Identifying Preferred Driving Styles of Automated Vehicles Through Drivers’ Feedback

Abstract

Personalized driving styles (DS) in automated vehicles (AVs) can improve drivers’ trust and acceptance. However, little is known about how drivers identify and communicate their preferred DSs to AVs. Also, most studies have focused on normal driving situations, without considering risky or off-nominal scenarios. This study examined how drivers find their preferred DS by allowing participants to adjust the AV’s driving behavior. A study was conducted whereby drivers experienced one of four predefined DSs, categorized by their level of conservativeness in terms of safety and speed in navigating road obstacles. Participants completed 10 driving trials where they provided feedback to an AV regarding whether to change or maintain its DS. Drivers preferred to maintain the same DS from previous trials and a DS that was neither too aggressive nor too conservative. Findings can help inform the development of adaptive algorithms that preemptively adjust DS without drivers’ explicit inputs.