Advanced Driver Assistance Systems (ADAS) are increasingly important in improving driving safety and comfort, with Adaptive Cruise Control (ACC) being one of the most widely used. However, pre-defined ACC settings may not always align with driver's preferences and habits, leading to discomfort and potential safety issues. Personalized ACC (P-ACC) has been proposed to address this problem, but most existing research uses historical driving data to imitate behaviors that conform to driver preferences, neglecting real-time driver feedback. To bridge this gap, we propose a cloud-vehicle collaborative P-ACC framework that incorporates driver feedback adaptation in real time. The framework is divided into offline and online parts. The offline component records the driver's naturalistic car-following trajectory and uses inverse reinforcement learning (IRL) to train the model on the cloud. In the online component, driver feedback is used to update the driving gap preference in real time. The model is then retrained on the cloud with driver's takeover trajectories, achieving incremental learning to better match driver's preference. Human-in-the-loop (HuiL) simulation experiments demonstrate that our proposed method significantly reduces driver intervention in automatic control systems by up to 62.8%. By incorporating real-time driver feedback, our approach enhances the comfort and safety of P-ACC, providing a personalized and adaptable driving experience.

翻译：高级驾驶辅助系统（ADAS）在提升驾驶安全性与舒适性方面日益重要，其中自适应巡航控制（ACC）是应用最广泛的技术之一。然而，预设的ACC参数未必始终符合驾驶员的偏好与习惯，可能导致不适感及潜在安全隐患。针对该问题，个性化自适应巡航控制（P-ACC）被提出，但现有研究大多基于历史驾驶数据模仿符合驾驶员偏好的行为，忽略了实时驾驶反馈。为弥补这一不足，我们提出了一种车云协同的P-ACC框架，可实时融合驾驶员反馈进行自适应调整。该框架分为离线与在线两部分：离线组件记录驾驶员自然跟车轨迹，并利用逆向强化学习（IRL）在云端训练模型；在线组件则通过驾驶员反馈实时更新驾驶间隙偏好，随后基于驾驶员接管轨迹在云端重新训练模型，实现增量学习以更精准匹配驾驶员偏好。人在环（HuiL）仿真实验表明，所提方法能将自动控制系统中驾驶员干预次数降低高达62.8%。通过集成实时驾驶反馈，该方法显著增强了P-ACC的舒适性与安全性，提供了个性化、可自适应的驾驶体验。