動的環境における自動運転のための効率的な速度計画：インタラクションポイントモデル
Efficient Speed Planning for Autonomous Driving in Dynamic Environment with Interaction Point Model
交通参加者との相互作用を保護時間と優先度で統一的に表現する学習ベースのインタラクションポイントモデルを提案し、それを組み込んだ速度計画フレームワークが動的環境での有効性と堅牢性を示す。
著者: Yingbing Chen, Ren Xin, Jie Cheng, Qingwen Zhang, Xiaodong Mei, Ming Liu, Lujia Wang
分類: cs.RO
原文アブストラクト
Safely interacting with other traffic participants is one of the core requirements for autonomous driving, especially in intersections and occlusions. Most existing approaches are designed for particular scenarios and require significant human labor in parameter tuning to be applied to different situations. To solve this problem, we first propose a learning-based Interaction Point Model (IPM), which describes the interaction between agents with the protection time and interaction priority in a unified manner. We further integrate the proposed IPM into a novel planning framework, demonstrating its effectiveness and robustness through comprehensive simulations in highly dynamic environments.
