Urban driving requires the autonomous vehicles to drive with smooth control and track the planned path accurately. However, most of the existing path-tracking controllers pay more attention to the tracking errors than the smoothness because of the difficulties to balance them. This paper proposes a learning-based method to achieve the trade-off between the smooth control and the tracking-error control. An Reinforcement Learning algorithm, which is called Proximal Policy Optimization, is used to train a neural model to tune the weights of a designed controller PP_PID (Pure-Pursuit_Proportional Integral Derivative). The successfully trained model will adaptively select the optimal weights for the Pure-Pursuit and $PID$ to guarantee the control smoothness and accuracy. Finally, the proposed controller will be tested in two path tracking scenarios. The results show that the proposed controller can change the weights adaptively to maintain a balance in the tracking error and lateral acceleration under the 35km/h.


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    Titel :

    An Adaptive Path Tracking Controller Based on Reinforcement Learning with Urban Driving Application


    Beteiligte:
    Chen, Longsheng (Autor:in) / Chen, Yuanpeng (Autor:in) / Yao, Xiangtong (Autor:in) / Shan, Yunxiao (Autor:in) / Chen, Long (Autor:in)


    Erscheinungsdatum :

    01.06.2019


    Format / Umfang :

    1428337 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    AN ADAPTIVE PATH TRACKING CONTROLLER BASED ON REINFORCEMENT LEARNING WITH URBAN DRIVING APPLICATION

    Chen, Longsheng / Chen, Yuanpeng / Yao, Xiangtong et al. | British Library Conference Proceedings | 2019


    Deep reinforcement learning based path tracking controller for autonomous vehicle

    Chen, I-Ming / Chan, Ching-Yao | SAGE Publications | 2021




    Urban Driving with Multi-Objective Deep Reinforcement Learning

    Li, Changjian / Czarnecki, Krzysztof | ArXiv | 2018

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