Driver drowsiness is an important public safety issue. The difficulty in monitoring the driver’s individual differences makes the error rate of fatigue judgments of different individuals larger. Nine steering wheel variables and three reaction time variables were measured as participants drove a fixed road course in a high-fidelity motion-based driving simulator. Drowsiness levels of participants were judged by the expert scoring method combined with the driver’s self-described method using the Stanford sleepiness scale. For each driver, a hidden Markov model (HMM) was trained and recognition of a given observation sequence was performed to score each drowsiness driving model. Ellipse, NMRHOLD, PNS, SW_Range_2, RT1, and RT2 were selected through one-way repeated ANOVA as the characteristic of drowsiness. Sixteen HMMs were identified by data sequences of 16 drivers and the accuracy rate was 87.5%. It is recommended that HMM could be used to detect driver drowsiness and address the inaccuracies caused by individual differences.


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    Titel :

    Driver Drowsiness Detection Considering Individual Specifics: A Simulator Study


    Beteiligte:
    Li, Shiwu (Autor:in) / Chai, Meng (Autor:in) / Guo, Mengzhu (Autor:in)

    Kongress:

    20th COTA International Conference of Transportation Professionals ; 2020 ; Xi’an, China (Conference Cancelled)


    Erschienen in:

    CICTP 2020 ; 2509-2520


    Erscheinungsdatum :

    2020-12-09




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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    Driver Drowsiness Detection

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