Complex traffic scenes greatly challenge the road safety of automated vehicles (AVs). Recent work only provides an independent perspective from the fundamental modules. This paper integrates the decision-making and path-planning modules to ensure the autonomous driving performance in the high-speed cruising scenario. First, to guarantee deep exploration of the reinforcement learning method, a Bootstrapped deep-Q-Network (BDQN) is proposed to address the adaptive decision-making of AVs. Then, quantifying the multi-performance requirements of AVs under high-speed cruising can be complex. We employ an inverse reinforcement learning (IRL) approach to learn path-planning ability from skilled drivers, generating a reference path for executing lane changes. The simulation results demonstrate the proposed framework can ensure the autonomous cruising performance with safety guarantees.


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    Title :

    An Integrated DRL Framework for Autonomous High-Speed Cruising Control


    Contributors:
    Liang, Jinhao (author) / Feng, Jiwei (author) / Tan, Chaopeng (author) / Zhou, Chaobin (author)


    Publication date :

    2024-11-08


    Size :

    532174 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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