Aiming at the trajectory tracking problem of autonomous vehicles, a Model Predictive Control (MPC) method imitating a driver is proposed. Using vehicle's dynamics as a prediction model, a linear time-varying model predictive controller is utilized to solve the optimal front wheel angle. According to the actual situation in which the driver pays more attention to the driving conditions in the close distance, the weight proportions of different step lengths in the prediction time domain are adjusted. A vehicle model is constructed in Carsim platform, and joint simulation with MATLAB/Simulink is performed to validate the effectiveness of the proposed controller. Simulation results show, compared to the traditional MPC algorithm, that the proposed control strategy can make the lateral displacement error of trajectory tracking decrease, improves tracking accuracy, and reduces fluctuations in the front wheel angle, thereby significantly enhancing the stability of trajectory tracking.


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    Title :

    Research on trajectory tracking of autonomous vehicle based on human imitation


    Contributors:
    Saxena, Sandeep (editor) / Zhao, Cairong (editor) / Lei, Tianyu (author) / Mao, Jin (author) / Yu, Ze (author) / Pan, Junwen (author)

    Conference:

    International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023) ; 2023 ; Yinchuan, China


    Published in:

    Proc. SPIE ; 12941


    Publication date :

    2023-12-07





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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