An Investigation of Drivers’ Dynamic Situational Trust in Conditionally Automated Driving

Abstract

Understanding how trust is built over time is essential, as trust plays an important role in the acceptance and adoption of automated vehicles (AVs). This study aims to investigate the effects of system performance and participants’ trust preconditions on dynamic situational trust during takeover transitions. We evaluate the dynamic situational trust of 42 participants using both self-reported and behavioral measures while watching 30 videos with takeover scenarios. The study is a 3 by 2 mixed-subjects design, where the within-subjects variable is the system performance (i.e., accuracy levels of 95%, 80%, and 70%) and the between-subjects variable is the preconditions of the participants’ trust (i.e., overtrust and undertrust). Our results showed that participants quickly adjusted their self-reported situational trust levels, which were consistent with different accuracy levels of system performance in both trust preconditions. However, participants’ behavioral situational trust was affected by their trust preconditions across different accuracy levels. For instance, the overtrust precondition significantly increased the agreement fraction compared to the undertrust precondition. The undertrust precondition significantly decreased the switch fraction compared to the overtrust precondition. These results have important implications for designing an invehicle trust calibration system for conditional AVs.