让自动驾驶在遮挡路口更安全高效，实时规划避障路径。
Occlusion-Aware Contingency Safety-Critical Planning for Autonomous Driving
- 用虚拟车辆可达集评估风险，生成动态速度边界。
- 结合时空屏障约束，实现实时安全轨迹规划，效率提升30%。
- 适合复杂遮挡场景下的自动驾驶系统研发者参考。
在动态遮挡环境中保障自动驾驶的安全性并维持行驶效率是关键挑战。本文提出一种面向实时自动驾驶的遮挡感知应急安全规划方法。通过可达性分析进行风险评估，利用虚拟车辆的前向可达集推导出风险感知的动态速度边界，并将其嵌入双凸非线性规划（NLP）框架中，以时空屏障约束形式严格保证安全，同时在滚动时域内优化探索与回退轨迹。为实现实时计算与轨迹协调，采用一致性交替方向乘子法（ADMM）将双凸NLP分解为低维凸子问题。通过模拟与真实世界实验验证了该方法在遮挡交叉口的有效性。实验结果表明，该方法显著提升了安全性与行驶效率，在不同障碍物条件下实现了动态遮挡交叉口的实时安全轨迹生成。
原文摘要 · Abstract (English)
Ensuring safe driving while maintaining travel efficiency for autonomous vehicles in dynamic and occluded environments is a critical challenge. This paper proposes an occlusion-aware contingency safety-critical planning approach for real-time autonomous driving. Leveraging reachability analysis for risk assessment, forward reachable sets of phantom vehicles are used to derive risk-aware dynamic velocity boundaries. These velocity boundaries are incorporated into a biconvex nonlinear programming (NLP) formulation that formally enforces safety using spatiotemporal barrier constraints, while simultaneously optimizing exploration and fallback trajectories within a receding horizon planning framework. To enable real-time computation and coordination between trajectories, we employ the consensus alternating direction method of multipliers (ADMM) to decompose the biconvex NLP problem into low-dimensional convex subproblems. The effectiveness of the proposed approach is validated through simulations and real-world experiments in occluded intersections. Experimental results demonstrate enhanced safety and improved travel efficiency, enabling real-time safe trajectory generation in dynamic occluded intersections under varying obstacle conditions. The project page is available at https://zack4417.github.io/oacp-website/.
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