Continuous estimation the driver's take-over readiness is critical for safe and timely transfer of control during the failure modes of autonomous vehicles. In this article, we propose a data-driven approach for estimating the driver's take-over readiness based purely on observable cues from in-vehicle vision sensors. We present an extensive naturalistic driving dataset of drivers in a conditionally autonomous vehicle running on Californian freeways. We collect subjective ratings for the driver's take-over readiness from multiple human observers viewing the sensor feed. Analysis of the ratings in terms of intra-class correlation coefficients (ICCs) shows a high degree of consistency in the ratings across raters. We define a metric for the driver's take-over readiness termed the ‘Observable Readiness Index (ORI)’ based on the ratings. Finally, we propose an LSTM model for continuous estimation of the driver's ORI based on a holistic representation of the driver's state, capturing gaze, hand, pose and foot activity. Our model estimates the ORI with a mean absolute error of 0.449 on a 5 point scale.


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    Titel :

    Looking at the Driver/Rider in Autonomous Vehicles to Predict Take-Over Readiness


    Beteiligte:
    Deo, Nachiket (Autor:in) / Trivedi, Mohan M. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.03.2020


    Format / Umfang :

    4673434 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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