Decoupled Heuristic Multi-Vehicle Emergency Trajectory Planning for Sudden Obstacles

Abstract

The emergence of sudden obstacles can significantly reduce the feasible space and may induce locally non-convex or fragmented space, especially in densely clustered scenarios, making vehicle trajectory planning remarkably challenging. Current methods face computational bottlenecks when generating emergency trajectories under such tight real-time constraints. To address this issue, we decouple safety-critical guidance from trajectory optimization for suddenly appearing obstacles. Specifically, a novel unified nonpositivity quantification method based on vector cross-product consistency is introduced to numerically constrain non-convex regions and a heuristic risk metric is designed to guide the optimization of avoidance target. Additionally, a dynamic priority strategy is further designed to adaptively adjust the constraint dimensionality in real time, improving the success rate of emergency planning. Comparative evaluations with existing emergency planning methods demonstrate the superiority of the proposed approach in terms of success rate, planning time, and emergency trajectory length. Finally, several real-world multi-vehicle experiments validate the effectiveness and practical applicability of the proposed method.