BiFF: ポリライン座標系を用いた双方向未来融合によるインタラクティブ軌道予測
BiFF: Bi-level Future Fusion with Polyline-based Coordinate for Interactive Trajectory Prediction
自動運転における複数エージェントの将来軌道予測で、高レベル意図と低レベル行動を段階的に融合し、ポリライン座標系で効率的かつ頑健に予測する手法を提案。
著者: Yiyao Zhu, Di Luan, Shaojie Shen
分類: cs.RO, cs.CV
原文アブストラクト
Predicting future trajectories of surrounding agents is essential for safety-critical autonomous driving. Most existing work focuses on predicting marginal trajectories for each agent independently. However, it has rarely been explored in predicting joint trajectories for interactive agents. In this work, we propose Bi-level Future Fusion (BiFF) to explicitly capture future interactions between interactive agents. Concretely, BiFF fuses the high-level future intentions followed by low-level future behaviors. Then the polyline-based coordinate is specifically designed for multi-agent prediction to ensure data efficiency, frame robustness, and prediction accuracy. Experiments show that BiFF achieves state-of-the-art performance on the interactive prediction benchmark of Waymo Open Motion Dataset.
