Presenting system uncertainty in automotive UIs for supporting trust calibration in autonomous driving

By Tove Helldin, Göran Falkman, Maria Riveiro, Staffan Davidsson. 2013

289 citations recorded by the source corpus.

Subfields: Automotive Engineering, Safety, Risk, Reliability and Quality, Social Psychology

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In Social Psychology

Citations in the corpus

Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI (2020, background): “…have found that particular non-probabilistic representations of uncertainty or conﬁdence can lead to improved trust calibration in speciﬁc settings, 79,80 but further work is needed to understand the best way to represent different kinds of uncertainty under different circum-stances and how best…”
Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making (2020, background): “While the concept of trust calibration has been studied for automation [13, 18, 20, 26], as to prevent both automation aversion and automation bias, it is not well understood in the context of AI systems.”
Towards Autonomous Cars: The Effect of Autonomy Levels on Acceptance and User Experience (2014, background): “Helldin et al. [12] suggest that visualizing the uncertainty of a car during an automated driving scenario helps drivers to regain control over the vehicle faster.”
Understanding Blind People's Experiences with Computer-Generated Captions of Social Media Images (2017, background): “In fact, there is even research on how to encourage people to have more trust in these intelligent systems (e.g., [1,2,15,30]).”
Towards a Theory of Longitudinal Trust Calibration in Human–Robot Teams (2019, methodology): “…of the operator [38] \ Convey uncertainty directly Conveying uncertainty directly by providing facial cues [8] or machine conﬁdence indicators [36,57,62,86,97,157] \ Provide verification methods Offering methods for detailed veriﬁcation of the system’s behavior and performance to reduce the…”
Measurement of Trust in Automation: A Narrative Review and Reference Guide (2021, background): “High trust results in longer reaction times because over-reliance on the automation causes the operator to be unprepared to respond, requiring greater mental effort and duration to switch tasks (Beller et al., 2013; Helldin et al., 2013; Körber et al., 2018).”