[2021 - 2025]

@inproceedings{Asabe2023b,

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

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

url = {https://github.com/tlab-wide/AutowareV2X

https://tlab-wide.github.io/AutowareV2X/main/

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

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

year  = {2023},

date = {2023-06-20},

urldate = {2023-06-20},

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

address = {Florence, Italy},

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

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Hirata2021b,

title = {Roadside-assisted Cooperative Planning using Future Path Sharing for Autonomous Driving},

author = {Mai Hirata and Manabu Tsukada and Keisuke Okumura and Yasumasa Tamura and Hideya Ochiai and Xavier Défago},

url = {https://arxiv.org/abs/2108.04629

https://youtu.be/xaBIQC0SClE},

doi = {10.1109/VTC2021-Fall52928.2021.9625324},

year  = {2021},

date = {2021-09-27},

urldate = {2021-09-27},

booktitle = {IEEE 94th Vehicular Technology Conference (VTC2021-Fall)},

address = {Online},

abstract = {Cooperative intelligent transportation systems (ITS) are used by autonomous vehicles to communicate with surrounding autonomous vehicles and roadside units (RSU). Current C-ITS applications focus primarily on real-time information sharing, such as cooperative perception. In addition to real-time information sharing, self-driving cars need to coordinate their action plans to achieve higher safety and efficiency. For this reason, this study defines a vehicle’s future action plan/path and designs a cooperative path-planning model at intersections using future path sharing based on the future path information of multiple vehicles. The notion is that when the RSU detects a potential conflict of vehicle paths or an acceleration opportunity according to the shared future paths, it will generate a coordinated path update that adjusts the speeds of the vehicles. We implemented the proposed method using the open-source Autoware autonomous driving software and evaluated it with the LGSVL autonomous vehicle simulator. We conducted simulation experiments with two vehicles at a blind intersection scenario, finding that each car can travel safely and more efficiently by planning a path that reflects the action plans of all vehicles involved. The time consumed by introducing the RSU is 23.0 % and 28.1 % shorter than that of the stand-alone autonomous driving case at the intersection.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Mizutani2021b,

title = {AutoMCM: Maneuver Coordination Service with Abstracted Functions for Autonomous Driving},

author = {Masaya Mizutani and Manabu Tsukada and Hiroshi Esaki},

url = {https://arxiv.org/abs/2107.06627

https://youtu.be/eC7L0R_1Ybo

https://youtu.be/s3l5zypizxQ

https://youtu.be/XBpZeT-apGE},

doi = {10.1109/ITSC48978.2021.9564556},

year  = {2021},

date = {2021-09-19},

urldate = {2021-09-19},

booktitle = {24th IEEE International Conference on Intelligent Transportation (ITSC)},

address = {Indianapolis, IN, United States},

abstract = {A cooperative intelligent transport system (C-ITS) uses vehicle-to-everything (V2X) technology to make self-driving vehicles safer and more efficient. Current C-ITS applications have mainly focused on real-time information sharing, such as for cooperative perception. In addition to better real-time perception, self-driving vehicles need to achieve higher safety and efficiency by coordinating action plans. This study designs a maneuver coordination (MC) protocol that uses seven messages to cover various scenarios and an abstracted MC support service. We implement our proposal as AutoMCM by extending two open-source software tools: Autoware for autonomous driving and OpenC2X for C-ITS. The results show that our system effectively reduces the communication bandwidth by limiting message exchange in an event-driven manner. Furthermore, it shows that the vehicles run 15% faster when the vehicle speed is 30 km/h and 28% faster when the vehicle speed is 50 km/h using our scheme. Our system shows robustness against packet loss in experiments when the message timeout parameters are appropriately set.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}