Toward Adaptive Driving Styles for Automated Driving with Users' Trust and Preferences

As autonomous vehicles (AVs) become ubiquitous, users' trust will be critical for the successful adoption of such systems. Prior works have shown that the driving styles of AVs can impact how users trust and rely on such systems. However, users' preferred driving style may vary with changes in trust or road conditions, experience, and personal driving preferences. We explore methods to adapt the driving style of an AV to match the preferred driving style of users to improve their trust in the vehicle. We conducted a pilot study ( $n=16$ ) on a simulated urban environment, where the users experience various static and adaptive driving styles for different pedestrian and traffic-related scenarios. Our results indicate that users best trust AVs that closely match their preferences ( $p< 0.05$ ). We believe that exploring the effects of AV driving style on users' trust and workload will provide necessary steps towards developing human-aware automated systems.