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Measures for Well-Being in Highly Automated Vehicles: The Effect of Prior Experience

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HCI in Mobility, Transport, and Automotive Systems. Automated Driving and In-Vehicle Experience Design (HCII 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12212))

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Abstract

Highly automated vehicles are likely to cause a paradigm shift, as they profoundly affect user behavior and vehicle design. To reflect this change in a user-centric approach to designing automated vehicles, passenger well-being can be a relevant variable. To be able to consider passenger well-being appropriately, valid and reliable measures are required. In study contexts or industry applications, restrictions may apply that limit the selection of measures for passenger well-being. Frequently utilized multi-dimensional self-report measures may not always be appropriate. Instead, single item measures and physiological measures may be more suitable. In case of physiological measures, habituation effects can affect measurement and require further investigation. This work utilizes a low-fidelity driving simulator study (n = 30) to identify suitable short self-report and physiological measures for automated driving settings. Further, this work contributes by investigating habituation effects on the relationship between physiological measures and subjective well-being. Results indicate that the short measure with three items “happy”, “calm” and “awake” is a permissible alternative for cases where multidimensional self-report measures for passenger well-being cannot be used due to time or distraction constraints. Further, electrodermal activity and heart rate variability can be physiological proxies for passenger well-being. The data also provide indication of habituation effects caused by increased experience with automated driving on sympathetic activity that requires consideration in the selection of physiological measures for passenger well-being.

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Authors and Affiliations

  1. Institute of Industrial Engineering and Ergonomics, RWTH Aachen University, 52056, Aachen, Germany

    Vanessa Sauer, Alexander Mertens, Alexander Heyden, Stefan Groß & Verena Nitsch

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  1. Vanessa Sauer
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  2. Alexander Mertens
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  3. Alexander Heyden
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  5. Verena Nitsch
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Correspondence to Vanessa Sauer .

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

    Heidi Krömker

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Sauer, V., Mertens, A., Heyden, A., Groß, S., Nitsch, V. (2020). Measures for Well-Being in Highly Automated Vehicles: The Effect of Prior Experience. In: Krömker, H. (eds) HCI in Mobility, Transport, and Automotive Systems. Automated Driving and In-Vehicle Experience Design. HCII 2020. Lecture Notes in Computer Science(), vol 12212. Springer, Cham. https://doi.org/10.1007/978-3-030-50523-3_12

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  • DOI: https://doi.org/10.1007/978-3-030-50523-3_12

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