Autonomous driving technology is one of the research hotspots in recent years. The development of some basic technologies such as motor sensors, vehicle to everything (V2X) and high-definition map also provides more powerful technical support for autonomous driving. However, it remains a challenge to propose a common decision strategy for autonomous driving. Considering the scenario that the networks can cover various urban roads through road side units (RSUs), this paper proposes a two-satage autonomous driving decision strategy based on environmental awareness and asynchronous advantage actor critic (A3C) algorithm. The first stage proposes a preliminary common model with safety guarantee for all roads; based on which the second stage trains a unique model for each road in consideration of different road features. Simulation results indicate that the proposed strategy can ensure high safety performance and driving efficiency.


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    Titel :

    A Distributed Driving Decision Scheme Based on Reinforcement Learning for Autonomous Driving Vehicles


    Beteiligte:
    Liu, Jie (Autor:in) / Zhao, Long (Autor:in) / Zheng, Kan (Autor:in) / Zhou, Qihao (Autor:in)


    Erscheinungsdatum :

    2020-05-01


    Format / Umfang :

    488888 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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