A driver can mask his sleepiness. This study aims to determine effective and reliable indications of a driver's unmasked sleepiness using driver-vehicle data. A Bayesian approach and the signal detection theory were applied to investigate the effectiveness of selected driver-vehicle parameters for this purpose. Twenty subjects participated in three consecutive driving sessions on the simulated 4-lane highway from Seoul to Cheonan, Korea, during which their PERCLOS (percentage of eye closure) data, assumed to be a true indicator of a driver's unmasked sleepiness, i.e., drowsiness, were monitored. Correlations between PERCLOS and the selected vehicle parameters, such as velocity RMSE (root-mean-square error), were analyzed while participants performed skill-based and rule-based driving tasks. The preliminary experimental results demonstrated that unmasked sleepiness, as indicated by PERCLOS, was not correlated with the selected vehicle parameters for skill-based tasks. Some rule-based tasks, such as VPVT (Visual Psychomotor Vigilance Task), showed significant correlations with masked and unmasked sleepiness, which shows that driver-vehicle data can potentially be used as a dynamic unmasked sleepiness indicator. More in-depth analysis is being conducted and is expected to be included in the final version of the manuscript.


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    Titel :

    Preliminary analysis of full-scale driving simulator data for unmasked sleepiness detection


    Beteiligte:
    Yang, Ji Hyun (Autor:in) / Yoon, Hong Joon (Autor:in) / Lee, Woon-Sung (Autor:in)


    Erscheinungsdatum :

    01.06.2014


    Format / Umfang :

    320049 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch