This study proposed a vehicle state prediction model, which can predict vehicle yaw rate and lateral acceleration in the prediction horizon on the basis of the information of state and control input at current sampling time. This prediction model of lateral acceleration obtained by bringing change rate of sideslip angle and yaw rate into lateral jerk expression, and the prediction result is modified by the steering speed at the same time to elevate the accurateness of this prediction model. The simulation results of constant frequency sine maneuver and double lane change maneuver show that the prediction model could predict the vehicle state in prediction horizon well and prediction error is kept within a small range.


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    Titel :

    A Lateral Acceleration Prediction Model based on Current Vehicle State


    Beteiligte:
    Li, ShaoSong (Autor:in) / Wang, XuYang (Autor:in) / Lu, XiaoHui (Autor:in) / Wang, ZaiXiao (Autor:in) / Yu, ZhiXin (Autor:in)


    Erscheinungsdatum :

    28.10.2022


    Format / Umfang :

    6931486 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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