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JRM Vol.36 No.3 pp. 642-657
doi: 10.20965/jrm.2024.p0642
(2024)

Paper:

Acquisition of Cooperative Control of Multiple Vehicles Through Reinforcement Learning Utilizing Vehicle-to-Vehicle Communication and Map Information

Tenta Suzuki*, Kenji Matsuda*, Kaito Kumagae*, Mao Tobisawa*, Junya Hoshino*, Yuki Itoh*, Tomohiro Harada** ORCID Icon, Jyouhei Matsuoka* ORCID Icon, Toshinori Kagawa***, and Kiyohiko Hattori* ORCID Icon

*Tokyo University of Technology
1404-1 Katakuramachi, Hachioji City, Tokyo 192-0983, Japan

**Saitama University
255 Shimo-Okubo, Sakura-ku, Saitama, Saitama 338-8570, Japan

***Central Research Institute of Electric Power Industry
2-6-1 Nagasaka, Yokosuka, Kanagawa 240-0196, Japan

Received:
November 28, 2023
Accepted:
May 1, 2024
Published:
June 20, 2024
Keywords:
multi-agent learning, autonomous driving, reinforcement learning, map utilization, vehicle-to-vehicle communication
Abstract

In recent years, extensive research has been conducted on the practical applications of autonomous driving. Much of this research relies on existing road infrastructure and aims to replace and automate human drivers. Concurrently, studies on zero-based control optimization focus on the effective use of road resources without assuming the presence of car lanes. These studies often overlook the physical constraints of vehicles in their control optimization based on reinforcement learning, leading to the learning of unrealistic control behaviors while simplifying the implementation of ranging sensors and vehicle-to-vehicle communication. Additionally, these studies do not use map information, which is widely employed in autonomous driving research. To address these issues, we constructed a simulation environment that incorporates physics simulations, realistically implements ranging sensors and vehicle-to-vehicle communication, and actively employs map information. Using this environment, we evaluated the effect of vehicle-to-vehicle communication and map information on vehicle control learning. Our experimental results show that vehicle-to-vehicle communication reduces collisions, while the use of map information improves the average vehicle speed and reduces the average lap time.

Utilizing vehicle-to-vehicle communication and map information

Utilizing vehicle-to-vehicle communication and map information

Cite this article as:
T. Suzuki, K. Matsuda, K. Kumagae, M. Tobisawa, J. Hoshino, Y. Itoh, T. Harada, J. Matsuoka, T. Kagawa, and K. Hattori, “Acquisition of Cooperative Control of Multiple Vehicles Through Reinforcement Learning Utilizing Vehicle-to-Vehicle Communication and Map Information,” J. Robot. Mechatron., Vol.36 No.3, pp. 642-657, 2024.
Data files:
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