Longitudinal following control is the fundamental component of vehicle platoon control. Although cooperation of vehicles helps to improve traffic efficiency, it exacerbates the non-linearity of the platoon control system as well. The promising development of reinforcement learning and neural networks brings inspiring ideas to the designs of the controller for the non-linear system. This paper proposes a novel control algorithm for longitudinal platoon following based on deep deterministic policy gradient (DDPG) reinforcement learning algorithms. Focusing on longitudinal control, this paper builds single-lane scenarios for platoon control simulation with Python programming language. Simulation results indicate that with the help of intelligent transportation systems (ITS), the proposed DDPG controller performs better than conventional adaptive cruise control, especially on vehicles’ velocity following and the convergence speed of the controller.


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    Titel :

    A Novel Vehicle Platoon Following Controller Based on Deep Deterministic Policy Gradient Algorithms


    Beteiligte:
    Wang, Guan (Autor:in) / Hu, Jianming (Autor:in) / Huo, Yusen (Autor:in) / Zhang, Zuo (Autor:in)

    Kongress:

    18th COTA International Conference of Transportation Professionals ; 2018 ; Beijing, China


    Erschienen in:

    CICTP 2018 ; 76-86


    Erscheinungsdatum :

    2018-07-02




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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