstract = {In the rapidly evolving landscape of mixed reality (MR) and spatial computing, the convergence of physical and virtual spaces is becoming increasingly crucial for enabling immersive, large-scale user experiences and shaping inter-reality dynamics. This is particularly significant for immersive audio at city-scale, where the 3D geometry of the environment must be considered, as it drastically influences how sound is perceived by the listener. This paper introduces PLATONE, a novel proof-of-concept MR platform designed to augment urban contexts with environment-dependent spatialized audio. It leverages custom hardware for localization and orientation, alongside a cloud-based pipeline for generating real-time binaural audio. By utilizing open-source 3D building datasets, sound propagation effects such as occlusion, reverberation, and diffraction are accurately simulated. We believe that this work may serve as a compelling foundation for further research and development.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Takada2024,

title = {Design of Digital Twin Architecture for 3D Audio Visualization in AR},

author = {Tokio Takada and Jin Nakazato and Alex Orsholits and Manabu Tsukada and Hideya Ochiai and Hiroshi Esaki},

year  = {2024},

date = {2024-08-12},

urldate = {2024-08-12},

booktitle = {The 2nd Annual IEEE International Conference on Metaverse Computing, Networking, and Applications (MetaCom 2024)},

address = {Hong Kong, China},

abstract = {Digital twins have recently attracted attention from academia and industry as a technology connecting physical space and cyberspace. Digital twins are compatible with Augmented Reality (AR) and Virtual Reality (VR), enabling us to understand information in cyberspace. In this study, we focus on music and design an architecture for a 3D representation of music using a digital twin. Specifically, we organize the requirements for a digital twin for music and design the architecture. We establish a method to perform 3D representation in cyberspace and map the recorded audio data in physical space. In this paper, we implemented the physical space representation using a smartphone as an AR device and employed a visual positioning system (VPS) for self-positioning. For evaluation, in addition to system errors in the 3D representation of audio data, we conducted a questionnaire evaluation with several users as a user study. From these results, we evaluated the effectiveness of the implemented system. At the same time, we also found issues we need to improve in the implemented system in future works.},

key = {CREST},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Iwaki2024b,

title = {Enhancing V2X Communication: Machine Learning Assisted Dynamic mmWave Beam Search},

author = {Ryo Iwaki and Jin Nakazato and Kazuki Maruta and Manabu Tsukada and Hideya Ochiai and Hiroshi Esaki},

doi = {10.1109/ICUFN61752.2024.10625435},

year  = {2024},

date = {2024-07-02},

urldate = {2024-07-02},

booktitle = {The 15th International Conference on Ubiquitous and Future Networks (ICUFN2024)},

address = {Budapest, Hungary},

abstract = {Only the chairs can edit This paper addresses the challenges of dynamic beam search in millimeter wave (mmWave) communications for vehicle-to-everything (V2X) applications. With the rapid mobility of connected autonomous vehicles (CAVs) and dense urban environments, maintaining high-quality mmWave connections is critical for the reliability and efficiency of V2X communications. We propose a novel machine learning-assisted framework for dynamic mmWave beam search, which significantly enhances the adaptability and performance of V2X communication systems. Our approach leverages real-time environmental data and CAV dynamics to predict optimal beam directions, improving connection stability. Simulation results demonstrate the effectiveness of the proposed method in a real-world road scenario, offering a partial improvement over conventional beam search techniques.},

note = {Best Paper Award},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Yoshimura2024,

title = {Towards Robust Communication in ITS: A Comprehensive Study of Blockchain for V2I},

author = {Atsuki Yoshimura and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},

doi = {10.1109/ICUFN61752.2024.10624948},

year  = {2024},

date = {2024-07-02},

urldate = {2024-07-02},

booktitle = {The 15th International Conference on Ubiquitous and Future Networks (ICUFN2024)},

address = {Budapest, Hungary},

abstract = {V2X in connected autonomous vehicles (CAV) plays an important role in information sharing through communication. The integration of V2X and blockchain has the potential to create functionalities such as seamless V2X information sharing, similar to Bitcoin, and post-accident investigation utilities that leverage data immutability. However, the integration of blockchain into V2X communication requires addressing CAV mobility. In this study, we propose a framework that takes into account the high mobility of CAVs. Furthermore, we propose a method that not only addresses this challenge but also achieves load balancing by facilitating cooperation among nodes responsible for member management. In this paper, we integrate the ITS simulator, the communication simulator, and the blockchain simulator to build an infrastructure that can be evaluated end-to-end. Using the integrated simulator, we perform an evaluation based on metrics such as latency and member change rate in a mobile environment with a single roadside unit (RSU). In the future, we plan to implement the proposed methodology and perform evaluations in environments with multiple RSUs.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Chi2024,

title = {V2I Blockage Modeling and Performance Evaluation for Connected Autonomous Vehicle},

