What's around the corner?: enhancing driver awareness in autonomous vehicles via in-vehicle spatial auditory displays

David Beattie, Lynne Baillie, Martin Halvey, Rod McCall

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    There is currently a distinct lack of design consideration associated with autonomous vehicles and their impact on human factors. Research has yet to consider fully the impact felt by the driver when he/she is no longer in control of the vehicle [12]. We propose that spatialised auditory feedback could be used to enhance driver awareness to the intended actions of autonomous vehicles. We hypothesise that this feedback will provide drivers with an enhanced sense of control. This paper presents a driving simulator study where 5 separate auditory feedback methods are compared during both autonomous and manual driving scenarios. We found that our spatialised auditory presentation method alerted drivers to the intended actions of autonomous vehicles much more than all other methods and they felt significantly more in control during scenarios containing sound vs. no sound. Finally, that overall workload in autonomous vehicle scenarios was lower compared to manual vehicle scenarios.
    Original languageEnglish
    Title of host publicationNodiCHI'14: Proceedings of the 8th Nordic Conference on Human-Computer Interaction: Fun, Fast, Foundational
    PublisherAssociation for Computing Machinery (ACM)
    Pages189-198
    Number of pages10
    ISBN (Print)9781450325424
    DOIs
    Publication statusPublished - 30 Oct 2014

    Keywords

    • human-centered computing
    • human computer interaction

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  • Cite this

    Beattie, D., Baillie, L., Halvey, M., & McCall, R. (2014). What's around the corner?: enhancing driver awareness in autonomous vehicles via in-vehicle spatial auditory displays. In NodiCHI'14: Proceedings of the 8th Nordic Conference on Human-Computer Interaction: Fun, Fast, Foundational (pp. 189-198). Association for Computing Machinery (ACM). https://doi.org/10.1145/2639189.2641206