A Case Study of Trust on Autonomous Driving
This work addresses trust dynamics in human-autonomous vehicle interaction to improve safety, but it is an incremental case study with limited scope.
The study investigated how different factors like automation alarms and weather conditions affect human trust in autonomous vehicles during simulated driving experiments with 19 participants, finding significant individual variability in trust changes and reaction times.
As autonomous vehicles have benefited the society, understanding the dynamic change of humans' trust during human-autonomous vehicle interaction can help to improve the safety and performance of autonomous driving. We designed and conducted a human subjects study involving 19 participants. Each participant was asked to enter their trust level in a Likert scale in real-time during experiments on a driving simulator. We also collected physiological data (e.g., heart rate, pupil size) of participants as complementary indicators of trust. We used analysis of variance (ANOVA) and Signal Temporal Logic (STL) to analyze the experimental data. Our results show the influence of different factors (e.g., automation alarms, weather conditions) on trust, and the individual variability in human reaction time and trust change.