SAE 二级驾驶自动化的自适应驾驶风格：最小化偏好不匹配
Adaptive Driving Style for SAE Level-2 Driving Automation: Minimizing Preference Mismatch
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中文总结 AI 辅助
研究 SAE 二级驾驶自动化中驾驶风格与驾驶员偏好不匹配问题，提出自适应驾驶风格控制框架，通过驾驶模拟器研究比较不同启发式方法，训练预测模型用于隐式自适应策略，降低偏好不匹配并提高信任度，向开发人类意识驾驶自动化迈进。
中文摘要 AI 辅助
驾驶风格是影响自动车辆（AV）功能舒适性和接受度的关键因素。在 SAE 二级自动化中，驾驶员必须监督系统并随时准备干预，自动化驾驶风格与驾驶员偏好之间的不匹配会降低信任并引发接管。本文提出了一种自适应驾驶风格控制框架，以最小化这种偏好不匹配。在驾驶模拟器研究中，比较了固定、基于信任和基于偏好的自适应启发式方法，并分析了它们对偏好不匹配和信任的影响。然后训练了一个驾驶偏好预测模型，并将其用于隐式自适应策略，为即将到来的事件在有限的驾驶风格中进行选择。验证研究表明，预测策略实现的偏好不匹配等于或低于比较基线，特别是从防御性较低的风格开始时，同时还产生了更高的平均信任度。结果为开发能够根据驾驶员偏好隐式调整驾驶风格的具备人类意识的驾驶自动化迈出了一步。
英文摘要
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.
