In this paper, four typical driving behaviors with strong risk of cognitive distraction were obtained through questionnaire survey, and simulated driving experiments of normal driving and four typical driving behavior sub-tasks were carried out. Eight characteristic indicators are selected from the characteristic values of vehicle operation information, and a long-short-term memory neural network model (LSTM) is established to discriminate driving distraction, which is compared with SVM model. The results show that the LSTM model can accurately identify the cognitive distraction state of the driver.


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    Titel :

    Driver cognitive distraction recognition


    Beteiligte:
    Zhang, Min (Autor:in) / Gai, Jiaoyun (Autor:in) / Zhang, Jinglei (Autor:in) / Li, Anmengdie (Autor:in) / Feng, Huanchao (Autor:in) / Wang, Kaili (Autor:in)

    Kongress:

    Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022) ; 2022 ; Guangzhou,China


    Erschienen in:

    Proc. SPIE ; 12302


    Erscheinungsdatum :

    23.11.2022





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Driver cognitive distraction recognition

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    Freier Zugriff

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    Driver cognitive distraction detection: Feature estimation and implementation

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