On-ramp merging is a complex and high-accidents scenario. The complexity of on-ramp merging scenario is mainly reflected in the aspects of varied merging environment and uncertain driving behavior of the surrounding vehicles, so it is a challenging scenario for autonomous vehicles. In this paper, a deep Q-learning network (DQN)-based merging decision-making method is proposed for autonomous vehicles. First, the models of vehicle dynamics and on-ramp merging scenarios are built, and the merging motivation is quantitative for designing the reinforcement learning. Second, the actions of the autonomous vehicle are defined, and a value-based DQN network for the merging decision-making is designed by simultaneously considering the driving safety, efficiency and comfort. Finally, in order to evaluate the performance of DQN-based merging decision-making method, a dynamic programming (DP)-based merging decision-making method is selected as the benchmarked method. The validation results demonstrate the autonomous vehicle by the proposed DQN-based merging decision-making method possesses good performance in safety, efficiency and comfort at on-ramp merging scenarios.


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    Titel :

    Reinforcement Learning-Based On-Ramp Merging Decision-Making for Autonomous Vehicles


    Beteiligte:
    Ma, Ning (Autor:in) / Zhang, Ying (Autor:in) / Cai, Wangze (Autor:in) / Qi, Haoran (Autor:in) / Zhang, Tianrong (Autor:in) / Fu, Chenxiu (Autor:in)


    Erscheinungsdatum :

    27.10.2023


    Format / Umfang :

    2836687 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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