Deep reinforcement learning (DRL) is confronted with the significant problem of sparse rewards for autonomous driving in heavy traffic because of the dynamic and diverse nature of the driving environment as well as the complexity of the driving task. To mitigate the impact of sparse rewards on the convergence process of DRL, this paper proposes a novel behavioral-adaptive deep Q-network (BaDQN) for autonomous driving decisions in heavy traffic. BaDQN applies the idea of task decomposition to the DRL process. To break down the complexity of the driving task and achieve shorter exploration paths, BaDQN divides the driving task into three subtasks: Lane-Changing, Posture-Adjustment, and Wheel-Holding. BaDQN uses the finite state machine (FSM) to model the collaborative relationship between different subtasks, and abstracts each subtask separately using the Markov decision process (MDP). We used the Carla simulator to conduct experiments in a specific heavy traffic scenario. Compared with previous methods, BaDQN achieves a longer safe driving distance and a higher success rate. To discuss the adaptability of BaDQN to changes in traffic density and traffic velocity, we also conducted two extended experiments, which fully demonstrated the performance stability of BaDQN.


    Zugriff

    Download

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Behavioral-Adaptive Deep Q-Network for Autonomous Driving Decisions in Heavy Traffic


    Weitere Titelangaben:

    Transportation Research Record: Journal of the Transportation Research Board


    Beteiligte:
    Liu, Zhicheng (Autor:in) / Yu, Hong (Autor:in)


    Erscheinungsdatum :

    2024-07-07




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    CONNECTED AND AUTONOMOUS VEHICLE (CAV) BEHAVIORAL ADAPTIVE DRIVING

    LIGHT DAVID / SMITH GAVIN / REYMANN STEFFEN | Europäisches Patentamt | 2020

    Freier Zugriff

    Connected and Autonomous Vehicle (CAV) Behavioral Adaptive Driving

    LIGHT DAVID / SMITH GAVIN / REYMANN STEFFEN | Europäisches Patentamt | 2020

    Freier Zugriff


    A behavioral planning framework for autonomous driving

    Wei, Junqing / Snider, Jarrod M. / Gu, Tianyu et al. | IEEE | 2014


    A BEHAVIORAL PLANNING FRAMEWORK FOR AUTONOMOUS DRIVING

    Wei, J. / Snider, J. / Gu, T. et al. | British Library Conference Proceedings | 2014