Driver Trust in Automated Driving Systems

Alexander Stocker

Research output: Contribution to conferencePaperpeer-review


Vehicle automation is a prominent example of safety-critical AI-based task automation. Recent digital innovations have led to the introduction of partial vehicle automation, which can already give vehicle drivers a sense of what fully automated driving would feel like. In the context of current imperfect vehicle automation, establishing an appropriate level of driver trust in automated driving systems (ADS) is seen as a key factor for their safe use and long-term acceptance. This paper thoroughly reviews and synthesizes the literature on driver trust in ADS, covering a wide range of academic disciplines. Pulling together knowledge on trustful user interaction with ADS, this paper offers a first classification of the main trust calibrators. Guided by this analysis, the paper identifies a lack of studies on adaptive, contextual trust calibration in contrast to numerous studies that focus on general trust calibration
Original languageEnglish
Publication statusPublished - 18 Jun 2022
Event30th European Conference on Information Systems : ECIS 2022 - Timisoara, Romania
Duration: 24 Jun 202228 Jun 2022


Conference30th European Conference on Information Systems
Abbreviated titleECIS 2022
Internet address

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