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Evaluating feedback requirements for trust calibration in automated vehicles

dc.contributor.authorWintersberger, Philipp
dc.contributor.authorJanotta, Frederica
dc.contributor.authorPeintner, Jakob
dc.contributor.authorLöcken, Andreas
dc.contributor.authorRiener, Andreas
dc.date.accessioned2021-06-16T13:31:53Z
dc.date.available2021-06-16T13:31:53Z
dc.date.issued2021
dc.description.abstractThe inappropriate use of automation as a result of trust issues is a major barrier for a broad market penetration of automated vehicles. Studies so far have shown that providing information about the vehicle’s actions and intentions can be used to calibrate trust and promote user acceptance. However, how such feedback could be designed optimally is still an open question. This article presents the results of two user studies. In the first study, we investigated subjective trust and user experience of (N=21) participants driving in a fully automated vehicle, which interacts with other traffic participants in virtual reality. The analysis of questionnaires and semi-structured interviews shows that participants request feedback about the vehicle’s status and intentions and prefer visual feedback over other modalities. Consequently, we conducted a second study to derive concrete requirements for future feedback systems. We showed (N=56) participants various videos of an automated vehicle from the ego perspective and asked them to select elements in the environment they want feedback about so that they would feel safe, trust the vehicle, and understand its actions. The results confirm a correlation between subjective user trust and feedback needs and highlight essential requirements for automatic feedback generation. The results of both experiments provide a scientific basis for designing more adaptive and personalized in-vehicle interfaces for automated driving.en
dc.identifier.doi10.1515/itit-2020-0024
dc.identifier.pissn2196-7032
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/36537
dc.language.isoen
dc.publisherDe Gruyter
dc.relation.ispartofit - Information Technology: Vol. 63, No. 2
dc.subjectAutomated Driving
dc.subjectTrust in Automation
dc.subjectUser Acceptance
dc.subjectUser Studies
dc.subjectMixed/Augmented Reality
dc.subjectAdaptive UIs
dc.subjectPersonalized UIs
dc.titleEvaluating feedback requirements for trust calibration in automated vehiclesen
dc.typeText/Journal Article
gi.citation.endPage122
gi.citation.publisherPlaceBerlin
gi.citation.startPage111

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