Optimal Vehicle Path Planning Using Quadratic Optimization for Baidu Apollo Open Platform
This addresses path planning for autonomous vehicles, specifically in complex urban scenarios, and appears incremental as it builds on existing optimization methods.
The paper tackles the problem of generating kinematically feasible and smooth collision-free paths for autonomous vehicles in cluttered urban environments, presenting a novel quadratic programming approach that achieves optimal paths with resolution-complete collision avoidance.
Path planning is a key component in motion planning for autonomous vehicles. A path specifies the geometrical shape that the vehicle will travel, thus, it is critical to safe and comfortable vehicle motions. For urban driving scenarios, autonomous vehicles need the ability to navigate in cluttered environment, e.g., roads partially blocked by a number of vehicles/obstacles on the sides. How to generate a kinematically feasible and smooth path, that can avoid collision in complex environment, makes path planning a challenging problem. In this paper, we present a novel quadratic programming approach that generates optimal paths with resolution-complete collision avoidance capability.