author = {Weiqi Chi and Jin Nakazato and Tomoki Murakami and Manabu Tsukada},

year  = {2024},

date = {2024-06-24},

urldate = {2024-06-24},

booktitle = {The IEEE 99th Vehicular Technology Conference (VTC2024-Spring)},

address = {Singapore},

abstract = {The burgeoning Intelligent Transportation System (ITS) spurs global technological advancements, notably in innovative community development through vehicle-to-everything (V2X) communication. This study focuses on the high data rates and low latency offered by a millimeter-wave (mmWave) enabled vehicular network while addressing the significant challenge of link quality degradation due to blockages, exacerbated by the mmWave band’s small wavelength in high mobility and traffic conditions. We propose an RSU-assisted ITS system tailored for multi-lane, straight-road scenarios, effectively identifying blockage status for vehicles. Combining Simulation of Urban Mobility (SUMO) and MATLAB, this blockage-aware scheme lays the groundwork for future ITS enhancements. The research also delves into the effects of various frequency bands, vehicle types, and communication ranges, offering a holistic system performance analysis.},

note = {IEEE VTS Tokyo/Japan Chapter Young Researcher's Encouragement Award},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Ito2024,

title = {Enhancing Real-Time Streaming Quality through a Multipath Redundant Communication Framework},

author = {Koki Ito and Jin Nakazato and Romain Fontugne and Manabu Tsukada and Hiroshi Esaki},

year  = {2024},

date = {2024-06-03},

urldate = {2024-06-03},

booktitle = {IFIP/IEEE Networking 2024},

address = {Thessaloniki, Greece},

abstract = {Recently, as networks operate as the infrastructure of modern society, the demands placed on the network by applications have become more complex. In particular, an increasing annual demand for high-capacity and low-latency services, including real-time streaming. 5G services have been launched to meet this demand, but their stability varies de- pending on location and time and can only sometimes be considered sufficient. One method to improve communication stability is multipath redundant communication, and much research has been conducted in this area. However, most of this research has focused on TCP-based communication and cannot be applied to real-time UDP streaming. Hence, we propose a multipath redundant communication framework to improve the quality of real-time media streaming communication. Tunneling at the IP layer in our proposed framework was performed to overcome the limitations of transport layer protocols, which was a challenge for traditional multipath redundant communication systems. Furthermore, to address the packet order inconsistency caused by multipath redundant communication, a buffering mechanism was implemented on the receiving side of our system. Our proposed system was verified using multipath redundant communication and multiple mobile networks from a vehicle moving in an urban area. The experiments used a real-time streaming application based on WebRTC, and the framework significantly reduced packet loss and improved bitrate compared to existing multipath redundant communication systems without interfering with the application’s congestion control.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Tao2024b,

title = {Zero-Knowledge Proof of Distinct Identity: a Standard-compatible Sybil-resistant Pseudonym Extension for C-ITS},

author = {Ye Tao and Hongyi Wu and Ehsan Javanmardi and Manabu Tsukada and Hiroshi Esaki},

url = {https://arxiv.org/abs/2403.14020},

year  = {2024},

date = {2024-06-02},

booktitle = {35th IEEE Intelligent Vehicles Symposium (IV2024)},

address = {Jeju Island, Korea},

abstract = {Pseudonyms are widely used in Cooperative Intelligent Transport Systems (C-ITS) to protect the location privacy of vehicles. However, the unlinkability nature of pseudonyms also enables Sybil attacks, where a malicious vehicle can pretend to be multiple vehicles at the same time. In this paper, we propose a novel protocol called zero-knowledge Proof of Distinct Identity (zk-PoDI,) which allows a vehicle to prove that it is not the owner of another pseudonym in the local area, without revealing its actual identity. Zk-PoDI is based on the Diophantine equation and zk-SNARK, and does not rely on any specific pseudonym design or infrastructure assistance. We show that zk-PoDI satisfies all the requirements for a practical Sybil-resistance pseudonym system, and it has low latency, adjustable difficulty, moderate computation overhead, and negligible communication cost. We also discuss the future work of implementing and evaluating zk-PoDI in a realistic city-scale simulation environment.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Maruta2024,

title = {Millimeter-Wave Fast Beam Tracking Enabled by RAN/V2X Cooperation},

author = {Kazuki Maruta and Jin Nakazato and Kengo Suzuki and Dou Hu and Ryo Iwaki and Sojin Ozawa and Yuki Sasaki and Hideya So and Manabu Tsukada},

url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/376818826_Millimeter-Wave_Fast_Beam_Tracking_Enabled_by_RANV2X_Cooperation/links/658ac62e6f6e450f19a60664/Millimeter-Wave-Fast-Beam-Tracking-Enabled-by-RAN-V2X-Cooperation.pdf},

