This paper presents the design and implementation of an intelligent switched control for lateral control of autonomous vehicles. The switched control is designed based on Linear Parameter-Varying (LPV) and Youla-Kucera (YK) parameterization. The proposed intelligent system aims to optimize the control switching performance using a Reinforcement Learning (RL) model. The presented approach studies the critical problem of initial or sudden large lateral errors in lane-tracking or lane-changing. It ensures stable and smooth switching performance to provide a smooth vehicle response regardless of the lateral error. The proposed RL-based switching strategy is validated using a RENAULT simulator on MATLAB, and compared to another modeled switching strategy with encouraging results.


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    Title :

    Intelligent Control Switching for Autonomous Vehicles based on Reinforcement Learning*


    Contributors:


    Publication date :

    2022-06-05


    Size :

    1587014 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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