Automobile driver's intention recognition is used to predict what a driver will do the next moment. Generally, driving warning system only considers environment perception, the recognition of safe distance and the warning methods, and it rarely takes driver's intention into account. Therefore, its effectiveness and accuracy is not satisfied. According to driver's acceleration characteristic, we analyze the driving operation behavior and vehicle status information, and propose data sequences about driver's operation data. The data sequences are used to describe the successive driving operation data present and before, the model added by the data sequences can improve the recognition accuracy. Then, based on fuzzy clustering, a model for recognizing driver's acceleration intention is proposed. The driving simulation experiment aiming at recognizing driver's acceleration intention is carried out. Experiment results indicate that the proposed recognition model combined fuzzy clustering with the data sequences can effectively recognize driver's acceleration intention. The model can be used to improve the effectiveness and accuracy of current driving warning system.


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    Titel :

    A Recognition Model for Acceleration Intention of Automobile Drivers Based on Fuzzy Clustering


    Beteiligte:
    Zhang, Rui (Autor:in) / Yan, Xinping (Autor:in) / Wu, Chaozhong (Autor:in)

    Kongress:

    First International Conference on Transportation Information and Safety (ICTIS) ; 2011 ; Wuhan, China


    Erschienen in:

    ICTIS 2011 ; 1938-1947


    Erscheinungsdatum :

    16.06.2011




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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