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Toward Driver State Models that Explain Interindividual Variability of Distraction for Adaptive Automation

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HCI in Mobility, Transport, and Automotive Systems. Driving Behavior, Urban and Smart Mobility (HCII 2020)

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Abstract

Although there exist many models of distracted driving, identifying a distracted driver is still challenging as distraction might appear differently for different drivers but also within an individual driver in different situations. Here we present a driver state model that focusses on safety-relevant driver-distraction by conceptualizing driving control as influenced by both environmental factors and individual preferences. Also, the model differentiates compensatory control from exploratory control movements to better diagnose driving distraction. We then test several predictions that are derived from this model in a driving-simulator study. In this study participants drove the same road with or without a secondary task while their eye movements and driving performance was recorded. Our results are consistent with previous findings that overall steering control actions increase in the distraction condition but also that exploratory steering movements are apparently more sensitive indicators for distraction than compensatory control actions.

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Acknowledgment

The publication was written at VIRTUAL VEHICLE Research Center in Graz and partially funded by the COMET K2 – Competence Centers for Excellent Technologies Programme of the Federal Ministry for Transport, Innovation and Technology (bmvit), the Federal Ministry for Digital, Business and Enterprise (bmdw), the Austrian Research Promotion Agency (FFG), the Province of Styria and the Styrian Business Promotion Agency (SFG).

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Authors and Affiliations

  1. Virtual Vehicle Research GmbH, Graz, Austria

    Margit Höfler & Peter Moertl

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  1. Margit Höfler
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  2. Peter Moertl
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Correspondence to Peter Moertl .

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Editors and Affiliations

  1. Institute of Media Technology, Ilmenau University of Technology, Ilmenau, Germany

    Heidi Krömker

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Höfler, M., Moertl, P. (2020). Toward Driver State Models that Explain Interindividual Variability of Distraction for Adaptive Automation. In: Krömker, H. (eds) HCI in Mobility, Transport, and Automotive Systems. Driving Behavior, Urban and Smart Mobility. HCII 2020. Lecture Notes in Computer Science(), vol 12213. Springer, Cham. https://doi.org/10.1007/978-3-030-50537-0_2

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  • DOI: https://doi.org/10.1007/978-3-030-50537-0_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50536-3

  • Online ISBN: 978-3-030-50537-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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