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• W4324304891 abstract "Under the human-automation codriving future, dynamic trust should be considered. This paper explored how trust changes over time and how multiple factors (time, trust propensity, neuroticism, and takeover warning design) calibrate trust together. We launched two driving simulator experiments to measure drivers' trust before, during, and after the experiment under takeover scenarios. The results showed that trust in automation increased during short-term interactions and dropped after four months, which is still higher than pre-experiment trust. Initial trust and trust propensity had a stable impact on trust. Drivers trusted the system more with the two-stage (MR + TOR) warning design than the one-stage (TOR). Neuroticism had a significant effect on the countdown compared with the content warning.Practitioner summary: The results provide new data and knowledge for trust calibration in the takeover scenario. The findings can help design a more reasonable automated driving system in long-term human-automation interactions." @default.
• W4324304891 created "2023-03-16" @default.
• W4324304891 creator A5015116294 @default.
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• W4324304891 date "2023-03-29" @default.
• W4324304891 modified "2023-10-16" @default.
• W4324304891 title "Understanding trust calibration in automated driving: the effect of time, personality, and system warning design" @default.
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• W4324304891 doi "https://doi.org/10.1080/00140139.2023.2191907" @default.
• W4324304891 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36920361" @default.
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