Deep reinforcement learning has been applied to the car-following control of autonomous driving in recent years. However, the safety of car-following through reinforcement learning remains a problem. To address this problem, a feedback forecasting based deep reinforcement learning algorithm is proposed. A differential equation model is used to predict the collision of adjacent vehicles. The output action of reinforcement learning is supervised by the feedback single to achieve a safe distance. As a result, the performance of autonomous driving safety is improved while others are slightly affected. The simulation results, based on NGSIM data, show that the predict collision avoidance algorithm is feasible and effective in the car-following control of autonomous driving.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Feedback Forecasting based Deep Deterministic Policy Gradient Algorithm for Car-Following of Autonomous Vehicle


    Beteiligte:
    Zheng, Yuqi (Autor:in) / Yan, Ruidong (Autor:in) / Jia, Bin (Autor:in) / Jiang, Rui (Autor:in)


    Erscheinungsdatum :

    15.10.2021


    Format / Umfang :

    4803035 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    DETC2019-97884 Autonomous Vehicle Driving via Deep Deterministic Policy Gradient

    Huang, Wenhui / Braghin, Francesco / Arrigoni, Stefano | British Library Conference Proceedings | 2019


    Stability Analysis for Autonomous Vehicle Navigation Trained over Deep Deterministic Policy Gradient

    Cabezas Olivenza, Mireya / Zulueta Guerrero, Ekaitz / Sánchez Chica, Ander et al. | BASE | 2023

    Freier Zugriff



    Autonomous Task Planning of Intelligent Unmanned Aerial Vehicle Swarm Based on Deep Deterministic Policy Gradient

    Qiang Jiang / Yongzhao Yan / Yinxing Dai et al. | DOAJ | 2025

    Freier Zugriff