The goal of this study is to provide a framework, using hidden semi-Markov models, for modeling a driver’s response time after an alert is provided in manual driving. Given the plethora of alerts and warning within a vehicle, there is a need to understand when a driver will respond after an alert is provided. Data from a previous driving simulator study, where drivers were interacting with an in-vehicle information system (IVIS) were used for model training. The final data set included 16 participants, with 288 task initiations. The proposed model could predict a driver’s response time accurately using only a small portion of the available data, and had a mean absolute error of 0.51 seconds with 84% of predictions within an absolute error of 1 second. This framework has applicability in mitigating the risk of transitions in driver distraction. This includes transitions from the road to the secondary task and back to the road.


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    Titel :

    Predicting Driver’s Transition Time to a Secondary Task Given an in-Vehicle Alert


    Beteiligte:
    Hwang, Steven (Autor:in) / Banerjee, Ashis G. (Autor:in) / Boyle, Linda Ng (Autor:in)


    Erscheinungsdatum :

    2022-05-01


    Format / Umfang :

    1580903 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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