P4P: 自動運転の計画のための衝突考慮型運動予測
P4P: Conflict-Aware Motion Prediction for Planning in Autonomous Driving
既存の運動予測器が衝突検出に苦戦することを示し、物理ベースの軌道生成と学習ベースの関係予測を組み合わせて衝突を特定するP4Pを提案した。
著者: Qiao Sun, Xin Huang, Brian C. Williams, Hang Zhao
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
Motion prediction is crucial in enabling safe motion planning for autonomous vehicles in interactive scenarios. It allows the planner to identify potential conflicts with other traffic agents and generate safe plans. Existing motion predictors often focus on reducing prediction errors, yet it remains an open question on how well they help identify the conflicts for the planner. In this paper, we evaluate state-of-the-art predictors through novel conflict-related metrics, such as the success rate of identifying conflicts. Surprisingly, the predictors suffer from a low success rate and thus lead to a large percentage of collisions when we test the prediction-planning system in an interactive simulator. To fill the gap, we propose a simple but effective alternative that combines a physics-based trajectory generator and a learning-based relation predictor to identify conflicts and infer conflict relations. We demonstrate that our predictor, P4P, achieves superior performance over existing learning-based predictors in realistic interactive driving scenarios from Waymo Open Motion Dataset.
