Identification of road grade and driver mode under complex driving conditions are the key to the commercial application of the autonomous vehicle. Accurate estimation results are helpful for improved vehicle road handling and ride comfort performances. In this paper, the Monte Carlo (MC) method of operating mode recognition is designed using data collected from the full-vehicle road test to identify the driver’s mode and the road surface level information under complex driving conditions. Based on the movement states of the vehicle system, the models of road excitation and driver style are first constructed. Moreover, the standard classification of driver style and road conditions based on the test data is carried out by using the nonlinear statistical theory and Markov chain MC method. The consistency model of driver style and road conditions is established by combining the recognition accuracy of standard classification, and the effectiveness of the designed MC method is verified by using the random control stability criterion. Finally, the proposed approach is validated under various external driver inputs with field test results, and results show that it can provide accurate input for vehicle chassis integrated control.


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    Titel :

    Driving Style Consistency Recognition for Vehicle System Based on Monte Carlo Approach


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Zhang, Minglu (Autor:in) / Zhang, Xiaojun (Autor:in) / Nie, Yanxin (Autor:in)

    Kongress:

    Automotive Technical Papers ; 2022



    Erscheinungsdatum :

    2022-03-22




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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