High-speed cruising scenarios with mixed traffic greatly challenge the road safety of autonomous vehicles (AVs). Unlike existing works that only look at fundamental modules in isolation, this work enhances AV safety in mixed-traffic high-speed cruising scenarios by proposing an integrated framework that synthesizes three fundamental modules, i.e., behavioral decision-making, path-planning, and motion-control modules. Considering that the integrated framework would increase the system complexity, a bootstrapped deep Q-Network (DQN) is employed to enhance the deep exploration of the reinforcement learning method and achieve adaptive decision making of AVs. Moreover, to make AV behavior understandable by surrounding HDVs to prevent unexpected operations caused by misinterpretations, we derive an inverse reinforcement learning (IRL) approach to learn the reward function of skilled drivers for the path planning of lane-changing maneuvers. Such a design enables AVs to achieve a human-like tradeoff between multi-performance requirements. Simulations demonstrate that the proposed integrated framework can guide AVs to take safe actions while guaranteeing high-speed cruising performance.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Enhancing High-Speed Cruising Performance of Autonomous Vehicles Through Integrated Deep Reinforcement Learning Framework


    Beteiligte:
    Liang, Jinhao (Autor:in) / Yang, Kaidi (Autor:in) / Tan, Chaopeng (Autor:in) / Wang, Jinxiang (Autor:in) / Yin, Guodong (Autor:in)


    Erscheinungsdatum :

    01.01.2025


    Format / Umfang :

    11076400 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    An Integrated DRL Framework for Autonomous High-Speed Cruising Control

    Liang, Jinhao / Feng, Jiwei / Tan, Chaopeng et al. | IEEE | 2024



    Cruising for Parking with Autonomous and Conventional Vehicles

    Mehdi Nourinejad / Matthew J. Roorda | DOAJ | 2021

    Freier Zugriff

    EDITORIAL - Cruising speed

    Online Contents | 2003


    High-Speed Collision Avoidance using Deep Reinforcement Learning and Domain Randomization for Autonomous Vehicles

    Kontes, Georgios D. / Scherer, Daniel D. / Nisslbeck, Tim et al. | IEEE | 2020