Automatic Route Design by Stepwise Subdivision of Virtual Walls —Reduces Route Length and Speeds Up Execution Time

Automatic Route Design by Stepwise Subdivision of Virtual Walls —Reduces Route Length and Speeds Up Execution Time—

Published:2024-06-20 Issue:3 Volume:36 Page:628-641
ISSN:1883-8049
Container-title:Journal of Robotics and Mechatronics
language:en
Short-container-title:JRM

Author:

Itoh Yuki¹,Hoshino Junya¹,Suzuki Tenta¹,Matsuda Kenji¹,Kumagae Kaito¹,Tobisawa Mao¹,Harada Tomohiro²^ORCID,Matsuoka Jyouhei¹^ORCID,Kagawa Toshinori³,Hattori Kiyohiko¹^ORCID

Affiliation:

1. Tokyo University of Technology, 1404-1 Katakuramachi, Hachioji, Tokyo 192-0982, Japan

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

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

Abstract

With the development of autonomous driving technology utilizing machine learning, AI, and sensors, research on autonomous driving control has become more active, and a large number of innovative studies are underway. In the near future, all autonomous vehicle fleets will be able to communicate with each other for sharing information and overall optimal traffic control will be achieved. One of the vehicle control systems that are based on the premise of such a fully automated society is the “signal-less intersection.” There is an intersection traffic control method that achieves safe and rational route selection by using virtual walls (VWs), which are virtual obstacles, but there are issues in terms of total route length and reduction of computation time. To address the issues, we propose a method that (1) prunes unneeded paths and (2) arranges VWs in a stepwise manner. The effectiveness of the proposed method was evaluated by simulation, and the results showed that the total route length and execution time were reduced.

Publisher

Fuji Technology Press Ltd.

Reference17 articles.

1. E. Marti, M. A. de Miguel, F. Garcia, and J. Perez, “A review of sensor technologies for perception in automated driving,” IEEE Intelligent Transportation Systems Magazine, Vol.11, No.4, pp. 94-108, 2019. https://doi.org/10.1109/MITS.2019.2907630

2. Y. Wu, L. Wu, and H. Cai, “A deep learning approach to secure vehicle to road side unit communications in intelligent transportation system,” Computers and Electrical Engineering, Vol.105, Article No.108542, 2023. https://doi.org/10.1016/j.compeleceng.2022.108542

3. S. Zeadally, J. Guerrero, and J. Contreras, “A tutorial survey on vehicle-to-vehicle communications,” Telecommunication Systems, Vol.73, No.3, pp. 469-489, 2020. https://doi.org/10.1007/s11235-019-00639-8

4. R. Saotome, K. Hattori, J. Matsuoka, and T. Harada, “Proposal of signal-less intersection control with dynamic obstacles optimized by using genetic algorithm — A concept of virtual walls –,” 6th Int. Symp. on Swarm Behavior and Bio-Inspired Robotics, pp. 1517-1522, 2023.

5. I. Ogawa et al., “Proposal of cooperative learning to realize motion control of RC cars group by Deep Q-network,” Proc. of the 31st Annual Conf. of Japanese Society for Artificial Intelligence, Article No.3I2-OS-13b-5, 2017 (in Japanese). https://doi.org/10.11517/pjsai.JSAI2017.0_3I2OS13b5