This study investigates how users’ trust evolves during their first ride in a fully driverless robotaxi and how it can be affected by user characteristics, system design, and traffic scenarios.
Background
As driving automation technology matures, driverless robotaxis have become available. Despite its immense economic and social potential, public acceptance can be strongly influenced by user trust. Previous research on trust in autonomous vehicles often relied on surveys, driving simulators, or “Wizard of Oz” methods, potentially introducing biases.
Method
An on-road experiment was conducted in commercially operating fully driverless robotaxis on public urban roads. In total, 30 participants with no prior experience riding fully driverless robotaxis were recruited, comprising nondrivers (n = 10), and drivers with (n = 10) and without (n = 10) driving automation experience. Dynamic trust was collected at a 2-min interval during the ride, along with participants’ think-aloud for changes in trust. A cumulative link mixed model was used to assess the impact of past driving experience, demographics, and riding time on trust development.
Results
Our findings revealed that dynamic trust increased gradually and stabilized over time, with user heterogeneity playing a moderating role in this process. Further think-aloud data analysis identified key factors in trust formation, including driving style, riding safety and comfort, and user interface design.
Conclusion
Trust in driverless robotaxis builds progressively with real-world exposure, shaped by user characteristics, vehicle control, and interface design.
Application
Our findings underscore the importance of considering user heterogeneity in fostering trust and acceptance of robotaxis.
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