Adaptive Driving Style for SAE Level-2 Driving Automation: Minimizing Preference Mismatch
For developers of automated driving systems, this work addresses the problem of driver discomfort and distrust due to style mismatch, offering a step toward human-aware adaptation.
This paper proposes an adaptive driving-style control framework for SAE Level-2 automation to minimize mismatch between the automation's driving style and driver preferences. In validation, the predictive policy achieved equal or lower preference mismatch and higher average trust compared to baselines.
Driving style is a key factor in the comfort and acceptance of automated vehicle (AV) features. In SAE Level-2 automation, where the driver must supervise the system and remain ready to intervene, mismatches between the automation's driving style and the driver's preference can reduce trust and trigger takeovers. This paper proposes an adaptive driving-style control framework that minimizes such preference mismatch. In a driving-simulator study, we compare fixed, trust-based, and preference-based adaptation heuristics and analyze their effects on preference mismatch and trust. We then train a driving-preference prediction model and use it in an implicit adaptation policy that selects among bounded driving styles for upcoming events. A validation study shows that the predictive policy achieves equal or lower preference mismatch than comparison baselines, particularly when starting from a less defensive style, while also yielding higher average trust. The results provide a step toward developing human-aware driving automation that can implicitly adapt its driving style to the driver's preferences.