Visible to the public Impacts of System Transparency and System Failure on Driver Trust During Partially Automated Driving

TitleImpacts of System Transparency and System Failure on Driver Trust During Partially Automated Driving
Publication TypeConference Paper
Year of Publication2020
AuthorsLee, J., Abe, G., Sato, K., Itoh, M.
Conference Name2020 IEEE International Conference on Human-Machine Systems (ICHMS)
KeywordsAnalysis of variance, Automated vehicle, Automation, automobiles, driver information systems, driver intervention, driver trust, flawless automated driving, Human Behavior, human trust, Lead, partially automated driving, partially automated vehicle, pubcrawl, road safety, road vehicles, supervisory control, system failure, system failure influence, system transparency, system-limit, system-malfunction led trust reduction, Task Analysis, trust change, Trust in automation, trust ratings
AbstractThe objective of this study is to explore changes of trust by a situation where drivers need to intervene. Trust in automation is a key determinant for appropriate interaction between drivers and the system. System transparency and types of system failure influence shaping trust in a supervisory control. Subjective ratings of trust were collected to examine the impact of two factors: system transparency (Detailed vs. Less) and system failure (by Limits vs. Malfunction) in a driving simulator study in which drivers experienced a partially automated vehicle. We examined trust ratings at three points: before and after driver intervention in the automated vehicle, and after subsequent experience of flawless automated driving. Our result found that system transparency did not have significant impacts on trust change from before to after the intervention. System-malfunction led trust reduction compared to those of before the intervention, whilst system-limits did not influence trust. The subsequent experience recovered decreased trust, in addition, when the system-limit occurred to drivers who have detailed information about the system, trust prompted in spite of the intervention. The present finding has implications for automation design to achieve the appropriate level of trust.
Citation Keylee_impacts_2020