year  = {2024},

date = {2024-02-19},

urldate = {2024-02-19},

booktitle = {International Conference on Artificial Intelligence in Information and Communication (ICAIIC 2024)},

address = {Osaka, Japan},

abstract = {Only the chairs can edit Automated driving has the same limitations as human drivers because it functions as a replacement for humans and operates based on local information using onboard sensors and computers. Cooperative automated driving is expected to achieve both safety and efficiency, which could not be achieved by imitating human driving, by sharing sensor information from roadside equipment and other vehicles. Since such sensor information is enormous, it is desirable to utilize millimeter-waves, which are capable of high-capacity transmission. However, wireless communication systems for cooperative automated driving have the challenge of radio quality degradation due to vehicle movement. Our research project aims to realize stable millimeter-wave transmission by incorporating Open RAN (O-RAN) and vehicle-to-everything (V2X) functions. This paper presents the overall proposed concept and an example of validation; we show the results of evaluating our previously proposed fast beam following scheme in a handover environment with multiple roadside units.},

note = {Excellent Paper Award},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Karimata2024,

title = {Multi-UAVs Strategies for Ad Hoc Network with Multi-Agent Reinforcement Learning},

author = {George Karimata, Jin Nakazato, Gia Khanh Tran, Katsuya Suto, Manabu Tsukada and Hiroshi Esaki, },

url = {https://icoin.org/media?key=site/icoin2024/abs/P-4-1.pdf},

year  = {2024},

date = {2024-01-17},

urldate = {2024-01-17},

booktitle = {The 38th International Conference on Information Networking (ICOIN2024)},

address = {Ho Chi Minh City, Vietnam},

abstract = {In recent years, extensive research has focused on leveraging advanced technologies beyond 5G and for Industry 5.0 to promote sustainability and prosperity in society. Our study advances this effort by seeking to create an aerial perspective using Unmanned Aerial Vehicles (UAVs). This paper introduces a method for optimizing UAV deployment strategies using multi-agent reinforcement learning, facilitating the formation of a flying ad hoc network. The results demonstrate practical cooperation among UAVs in flight.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Jiang2023,

title = {Roadside LiDAR Assisted Cooperative Localization for Connected Autonomous Vehicles},

author = {Yuze Jiang and Ehsan Javanmard and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},

url = {https://arxiv.org/abs/2311.07913},

year  = {2023},

date = {2023-12-14},

urldate = {2023-12-14},

booktitle = {ACM Intelligent Computing and its Emerging Applications (ICEA 2023)},

abstract = {Advancements in LiDAR technology have led to more cost-effective production while simultaneously improving precision and resolution. As a result, LiDAR has become integral to vehicle localization, achieving centimeter-level accuracy through techniques like Normal Distributions Transform (NDT) and other advanced 3D registration algorithms. Nonetheless, these approaches are reliant on high-definition 3D point cloud maps, the creation of which involves significant expenditure. When such maps are unavailable or lack sufficient features for 3D registration algorithms, localization accuracy diminishes, posing a risk to road safety. To address this, we proposed to use LiDAR-equipped roadside unit and Vehicle-to-Infrastructure (V2I) communication to accurately estimate the connected vehicle's position and help the vehicle when its self-localization is not accurate enough. Our simulation results indicate that this method outperforms traditional NDT scan matching-based approaches in terms of localization accuracy.},

note = {Best paper award (Silver)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Matsumoto2023b,

title = {Localizability Estimation for Autonomous Driving: A Deep Learning-Based Place Recognition Approach},

author = {Kazuto Matsumoto and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada},

year  = {2023},

date = {2023-12-11},

booktitle = {IEEE Robotic Computing 2023},

address = {California, USA},

abstract = {In recent years, research and development aimed at the societal implementation of autonomous driving have attracted increasing attention. Localization, which involves obtaining in- formation about the surrounding environment from sensor data and estimating the vehicle’s position, is necessary for realizing autonomous driving. Localization is commonly performed with 3D LiDAR as a sensor owing to its high measurement accuracy and immunity to ambient light conditions, which allow for precise localization. However, localization accuracy may decrease when the surrounding area does not have distinctive features. In this study, we proposed a method based on deep learning to estimate localization accuracy for autonomous driving. The overall localization accuracy can be improved by estimating the accuracy of localization using other sensors, such as GNSS and IMU, or pavement markings in areas with poor accuracy. We created a dataset for estimating localization accuracy using an open-source autonomous driving simulator. In an experiment, we applied the proposed method to the created dataset. Estimations with low MSE were obtained. The results indicate that the proposed method can accurately estimate localization accuracy.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Furuta2023,

title = {Web-Based BIM Platform for Building Digital Twin},

author = {Satoru Furuta and Jin Nakazato and Manabu Tsukada},

doi = {10.1109/DTPI59677.2023.10365476},

year  = {2023},

date = {2023-11-07},

urldate = {2023-11-07},

booktitle = {3rd Annual IEEE International Conference on Digital Twins and Parallel Intelligence (DTPI 2023)},

