An adaptive cruise control system prototype based on self-learning algorithm for driver characteristics is presented. To imitate the driver operations during car-following, a driver model is developed to generate the desired throttle depression and braking pressure. A self-learning algorithm for driver characteristics is proposed based on the recursive least square method with forgetting factor. Using this algorithm, the parameters of the driver model are real-time identified from the data sequences collected during the driver manual operation state, and the identification result is applied during the system automatic control state. The system is verified in a driving assistance system testbed with electronic throttle and electro-hydraulic brake actuators. The experimental results show that the self-learning algorithm is effective and the system performance is adaptive to driver characteristics.


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    Title :

    An adaptive cruise control system based on self-learning algorithm for driver characteristics


    Additional title:

    Ein adaptives Tempomatsystem auf Basis des Selbstlernalgorithmus für die Fahrercharakteristik


    Contributors:
    Zhang, Lei (author) / Wang, Jianqiang (author) / Li, Keqiang (author)


    Publication date :

    2009


    Size :

    10 Seiten, 7 Bilder, 1 Tabelle, 10 Quellen



    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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