The complex mathematical model of autonomous vehicles makes it difficult for system identification due to a combination of non-linearity and uncertainty. Various strategies have been proposed to address the difficulty in system identification, as it significantly influences the precise path-tracking performance of autonomous vehicles. This paper proposes a Koopman operator approach data-driven optimal control algorithm for path-tracking of autonomous vehicles. To identify mathematical model’s various vehicle types of autonomous vehicle driving data were acquired in virtual simulation and real-world environments. An integrated linear model was identified using the Koopman operator neural network and the acquired driving data of autonomous vehicles. The identified integrated linear model was incorporated into a model predictive control algorithm designed for the path-tracking of autonomous vehicles. Reasonable path tracking performance was confirmed through performance evaluations conducted in path-tracking scenarios using various vehicle types for real and virtual vehicles in the real autonomous driving proving ground C-track and CARLA simulator environments. Index Terms—Koopman operator, deep neural network, model predictive control, autonomous vehicles, path-tracking


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    Titel :

    Koopman Operator Approach Data-Driven Optimal Control Algorithm for Autonomous Vehicles with various characteristics


    Beteiligte:
    Kim, Hakjoo (Autor:in) / Lee, Hwan-Hong (Autor:in) / Kee, Seok-Cheol (Autor:in)


    Erscheinungsdatum :

    2024-06-02


    Format / Umfang :

    2515270 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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