Human-machine shared control in critical collision scenarios aims to aid drivers' accident avoidance through intervening only when necessary. Existing methods count on replanning collision-free trajectories and imposing human-machine tracking, which usually interrupts the driver's intent and increases the risk of conflict. Additionally, the lack of guaranteed trajectory feasibility under extreme conditions can compromise safety and reliability. This paper introduces a Reachability-Aware Reinforcement Learning framework for shared control, guided by Hamilton-Jacobi (HJ) reachability analysis. Machine intervention is activated only when the vehicle approaches the Collision Avoidance Reachable Set (CARS), which represents states where collision is unavoidable. First, we precompute the reachability distributions and the CARS by solving the Bellman equation using offline data. To reduce human-machine conflicts, we develop a driver model for sudden obstacles and propose an authority allocation strategy considering key collision avoidance features. Finally, we train a reinforcement learning agent to reduce human-machine conflicts while enforcing the hard constraint of avoiding entry into the CARS. The proposed method was tested on a real vehicle platform. Results show that the controller intervenes effectively near CARS to prevent collisions while maintaining improved original driving task performance. Robustness analysis further supports its flexibility across different driver attributes.

翻译：在关键碰撞场景中，人机共享控制旨在通过仅在必要时介入来辅助驾驶员规避事故。现有方法依赖于重新规划无碰撞轨迹并强制人机跟踪，这通常会干扰驾驶员意图并增加冲突风险。此外，极端条件下缺乏有保障的轨迹可行性可能损害安全性与可靠性。本文提出一种基于汉密尔顿-雅可比可达性分析指导的可达性感知强化学习框架用于共享控制。仅当车辆接近碰撞规避可达集时激活机器介入，该集合表征碰撞不可避免的状态集合。首先，我们利用离线数据求解贝尔曼方程，预计算可达性分布与碰撞规避可达集。为减少人机冲突，我们建立了针对突发障碍的驾驶员模型，并提出考虑关键碰撞规避特征的权限分配策略。最后，我们训练强化学习智能体以在严格执行避免进入碰撞规避可达集的硬约束条件下减少人机冲突。所提方法在实车平台上进行了验证。结果表明，控制器能在接近碰撞规避可达集时有效介入以防止碰撞，同时保持改进的原始驾驶任务性能。鲁棒性分析进一步证明了该方法在不同驾驶员特性下的适应性。