Emergency collision avoidance under extreme driving conditions demands safety-critical control that accounts for both obstacle proximity and vehicle dynamic stability over a future time horizon, yet existing methods often rely on instantaneous or local safety evaluations. This paper proposes a safe reinforcement learning framework guided by a Hamilton-Jacobi (HJ) reachability based motion safety set that provides forward-looking safety supervision for constrained policy optimization. Specifically, a unified signed safety function is formulated by combining geometric collision margins and chassis stability limits, and is then extended through reachability analysis into a finite-horizon motion safety set that characterizes whether safety can be maintained under future vehicle state evolution. To enable practical computation, the motion safety set is approximated from offline extreme driving data, mitigating the computational burden of grid-based HJ solvers. The learned motion safety set is then embedded as a continuous safety cost into a constrained Markov decision process, and a PID-Lagrangian policy optimization scheme is employed to adaptively regulate the Lagrange multiplier for safety constraint enforcement. Simulation and real-vehicle experiments on low-adhesion obstacle-avoidance scenarios demonstrate that the proposed method achieves higher goal-reaching rates, produces smoother avoidance maneuvers, and maintains larger unified safety margins than baseline methods.

翻译：极端驾驶条件下的应急碰撞规避要求安全关键控制能够同时考虑未来时间范围内的障碍物接近性与车辆动态稳定性，然而现有方法往往依赖瞬时或局部的安全性评估。本文提出一种由Hamilton-Jacobi（HJ）可达性运动安全集引导的安全强化学习框架，该安全集为约束策略优化提供前瞻性安全监督。具体而言，通过结合几何碰撞裕度与底盘稳定性极限构建统一符号安全函数，并借助可达性分析将其扩展为有限时域运动安全集，以刻画在未来车辆状态演化下能否维持安全性。为便于实际计算，基于离线极端驾驶数据对运动安全集进行近似，从而缓解基于网格的HJ求解器的计算负担。随后将学习得到的运动安全集作为连续安全代价嵌入约束马尔可夫决策过程，并采用PID-拉格朗日策略优化方案自适应调节拉格朗日乘子以强制执行安全约束。在低附着系数避障场景中的仿真与实车实验表明：与基线方法相比，所提方法具有更高的目标到达率，能生成更平滑的避障操作，并保持更大的统一安全裕度。