address = {Florida, USA},

abstract = {Building digital twin (BDT) is utilized throughout the lifecycle of a building. It serves for efficient operations during the design and construction phases, and during the operational and maintenance phases, it’s used for asset management and as field maps for robots. Building Information Modeling (BIM), which contains both semantics and geometry data of building elements, holds promise as a data source for BDT. We have extracted four key technical challenges of the digital twin, particularly vital during the operational and maintenance phases: software, visualization, update, and real-scene reconstruction. Due to the exclusive and static nature of BIM, these challenges also pose significant issues in the context of BDT. To address these challenges, we designed and implemented a Web-based BIM platform. The implemented application shows not only geometry data but also semantics data, enables easy overlay with the latest indoor conditions, and provides updating functionality. The developed system is essential for the continuous operation of BDT in dynamic indoor environments. We evaluated the application through a questionnaire survey. },

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Lin2023c,

title = {Potential Field-based Path Planning with Interactive Speed Optimization for Autonomous Vehicles},

author = {Pengfei Lin and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada},

url = {https://arxiv.org/abs/2306.06987},

year  = {2023},

date = {2023-10-16},

urldate = {2023-10-16},

booktitle = {49th Annual Conference of the IEEE Industrial Electronics Society (IECON 2023)},

abstract = {Path planning is critical for autonomous vehicles (AVs) to determine the optimal route while considering constraints and objectives. The potential field (PF) approach has become prevalent in path planning due to its simple structure and computational efficiency. However, current PF methods used in AVs focus solely on the path generation of the ego vehicle while assuming that the surrounding obstacle vehicles drive at a preset behavior without the PF-based path planner, which ignores the fact that the ego vehicle’s PF could also impact the path generation of the obstacle vehicles. To tackle this problem, we propose a PF-based path planning approach where local paths are shared among ego and obstacle vehicles via vehicle-to- vehicle (V2V) communication. Then by integrating this shared local path into an objective function, a new optimization function called interactive speed optimization (ISO) is designed to allow driving safety and comfort for both ego and obstacle vehicles. The proposed method is evaluated using MATLAB/Simulink in the urgent merging scenarios by comparing it with conventional methods. The simulation results indicate that the proposed method can mitigate the impact of other AVs’ PFs by slowing down in advance, effectively reducing the oscillations for both ego and obstacle AVs.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sugizaki2023,

title = {Wireless Ad-Hoc Federated Learning for Cooperative Map Creation and Localization Models},

author = {Yusuke Sugizaki and Hideya Ochiai and Muhammad Asad and Manabu Tsukada and Hiroshi Esaki},

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},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Chauhan2023,

title = {Keep Calm and Cross: Smart Pole Interaction Unit for Easing Pedestrian Cognitive Load},

author = {Vishal Chauhan and Chia-Ming Chang and Ehsan Javanmardi and Jin Nakazato and Koki Toda and Pengfei Lin and Takeo Igarashi and Manabu Tsukada},

url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/374582122_Keep_Calm_and_Cross_Smart_Pole_Interaction_Unit_for_Easing_Pedestrian_Cognitive_Load/links/6525681eb32c91681fb2e1b5/Keep-Calm-and-Cross-Smart-Pole-Interaction-Unit-for-Easing-Pedestrian-Cognitive-Load.pdf},

year  = {2023},

date = {2023-10-12},

urldate = {2023-10-12},

booktitle = {The 9th IEEE World Forum on Internet of Things (IEEE WFIoT2023)},

address = {Aveiro, Portugal},

abstract = {Recently, there has been a growing emphasis on autonomous vehicles (AVs), and as they coexist with pedestrians, ensuring pedestrian safety at crosswalks has become paramount. While AVs exhibit commendable performance on traditional roads with established traffic infrastructure, their interaction in different environments, such as shared spaces lacking traffic lights or sign rules (also known as naked streets), can present significant challenges, including right-of-way and accessibility concerns. To address these challenges, this study proposes a novel approach to enhance pedestrian safety in shared spaces, focusing on the proposed smart pole interaction unit (SPIU) combined with an external human-machine interface (eHMI). By evaluating the proposal of SPIU developed by a virtual reality system, we explore its usability and effectiveness in facilitating vehicle-to-pedestrian (V2P) interactions at crosswalks. Our findings from this study showed that SPIU facilitates safe, quicker decision-making to stop and pass at crosswalks in shared space and reduces cognitive load compared to scenarios where an SPIU is absent for pedestrians and reduce the need for eHMI to see on multiple AVs. The SPIU addition with the eHMI in vehicles yields a noteworthy 21 % improvement in response time, enhancing efficiency during pedestrian stops. In both scenarios, whether with a single AV (1-way) or multiple AVs (2-way), SPIU has a positive impact on interaction dynamics and statistically demonstrates a significant improvement (p = 0.001). },

