遮蔽環境に対応した自律走行のためのコンティンジェンシー安全臨界プランニング
Occlusion-Aware Contingency Safety-Critical Planning for Autonomous Driving
遮蔽された動的環境で自律走行車の安全性と走行効率を両立するため、到達可能性解析と時空間バリア制約を用いたリアルタイム軌道生成手法を提案し、実機実験で有効性を示した。
著者: Lei Zheng, Rui Yang, Minzhe Zheng, Zengqi Peng, Michael Yu Wang, Jun Ma
分類: cs.RO
原文アブストラクト
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/.
