Baidu Apollo向け二次最適化による最適車両経路計画
Optimal Vehicle Path Planning Using Quadratic Optimization for Baidu Apollo Open Platform
都市環境での自動運転向けに、衝突回避能力を備えた滑らかで実行可能な経路を生成する二次計画法ベースの経路計画手法を提案した論文。
著者: Yajia Zhang, Hongyi Sun, Jinyun Zhou, Jiacheng Pan, Jiangtao Hu, Jinghao Miao
分類: cs.RO
原文アブストラクト
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.
