This paper proposes a novel nonlinear system modeling technique using a data-driven approach with temporal information for adaptive cruise control (ACC) focused on reducing motion sickness through model predictive control (MPC). We develop an approximated human model from real-world data to enhance motion prediction and integrate it into MPC's cost function and constraints, emphasizing tracking performance, control effort, and motion sickness reduction. Using the ISO 2631-1:1977 standard, motion sickness is evaluated with the motion sickness dose value (MSDV) in the longitudinal axis of human motion. Validated through MATLAB/Simulink simulations, our method improves low-frequency human motion prediction accuracy, reduces RMSE and maximum error by 5.23% and 23.4%, lowers MSDV by 17% in ACC scenarios, and increases car-following performance by 42.3% compared to previous methods.


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    Title :

    Data-driven Human Modeling based on Temporal Information and Nonlinear Model Predictive Control for Adaptive Cruise Control Reducing Motion Sickeness


    Contributors:


    Publication date :

    2024-09-24


    Size :

    4225506 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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