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.
@article{arxiv.2405.03935,
title = {Roadside Units Assisted Localized Automated Vehicle Maneuvering: An Offline Reinforcement Learning Approach},
author = {Kui Wang and Changyang She and Zongdian Li and Tao Yu and Yonghui Li and Kei Sakaguchi},
journal= {arXiv preprint arXiv:2405.03935},
year = {2024}
}