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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    Titel :

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


    Beteiligte:
    Seo, Ju Won (Autor:in) / Ko, Chan Hyeok (Autor:in) / Sung, Ji Ho (Autor:in) / Yun, Dong Geun (Autor:in) / Lee, Byeongyu (Autor:in) / Kim, Jin Sung (Autor:in) / Park, Taewoong (Autor:in) / Park, Ho Sung (Autor:in) / Ju, Seong Pil (Autor:in) / Chung, Chung Choo (Autor:in)


    Erscheinungsdatum :

    24.09.2024


    Format / Umfang :

    4225506 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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