Calibration of Trust in Autonomous Vehicle

Lee, Seul Chan; Ji, Yong Gu

doi:10.1007/978-3-031-10784-9_16

Cited by 3 publications

(2 citation statements)

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“…Loss of situational awareness and slow or inadequate human response in case of automation failures can often be interpreted as an excess of trust, or "overtrust" (also described as "complacency"; Muir, 1987;Parasuraman et al, 1993;Parasuraman and Riley, 1997;Lee and See, 2004;Inagaki and Itoh, 2013;Hoff and Bashir, 2015;Payre et al, 2016;Boubin et al, 2017;Flemisch et al, 2017;Noah et al, 2017;Lee et al, 2021;Lee and Ji, 2023). However, there are also situations in which users do not place enough trust in a reliable system (Muir, 1987;Parasuraman and Riley, 1997;Lee and See, 2004;Hoff and Bashir, 2015;Carsten and Martens, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…However, there are also situations in which users do not place enough trust in a reliable system (Muir, 1987;Parasuraman and Riley, 1997;Lee and See, 2004;Hoff and Bashir, 2015;Carsten and Martens, 2019). Some authors have called this "undertrust" (or "distrust") (Muir, 1987;Lee and See, 2004;Sheridan et al, 2005;Hoff and Bashir, 2015;Wintersberger et al, 2018;Lee and Ji, 2023).…”

Section: Introductionmentioning

confidence: 99%

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Trust in automated vehicles: constructs, psychological processes, and assessment

Walker,

Forster,

Hergeth

et al. 2023

Front. Psychol.

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There is a growing body of research on trust in driving automation systems. In this paper, we seek to clarify the way trust is conceptualized, calibrated and measured taking into account issues related to specific levels of driving automation. We find that: (1) experience plays a vital role in trust calibration; (2) experience should be measured not just in terms of distance traveled, but in terms of the range of situations encountered; (3) system malfunctions and recovery from such malfunctions is a fundamental part of this experience. We summarize our findings in a framework describing the dynamics of trust calibration. We observe that methods used to quantify trust often lack objectivity, reliability, and validity, and propose a set of recommendations for researchers seeking to select suitable trust measures for their studies. In conclusion, we argue that the safe deployment of current and future automated vehicles depends on drivers developing appropriate levels of trust. Given the potentially severe consequences of miscalibrated trust, it is essential that drivers incorporate the possibility of new and unexpected driving situations in their mental models of system capabilities. It is vitally important that we develop methods that contribute to this goal.

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