Roadside Units Assisted Localized Automated Vehicle Maneuvering: An Offline Reinforcement Learning Approach
<|reference_start|>Roadside Units Assisted Localized Automated Vehicle Maneuvering: An Offline Reinforcement Learning Approach: 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.<|reference_end|>
@article{wang2024roadside,
    title={Roadside Units Assisted Localized Automated Vehicle Maneuvering: An
  Offline Reinforcement Learning Approach},
    author={Kui Wang, Changyang She, Zongdian Li, Tao Yu, Yonghui Li, and Kei
  Sakaguchi},
    journal={arXiv preprint arXiv:2405.03935},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03935},
    primaryClass={eess.SY cs.SY}
}