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Bao2023,

title = {Personalized Causal Factor Generalization for Subjective Risky Scene Understanding with Vision Transformer},

author = {Naren Bao and Alexander Carballo and Manabu Tsukada and Kazuya Takeda},

doi = {10.1109/ITSC57777.2023.10422148},

year  = {2023},

date = {2023-09-24},

urldate = {2023-09-24},

booktitle = {The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)},

address = {Bilbao, Bizkaia, Spain},

abstract = {This paper presents a framework to understanding subjective driving scene perception by Vision Transformer for Environmental Feature Extraction within a Causal Modeling Analysis method. By leveraging vision transformer models, informative features are extracted from video camera images capturing the surrounding environment. Through the causal analysis, the causal effects of these variables on subjective risk perception are explored, shedding light on the factors influencing individuals' perception of driving risk. The findings demonstrate understanding of  environmental features and individual difference on risk perception, providing a deeper understanding of risky scene perception. The paper concludes with this approach unifies selective attentional phenomena can improve the scene understanding for subjective perception in real-world driving scenarios aiming to enhance driving safety based on the identified causal factors.  },

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Yamazaki2023,

title = {ToST: Tokyo SUMO traffic scenario },

author = {Yuji Yamazaki and Yasumasa Tamura and Xavier Defago and Ehsan Javanmardi and Manabu Tsukada},

url = {https://github.com/dfg-lab/ToSTScenario},

doi = {10.1109/ITSC57777.2023.10422517},

year  = {2023},

date = {2023-09-24},

urldate = {2023-09-24},

booktitle = {The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)},

address = {Bilbao, Bizkaia, Spain},

abstract = {In recent years, research, development, and demonstrations aimed at the societal implementation of autonomous driving have attracted increasing attention. Localization, which involves obtaining information of the surrounding environment from sensor data and estimating the vehicle's position, is necessary for realizing autonomous driving. Localization is commonly performed with 3D LiDAR as a sensor owing to its high measurement accuracy and immunity to ambient light conditions, which allow for precise localization. However, when the surrounding area has distinctive features, localization accuracy may decrease. In this study, we proposed a method based on deep learning to predict the localization accuracy for autonomous driving. The overall localization accuracy can be improved by predicting the accuracy of localization using other sensors, such as GNSS and IMU, or pavement markings in areas with poor accuracy. We created a dataset for predicting the localization accuracy using an open-source autonomous driving simulator. In an experiment, we applied the proposed method to the created dataset. Thresholds were set for errors in the x-direction, y-direction, and distance for localization. Predictions with high accuracy and F-values were obtained. The results indicate that the proposed method can accurately predict the localization accuracy.  },

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Lin2023b,

title = {Occlusion-Aware Path Planning for Collision Avoidance: Leveraging Potential Field Method with Responsibility-Sensitive Safety},

author = {Pengfei Lin and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada},

url = {https://arxiv.org/abs/2306.06981},

doi = {10.1109/ITSC57777.2023.10422621},

year  = {2023},

date = {2023-09-24},

urldate = {2023-09-24},

booktitle = {The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)},

series = {Bilbao, Bizkaia, Spain},

abstract = {Collision avoidance (CA) has always been the foremost task for autonomous vehicles (AVs) under safety criteria. And path planning is directly responsible for generating a safe path to accomplish CA while satisfying other commands. Due to the real-time computation and simple structure, the potential field (PF) has emerged as one of the mainstream path-planning algorithms. However, the current PF is primarily simulated in ideal CA scenarios, assuming complete obstacle information while disregarding occlusion issues where obstacles can be partially or entirely hidden from the AV's sensors. During the occlusion period, the occluded obstacles do not possess a PF. Once the occlusion is over, these obstacles can generate an instantaneous virtual force that impacts the ego vehicle. Therefore, we propose an occlusion-aware path planning (OAPP) with the responsibility-sensitive safety (RSS)-based PF to tackle the occlusion problem for non-connected AVs. We first categorize the detected and occluded obstacles, and then we proceed to the RSS violation check. Finally, we can generate different virtual forces from the PF for occluded and non-occluded obstacles. We compare the proposed OAPP method with other PF-based path planning methods via MATLAB/Simulink. The simulation results indicate that the proposed method can eliminate instantaneous lateral oscillation or sway and produce a smoother path than conventional PF methods.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Tao2023c,

title = {Flowsim: A Modular Simulation Platform for Microscopic Behavior Analysis of City-Scale Connected Autonomous Vehicles},

