提出防路霸自适应巡航，能主动保护行车权并适应不同驾驶风格。
Anti-bullying Adaptive Cruise Control: A proactive right-of-way protection approach
- 基于逆最优控制识别驾驶风格，用博弈论建模对方反应。
- 可减少79.8%安全隐患，舒适度提升20.4%，交通效率增19.33%。
- 实时性好（<50毫秒），适合实际道路部署，支持灵活策略。
自适应巡航控制系统近年来广泛商用，但对近距离突然切入（类似“路霸”行为）仍显脆弱。为此，本文提出抗路霸自适应巡航（AACC）方法，可主动保护本车路权。该方法首先采用在线逆最优控制（IOC）算法识别驾驶员个体风格；随后基于斯塔克尔伯格博弈理论，构建运动规划框架，利用识别出的驾驶风格建模切入车辆的反应函数。通过将这些反应函数融入本车运动规划，本车可预判对方所有可能反应，从而选择最优路权保护策略。据我们所知，这是首个建模车辆交互动态并开发自适应交互式规划器的研究。仿真结果表明，该方法可有效防止“路霸”切入，且对不同切入车辆驾驶风格具有适应性。相比传统方法，安全性和舒适度分别提升79.8%和20.4%，交通流效率提高19.33%。该方法还可实现更灵活的驾驶策略，并满足实时性要求（计算时间低于50毫秒），支持现场部署。
原文摘要 · Abstract (English)
Adaptive Cruise Control (ACC) systems have been widely commercialized in recent years. However, existing ACC systems remain vulnerable to close-range cut-ins, a behavior that resembles "road bullying". To address this issue, this research proposes an Anti-bullying Adaptive Cruise Control (AACC) approach, which is capable of proactively protecting right-of-way against such "road bullying" cut-ins. To handle diverse "road bullying" cut-in scenarios smoothly, the proposed approach first leverages an online Inverse Optimal Control (IOC) based algorithm for individual driving style identification. Then, based on Stackelberg competition, a game-theoretic-based motion planning framework is presented in which the identified individual driving styles are utilized to formulate cut-in vehicles' reaction functions. By integrating such reaction functions into the ego vehicle's motion planning, the ego vehicle could consider cut-in vehicles' all possible reactions to find its optimal right-of-way protection maneuver. To the best of our knowledge, this research is the first to model vehicles' interaction dynamics and develop an interactive planner that adapts cut-in vehicle's various driving styles. Simulation results show that the proposed approach can prevent "road bullying" cut-ins and be adaptive to different cut-in vehicles' driving styles. It can improve safety and comfort by up to 79.8% and 20.4%. The driving efficiency has benefits by up to 19.33% in traffic flow. The proposed approach can also adopt more flexible driving strategies. Furthermore, the proposed approach can support real-time field implementation by ensuring less than 50 milliseconds computation time.
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