This paper presents a learning-based methodology for developing an optimal lane-changing control policy for a Remote Controlled (RC) car using real-time sensor data. The RC car is equipped with sensors including GPS, IMU devices, and a camera integrated in an Nvidia Jetson AGX Xavier board. By a novel Adaptive Dynamic Programming (ADP) algorithm, our RC car is capable of learning the optimal lane-changing strategies based on the real-time processed measurement from the sensors. The experimental outcomes show that our learning-based control algorithm can be effectively implemented, adapt to parameter changes, and complete the lane changing tasks in a short learning time with satisfactory performance.


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    Titel :

    Automated Lane Changing Through Learning-Based Control: An Experimental Study


    Beteiligte:
    Ha, Won Yong (Autor:in) / Chakraborty, Sayan (Autor:in) / Yu, Yujie (Autor:in) / Ghasemi, Samin (Autor:in) / Jiang, Zhong-Ping (Autor:in)


    Erscheinungsdatum :

    24.09.2023


    Format / Umfang :

    6445331 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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