author = {Ye Tao and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},

url = {https://arxiv.org/abs/2306.05738},

doi = {10.1109/ITSC57777.2023.10421900},

year  = {2023},

date = {2023-09-24},

urldate = {2023-09-24},

booktitle = {The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)},

address = {Bilbao, Bizkaia, Spain},

abstract = {As connected autonomous vehicles (CAVs) become increasingly prevalent, there is a growing need for simulation platforms that can accurately evaluate CAV behavior in large-scale environments. In this paper, we propose Flowsim, a novel simulator specifically designed to meet these requirements. Flowsim offers a modular and extensible architecture that enables the analysis of CAV behaviors in large-scale scenarios. It provides researchers with a customizable platform for studying CAV interactions, evaluating communication and networking protocols, assessing cybersecurity vulnerabilities, optimizing traffic management strategies, and developing and evaluating policies for CAV deployment. Flowsim is implemented in pure Python in approximately 1,500 lines of code, making it highly readable, understandable, and easily modifiable. We verified the functionality and performance of Flowsim via a series of experiments based on realistic traffic scenarios. The results show the effectiveness of Flowsim in providing a flexible and powerful simulation environment for evaluating CAV behavior and data flow. Flowsim is a valuable tool for researchers, policymakers, and industry professionals who are involved in the development, evaluation, and deployment of CAVs. The code of Flowsim is publicly available on GitHub under the MIT license. },

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Yosodipuro2023,

title = {Mixed-traffic Intersection Management using Traffic-load-responsive Reservation and V2X-enabled Speed Coordination},

author = {Nicholaus Danispadmanaba Yosodipuro and Ehsan Javanmardi and Jin Nakazato and Yasumasa Tamura and Xavier Defago and Manabu Tsukada},

url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/374470825_Mixed-traffic_Intersection_Management_using_Traffic-load-responsive_Reservation_and_V2X-enabled_Speed_Coordination/links/651ee8d63ab6cb4ec6bde79a/Mixed-traffic-Intersection-Management-using-Traffic-load-responsive-Reservation-and-V2X-enabled-Speed-Coordination.pdf},

doi = {10.1109/ITSC57777.2023.10422248},

year  = {2023},

date = {2023-09-24},

urldate = {2023-09-24},

booktitle = {The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)},

address = {Bilbao, Bizkaia, Spain},

abstract = {Vehicle-to-everything (V2X) communication enables connected autonomous vehicles (CAVs) to share information and generate optimal decisions. The networking abilities of CAVs have led to the development of unsignalized autonomous intersection management (AIM) methods that leverage CAVs to significantly improve traffic flows. However, AIM methods assume 100% CAV market penetration, which is currently unrealistic owing to the gradual adoption of CAVs. Therefore, CAVs must share road usage with nonconnected vehicles (NCVs). Thus, we propose a mixed-traffic intersection management method that considers NCVs while ensuring high traffic flow, called traffic-load-responsive reservation for intersection management (TLRRIM). In TLRRIM, the roadside unit (RSU) first classifies vehicles and groups them into clusters before selecting a reservation cluster to cross an intersection. The reservation cluster selection considers both traffic load and crossing urgency. In addition, the RSU utilizes V2X-enabled speed coordination (VESC) for CAVs within the reservation cluster to further improve traffic flow, while utilizing traffic lights to guide NCVs. Simulation-based experiments using OpenCDA and CARLA showed that TLRRIM can increase throughput and reduce waiting time by up to 89.63% and 60.71%, respectively, compared with the fixed-time signaling method. Moreover, adding VESC can increase throughput by 12.21% and reduce waiting time by 10.80%, further enhancing traffic flow.  },

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Wang2023,

title = {Overcoming Environmental Challenges in CAVs Through MEC-Based Federated Learning},

author = {Zekun Wang and Jin Nakazato and Muhammad Asad and Ehsan Javanmardi and Manabu Tsukada},

url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/371685830_Overcoming_Environmental_Challenges_in_CAVs_through_MEC-based_Federated_Learning/links/64a420ea8de7ed28ba7465c7/Overcoming-Environmental-Challenges-in-CAVs-through-MEC-based-Federated-Learning.pdf},

doi = {10.1109/ICUFN57995.2023.10200688},

year  = {2023},

date = {2023-07-04},

urldate = {2023-07-04},

booktitle = {14th International Conference on Ubiquitous and Future Networks (ICUFN2023)},

pages = {1-6},

address = {Paris, France},

abstract = {Connected autonomous vehicles (CAVs), through vehicle-to-everything communication and computing resources, enable the vital exchange of information. Although deep learning is crucial in this landscape, it requires extensive and intricate datasets covering all potential scenarios. Furthermore, this situation poses a hazard, as the likelihood of accidents associated with imbalanced datasets increases, particularly in scenarios where processing analysis is compromised due to fluctuating weather conditions. We propose a Federated Learning (FL) framework undergirded by Multi-Access Edge Computing (MEC) to counter these challenges. This local device-focused framework enhances task-specific models' caching and continual updating across various conditions. In a more specific sense, edge nodes (ENs) operate as MEC, each caching multiple dedicated models and serving as the aggregator as part of the FL process. Additionally, we have engineered two innovative algorithms that categorize various states into multiple classes, thereby ensuring the efficient utilization of computing resources in ENs. Simulation results substantiate the effectiveness of our approach, showing that the proposed dedicated model consistently outperforms a general model designed for all situations.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Inokuchi2023b,

