都市走行のための安全で効率的なタスク・モーション統合計画
Task-Motion Planning for Safe and Efficient Urban Driving
自動運転においてタスク計画とモーション計画が安全度を共有し合うことで、効率を最大化しつつ走行の安全性を確保するアルゴリズムTMPUDを提案し、都市走行シミュレーションで有効性を示した。
著者: Yan Ding, Xiaohan Zhang, Xingyue Zhan, Shiqi Zhang
分類: cs.RO
原文アブストラクト
Autonomous vehicles need to plan at the task level to compute a sequence of symbolic actions, such as merging left and turning right, to fulfill people's service requests, where efficiency is the main concern. At the same time, the vehicles must compute continuous trajectories to perform actions at the motion level, where safety is the most important. Task-motion planning in autonomous driving faces the problem of maximizing task-level efficiency while ensuring motion-level safety. To this end, we develop algorithm Task-Motion Planning for Urban Driving (TMPUD) that, for the first time, enables the task and motion planners to communicate about the safety level of driving behaviors. TMPUD has been evaluated using a realistic urban driving simulation platform. Results suggest that TMPUD performs significantly better than competitive baselines from the literature in efficiency, while ensuring the safety of driving behaviors.
