Optimal Vehicle Path Planning Using Quadratic Optimization for Baidu Apollo Open Platform
Yajia Zhang, Hongyi Sun, Jinyun Zhou, Jiacheng Pan, Jiangtao Hu, Jinghao Miao
Abstract
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.