Adaptive Driving Style for SAE Level-2 Driving Automation: Minimizing Preference Mismatch

2026-07-23

AI Summary

This study addresses the issue of declining driver trust and frequent takeovers in SAE Level-2 automated driving systems caused by mismatches between the system’s driving style and the driver’s preferences. To this end, the authors propose an adaptive driving style control framework that implicitly learns driver preferences and dynamically selects the most suitable driving style from a predefined set for upcoming driving events, enabling personalization without requiring explicit user input. Validation through driving simulator experiments—comparing the proposed approach against fixed-style, trust-based, and preference-based baselines—demonstrates that when initialized with a relatively aggressive driving style, the proposed method significantly reduces preference mismatch and enhances average trust levels, outperforming existing baseline strategies.

Abstract

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.

Problem

• driving style
• preference mismatch
• Level-2 automation
• trust
• adaptive automation

Innovation

• adaptive driving style
• preference prediction
• Level-2 automation
• human-aware automation
• driving simulator study