Abstract: In urban driving scenarios, owing to the presence of multiple static obstacles such as parked cars and roadblocks, planning a collision-free and smooth path remains a challenging problem. In addition, the path-planning problem is mostly non-convex, and contains multiple local minima. Therefore, a method for combining a sampling-based method and an optimization-based method is proposed in this paper to generate a collision-free path with kinematic constraints for urban scenarios. The sampling-based method constructs a search graph to search for a seeding path for exploring a safe driving corridor, and the optimization-based method constructs a quadratic programming problem considering the desired state constraints, continuity constraints, driving corridor constraints, and kinematic constraints to perform path optimization. The experimental results show that the proposed method is able to plan a collision-free and smooth path in real time when managing typical urban scenarios.

Key words: autonomous vehicle, path planning, dynamic searching, quadratic programming