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-theart 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.


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    Titel :

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


    Beteiligte:
    Wang, Kui (Autor:in) / She, Changyang (Autor:in) / Li, Zongdian (Autor:in) / Yu, Tao (Autor:in) / Li, Yonghui (Autor:in) / Sakaguchi, Kei (Autor:in)


    Erscheinungsdatum :

    24.09.2024


    Format / Umfang :

    8060666 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch