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Function-Specific Uncertainty Communication in Automated Driving

Function-Specific Uncertainty Communication in Automated Driving

Alexander Kunze (Loughborough University, Loughborough, UK), Stephen J. Summerskill (Loughborough University, Loughborough, UK), Russell Marshall (Loughborough University, Loughborough, UK), and Ashleigh J. Filtness (Loughborough University, Loughborough, UK)
Copyright: © 2019 |Pages: 23
DOI: 10.4018/IJMHCI.2019040105
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Abstract

Conveying the overall uncertainties of automated driving systems was shown to improve trust calibration and situation awareness, resulting in safer takeovers. However, the impact of presenting the uncertainties of multiple system functions has yet to be investigated. Further, existing research lacks recommendations for visualizing uncertainties in a driving context. The first study outlined in this publication investigated the implications of conveying function-specific uncertainties. The results of the driving simulator study indicate that the effects on takeover performance depends on driving experience, with less experienced drivers benefitting most. Interview responses revealed that workload increments are a major inhibitor of these benefits. Based on these findings, the second study explored the suitability of 11 visual variables for an augmented reality-based uncertainty display. The results show that particularly hue and animation-based variables are appropriate for conveying uncertainty changes. The findings inform the design of all displays that show content varying in urgency.
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Background

Sufficient levels of SA are needed for the successful execution of takeovers. SA may be described as a non-linear, three-step process consisting of (1) the perception of elements in the environment, (2) an understanding of their implications for the ego vehicle, and (3) a projection of their future states (Endsley, 1995). In a driving context, SA can primarily be gained by glancing towards the field relevant for driving (FRD), essentially the driving scene. Thus, automation interfaces can aid the build-up of SA by managing the gaze behavior of human operators. Considering that operators’ trust in automation and their monitoring frequency to the FRD are inversely related (Hergeth, Lorenz, Vilimek, & Krems, 2016), the monitoring behavior, and thereby SA, can be affected through trust calibration.

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