速度場：軌道計画のための情報豊富な走行コスト表現
Velocity Field: An Informative Traveling Cost Representation for Trajectory Planning
自動運転の軌道計画向けに、人間の運転デモから学習した進行方向と速度の事前情報を与える局所地図表現「Velocity Field」を提案し、実データでラスタ化コストマップより高信頼・高効率であることを示した。
著者: Ren Xin, Jie Cheng, Sheng Wang, Ming Liu
分類: cs.RO
原文アブストラクト
Trajectory planning involves generating a series of space points to be followed in the near future. However, due to the complex and uncertain nature of the driving environment, it is impractical for autonomous vehicles~(AVs) to exhaustively design planning rules for optimizing future trajectories. To address this issue, we propose a local map representation method called Velocity Field. This approach provides heading and velocity priors for trajectory planning tasks, simplifying the planning process in complex urban driving. The heading and velocity priors can be learned from demonstrations of human drivers using our proposed loss. Additionally, we developed an iterative sampling-based planner to train and compare the differences between local map representations. We investigated local map representation forms for planning performance on a real-world dataset. Compared to learned rasterized cost maps, our method demonstrated greater reliability and computational efficiency.