title = {Semantic Digital Twin for interoperability and comprehensive management of data assets},

author = {Kazuma Inokuchi and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},

url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/371903091_Semantic_digital_twin_for_interoperability_and_Comprehensive_Management_of_Data_Assets/links/649b13318de7ed28ba5ca665/Semantic-digital-twin-for-interoperability-and-Comprehensive-Management-of-Data-Assets.pdf},

doi = {10.1109/MetaCom57706.2023.00049},

year  = {2023},

date = {2023-06-26},

urldate = {2023-06-26},

booktitle = {IEEE International Conference on Metaverse Computing, Networking and Applications (IEEE MetaCom 2023)},

address = {Kyoto, Japan},

abstract = {Fusion of the real and virtual worlds is essential for applying digital technology to the infrastructure of human life. A digital twin is one of the technologies that aim to integrate real and virtual space. It creates a digital world with high fidelity to reality by accumulating exhaustive information from sensors to improve simulation and prediction accuracy. However, traditional digital twins have data asset management challenges owing to the physical, temporal, and structural heterogeneity of their objects. In this paper, we propose two metadata schemas that leverage semantics to construct a designer-oriented digital twin. Moreover, we implemented a viewer that reproduced the office-like demonstration field to verify the application of the proposed ontology. The proposed method enables a generic description of the dynamic behaviors of any entity by integrating physical twins faithful to the real world with virtual models expected by designers. We compared the proposed ontologies with existing techniques, conducted user evaluations, and discussed possible approaches for further enhancements for widespread use.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Asabe2023b,

title = {AutowareV2X: Reliable V2X Communication and Collective Perception for Autonomous Driving},

author = {Yu Asabe and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},

url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/371903099_AutowareV2X_Reliable_V2X_Communication_and_Collective_Perception_for_Autonomous_Driving/links/649b1457c41fb852dd36b04d/AutowareV2X-Reliable-V2X-Communication-and-Collective-Perception-for-Autonomous-Driving.pdf

https://github.com/tlab-wide/AutowareV2X

https://www.youtube.com/watch?v=57fx3-gUNxU},

doi = {10.1109/VTC2023-Spring57618.2023.10199425},

year  = {2023},

date = {2023-06-20},

urldate = {2023-06-20},

booktitle = {The 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)},

address = {Florence, Italy},

abstract = {For cooperative intelligent transport systems (C-ITS), vehicle-to-everything (V2X) communication is utilized to allow autonomous vehicles to share critical information with each other. We propose AutowareV2X, an implementation of a V2X communication module that is integrated into the autonomous driving (AD) software, Autoware. AutowareV2X provides external connectivity to the entire AD stack, enabling the end-to-end (E2E) experimentation and evaluation of connected autonomous vehicles (CAV). The Collective Perception Service was also implemented, allowing the transmission of Collective Perception Messages (CPMs). A dual-channel mechanism that enables wireless link redundancy on the critical object information shared by CPMs is also proposed. Performance evaluation in field experiments has indicated that the E2E latency of perception information is around 30 ms, and shared object data can be used by the AD software to conduct collision avoidance maneuvers. Dual-channel delivery of CPMs enabled the CAV to dynamically select the best CPM from CPMs received from different links, depending on the freshness of their information.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Lin2023,

title = {Time-To-Collision-Aware Lane-Change Strategy Based on Potential Field and Cubic Polynomial for Autonomous Vehicles},

author = {Pengfei Lin and Ehsan Javanmardi and Ye Tao and Vishal Chauhan and Jin Nakazato and Manabu Tsukada},

url = {https://arxiv.org/abs/2306.06981},

year  = {2023},

date = {2023-06-04},

urldate = {2023-06-04},

booktitle = {2023 IEEE Intelligent Vehicles Symposium (IEEE IV 2023)},

address = {Anchorage, Alaska, USA},

abstract = {Making safe and successful lane changes (LCs) is one of the many vitally important functions of autonomous vehicles (AVs) that are needed to ensure safe driving on expressways. Recently, the simplicity and real-time performance of the potential field (PF) method have been leveraged to design decision and planning modules for AVs. However, the LC trajectory planned by the PF method is usually lengthy and takes the ego vehicle laterally parallel and close to the obstacle vehicle, which creates a dangerous situation if the obstacle vehicle suddenly steers. To mitigate this risk, we propose a time-to-collision-aware LC (TTCA-LC) strategy based on the PF and cubic polynomial in which the TTC constraint is imposed in the optimized curve fitting. The proposed approach is evaluated using MATLAB/Simulink under high-speed conditions in a comparative driving scenario. The simulation results indicate that the TTCA-LC method performs better than the conventional PF-based LC (CPF-LC) method in generating shorter, safer, and smoother trajectories. The length of the LC trajectory is shortened by over 27.1%, and the curvature is reduced by approximately 56.1% compared with the CPF-LC method.

