Physiological Measures of Risk Perception in Highly Automated Driving

J. R. Perello-March, C. G. Burns, S. A. Birrell, R. Woodman, M. T. Elliott

    Research output: Contribution to journalArticlepeer-review

    12 Citations (Scopus)
    132 Downloads (Pure)

    Abstract

    Highly automated driving will likely result in drivers being out-of-the-loop during specific scenarios and engaging in a wide range of non-driving related tasks. Manifesting in lower levels of risk perception to emerging events, and thus affect drivers' availability to take-over manual control in safety-critical scenarios. In this empirical research, we measured drivers' (N = 20) risk perception with cardiac and skin conductance indicators through a series of high-fidelity, simulated highly automated driving scenarios. By manipulating the presence of surrounding traffic and changing driving conditions as long-term risk modulators, and including a driving hazard event as a short-term risk modulator, we hypothesised that an increase in risk perception would induce greater physiological arousal. Our results demonstrate that heart rate variability features are superior at capturing arousal variations from these long-term, low to moderate risk scenarios. In contrast, skin conductance responses are more sensitive to rapidly evolving situations associated with moderate to high risk. Based on this research, future driver state monitoring systems should adopt multiple physiological measures to capture changes in the long and short term, modulation of risk perception. This will enable enhanced perception of driver readiness and improved availability to safely deal with take-over events when requested by an automated vehicle.
    Original languageEnglish
    Pages (from-to)4811-4822
    Number of pages12
    JournalIEEE Transactions on Intelligent Transportation Systems
    Volume23
    Issue number5
    Early online date7 Feb 2022
    DOIs
    Publication statusPublished - May 2022

    Bibliographical note

    This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

    Funder

    10.13039/501100000741-University of Warwick

    Keywords

    • Vehicles
    • Monitoring
    • Task analysis
    • Biomedical monitoring
    • Skin
    • Stress
    • Human factors
    • Driver state monitoring
    • Highly automated driving
    • Monitoring request
    • Take-over request
    • Risk perception.

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