从共享控制干预中学习：面向个性化超车的上下文驱动加速度轮廓预测
Learning from Shared-Control Overrides: Context-Driven Acceleration Profile Prediction for Personalized Overtaking
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中文总结 AI 辅助
针对ACC在超车时与驾驶员期望不匹配的问题，提出CoP-ACC框架，通过聚类、分类和残差回归从干预中学习个性化加速度轮廓，减少干预并提升舒适性。
中文摘要 AI 辅助
自适应巡航控制（ACC）系统通常针对普通驾驶员进行校准，在高速公路超车等时间紧迫的机动过程中，往往会导致车辆行为与个人期望之间的不匹配。当ACC被认为过于保守且不一致时，驾驶员会通过油门干预进行介入，从而对系统行为提供隐式反馈。本文将此类干预行为重新定义为人在回路中的监督信号，并提出一种数据驱动的个性化车辆自适应框架，称为上下文驱动个性化ACC（CoP-ACC）。我们并未单纯依赖容易过度平滑动态响应的端到端回归，而是引入了一种混合流程，结合了：（i）无监督层次聚类，从干预事件中提取代表性加速度轮廓；（ii）上下文分类器，将机动前驾驶条件映射到相应的轮廓；（iii）残差回归器，将所选轮廓细化成适合当前上下文的平滑个性化加速度轮廓。在真实公共道路数据上，与保留的强制ACC基线相比，该方法展现出较高的重建保真度，并生成在潜在干预情境中趋向于驾驶员预期行为的加速度轮廓。研究结果凸显了从共享控制干预中学习以实现预期性、个性化ACC行为的潜力，从而减少人工干预并提升乘坐舒适性。
英文摘要
Adaptive Cruise Control (ACC) systems are typically calibrated for an average driver, often resulting in a mismatch between vehicle behavior and individual expectations during time-critical maneuvers such as highway overtaking. When the ACC is perceived as too conservative and inconsistent, drivers intervene through throttle overrides, providing implicit feedback on the system's behavior. This paper reframes these override actions as human-in-theloop supervisory signals and proposes a data-driven framework for personalized vehicle adaptation, termed Context-driven Personalized ACC (CoP-ACC). Rather than relying solely on end-to-end regression, which tends to over-smooth dynamic responses, we introduce a hybrid pipeline combining: (i) unsupervised hierarchical clustering to extract representative acceleration profiles from override events; (ii) a context classifier that maps pre-maneuver driving conditions to the appropriate profile; and (iii) a residual regressor that refines the selected profile into a smooth, personalized acceleration profile tailored to the immediate context. Evaluated on real-world public-road data against a withheld forced-ACC baseline, the approach demonstrates high reconstruction fidelity and generates acceleration profiles that tend toward the driver's expected behavior in potential override contexts. The results highlight the potential of learning from shared-control overrides to enable anticipatory, personalized ACC behavior, reducing manual interventions and improving ride comfort.
