Driver Responses to Mismatches Between Expected and Experienced Automated Vehicle Driving Styles

Automated vehicles (AV) can exhibit diverse driving behaviors that deviate from driver expectations. More research is needed to understand drivers’ preferred driving styles when navigating safety-critical situations as well as their responses to unexpected AV driving behaviors. Forty-eight participants experienced simulated automated driving scenarios where an AV navigated a construction zone while exhibiting four different predefined driving styles that varied in terms of cautiousness and efficiency. After each trial, participants provided feedback to adjust the AV’s driving style toward their preferences, which was successfully implemented in 80% cases. Drivers’ trust, workload, voluntary takeover decisions, eye movements, and skin conductance were measured. Overall, participants preferred driving styles balancing cautiousness and efficiency. When AV behavior matched driver preferences, they reported higher trust and more stable visual scanning patterns, while unexpected behaviors were associated with broader ocular scanning. Findings can inform the development of adaptive systems that intelligently modify AV behaviors.