Drivers often engage in secondary in-vehicle activity that is not related to vehicle control. This may be functional and/or to relieve monotony. Regardless, drivers believe they can safely do so when their perceived workload is low. In this paper, we describe a data acquisition system and machine learning based algorithms to determine perceived workload. Data collected were from on-road driving in light and heavy traffic, and individual physiological measures were recorded while the driver also performed in-vehicle tasks. Initial results show how the workload function can be personalized to an individual, and what implications this may have for vehicle design.


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    Titel :

    Personalized Driver Workload Estimation in Real-World Driving


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Kochhar, Dev S. (Autor:in) / Murphey, Yi (Autor:in) / Xie, Yongquan (Autor:in)

    Kongress:

    WCX World Congress Experience ; 2018



    Erscheinungsdatum :

    2018-04-03




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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