WANG, KUI (Tokyo Institute of Technology), She, Changyang (The University of Sydney), Li, Zongdian (Tokyo Institute of Technology), Yu, Tao (Tokyo Institute of Technology), Li, Yonghui (The University of Sydney), Sakaguchi, Kei (Tokyo Institute of Technology)

Roadside Units Assisted Localized Automated Vehicle Maneuvering: An Offline Reinforcement Learning Approach

Scheduled for presentation during the Regular Session "Driving based on reinforcement learning" (ThAT6), Thursday, September 26, 2024, 10:50−11:10, Salon 14

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

Traffic intersections present significant challenges for the safe and efficient maneuvering of connected and automated vehicles (CAVs). This research proposes an innovative roadside unit (RSU)-assisted cooperative maneuvering system aimed at enhancing road safety and traveling efficiency at intersections for CAVs. We utilize RSUs for real-time traffic data acquisition and train an offline reinforcement learning (RL) algorithm based on human driving data. Evaluation results obtained from hardware-in-loop autonomous driving simulations show that our approach employing the twin delayed deep deterministic policy gradient and behavior cloning (TD3+BC), achieves performance comparable to state-of-the-art autonomous driving systems in terms of safety measures while significantly enhancing travel efficiency by up to 17.38% in intersection areas. This paper makes a pivotal contribution to the field of intelligent transportation systems, presenting a breakthrough solution for improving urban traffic flow and safety at intersections.