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Open AccessArticle

Tell Them How They Did: Feedback on Operator Performance Helps Calibrate Perceived Ease of Use in Automated Driving

1
Bayerische Motoren Werke (BMW) Group, Knorrstr. 147, 80937 Munich, Germany
2
Department of Psychology, Chemnitz University of Technology, 09111 Chemnitz, Germany
*
Author to whom correspondence should be addressed.
Multimodal Technologies Interact. 2019, 3(2), 29; https://doi.org/10.3390/mti3020029
Received: 4 March 2019 / Revised: 24 April 2019 / Accepted: 25 April 2019 / Published: 29 April 2019
The development of automated driving will profit from an agreed-upon methodology to evaluate human–machine interfaces. The present study examines the role of feedback on interaction performance provided directly to participants when interacting with driving automation (i.e., perceived ease of use). In addition, the development of ratings itself over time and use case specificity were examined. In a driving simulator study, N = 55 participants completed several transitions between Society of Automotive Engineers (SAE) level 0, level 2, and level 3 automated driving. One half of the participants received feedback on their interaction performance immediately after each use case, while the other half did not. As expected, the results revealed that participants judged the interactions to become easier over time. However, a use case specificity was present, as transitions to L0 did not show effects over time. The role of feedback also depended on the respective use case. We observed more conservative evaluations when feedback was provided than when it was not. The present study supports the application of perceived ease of use as a diagnostic measure in interaction with automated driving. Evaluations of interfaces can benefit from supporting feedback to obtain more conservative results. View Full-Text
Keywords: automated driving; human–machine interface; diagnostic measures; ease of use; method development automated driving; human–machine interface; diagnostic measures; ease of use; method development
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MDPI and ACS Style

Forster, Y.; Hergeth, S.; Naujoks, F.; Krems, J.; Keinath, A. Tell Them How They Did: Feedback on Operator Performance Helps Calibrate Perceived Ease of Use in Automated Driving. Multimodal Technologies Interact. 2019, 3, 29.

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