This paper presents a novel hierarchical reinforcement learning (HRL) framework for automated overtaking. The proposed framework is developed based on the semi-Markov decision process (SMDP) and motion primitives (MPs) which can be applied to different overtaking phases. Unlike the high-level decision and low-level control which are usually independent with each other, the high-level decision making and low-level control are combined by defining MPs with different time intervals. As for the high-level decision making, a SMDP Q-learning algorithm is adopted to realize decision-making of MPs. Besides, a development method of MPs used in the low-level control of automated overtaking is proposed. The performance of the HRL framework is tested in the simulation environment built in a driving simulator called CARLA. The results show that the HRL framework can determine the optimal trajectory under different driving styles of the overtaken vehicle.


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    Title :

    Hierarchical Reinforcement Learning Combined with Motion Primitives for Automated Overtaking


    Contributors:
    Yu, Yang (author) / Lu, Chao (author) / Yang, Lei (author) / Li, Zirui (author) / Hu, Fengqing (author) / Gong, Jianwei (author)


    Publication date :

    2020-10-19


    Size :

    510358 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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