},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Tao2023,

title = {zk-PoT: Zero-Knowledge Proof of Traffic for Privacy Enabled Cooperative Perception},

author = {Ye Tao and Yuze Jiang and Pengfei Lin and Manabu Tsukada and Hiroshi Esaki},

url = {http://arxiv.org/abs/2211.07875},

doi = {10.1109/CCNC51644.2023.10059601},

year  = {2023},

date = {2023-01-08},

urldate = {2023-01-08},

booktitle = {2023 IEEE 20th Annual Consumer Communications & Networking Conference (CCNC)},

address = {Las Vegas, NV, USA},

abstract = {Cooperative perception is an essential and widely discussed application of connected automated vehicles. However, the authenticity of perception data is not ensured, because the vehicles cannot independently verify the event they did not see. Many methods, including trust-based (i.e., statistical) approaches and plausibility-based methods, have been proposed to determine data authenticity. However, these methods cannot verify data without a priori knowledge. In this study, a novel approach of constructing a self-proving data from the number plate of target vehicles was proposed. By regarding the pseudonym and number plate as a shared secret and letting multiple vehicles prove they know it independently, the data authenticity problem can be transformed to a cryptography problem that can be solved without trust or plausibility evaluations. Our work can be adapted to the existing works including ETSI/ISO ITS standards while maintaining backward compatibility. Analyses of common attacks and attacks specific to the proposed method reveal that most attacks can be prevented, whereas preventing some other attacks, such as collusion attacks, can be mitigated. Experiments based on realistic data set show that the rate of successful verification can achieve 70% to 80% at rush hours.

},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Lin2022b,

title = {Cooperative Path Planning Using Responsibility-Sensitive Safety (RSS)-based Potential Field with Sigmoid Curve},

author = {Pengfei Lin and Manabu Tsukada},

url = {https://youtu.be/AhgptWUyzSc},

doi = {10.1109/VTC2022-Spring54318.2022.9860508},

year  = {2022},

date = {2022-06-19},

urldate = {2022-06-19},

booktitle = {The 2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring)},

address = {Helsinki, Finland},

abstract = {Potential field (PF)-based path planning is reported to be highly efficient for autonomous vehicles because it performs risk-aware computation and has a simple structure. However, the inherent limitations of the PF make it vulnerable in some specific traffic scenarios, such as local minima and oscillations in close obstacles. Therefore, a hybrid path planning with the sigmoid curve has recently been presented to generate better trajectories than those generated by the PF for collision avoidance. However, it is time-consuming and less applicable in complex dynamic environments, especially in traffic emergencies. To address these limitations, we propose a cooperative hybrid path planning (CHPP) approach that involves collaboration with adjacent vehicles for emergency collision avoidance via V2V communication. Moreover, the responsibility-sensitive safety (RSS) model is introduced to enhance the PF and sigmoid curve for safe-critical and time-saving requirements. The effectiveness of the proposed CHPP method compared with the state-of-the-art methods is studied through simulation of both static and dynamic traffic emergency scenarios. The simulation results prove that the CHPP approach performs better in terms of computation time (0.02 s faster) and driving safety (avoiding collision) than other methods, which are more supportive for emergency cooperative driving.

},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Lin2022c,

title = {Adaptive Potential Field with Collision Avoidance for Connected Autonomous Vehicles},

author = {Pengfei Lin and Manabu Tsukada},

doi = {10.23919/ASCC56756.2022.9828160},

year  = {2022},

date = {2022-05-03},

urldate = {2022-05-03},

booktitle = {13th Asian Control Conference (ASCC) 2022},

address = {Jeju, Korea},

abstract = {Potential field (PF), as a risk assessment method, is proposed to enhance autonomous vehicles’ (AVs) safety in collision avoidance. However, current PF targets mainly standalone-mode AVs (SAVs) by evaluating their relative position and velocity. In addition, the risk energy of the PF is usually assigned an infinite value along the z-axis. Therefore, this study presents an adaptive potential field (APF) for connected autonomous vehicles (CAVs). Valuable information (heading angle, steering wheel angle, etc.) other than relative position and velocity is supplemented to PF. Furthermore, we separate the APF from the cost function of the model predictive controller (MPC) to compute the desired reference signals directly, saving more computation time. The proposed APF-MPC is co-simulated in a comparative driving scenario via MATLAB/Simulink and CarSim simulator compared with the latest PF-MPC method.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}