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.

翻译：预测周围智能体的未来轨迹对于安全关键的自动驾驶至关重要。现有工作大多侧重于独立预测每个智能体的边缘轨迹，但较少探索交互式智能体的联合轨迹预测。本文提出双层未来融合方法（BiFF），显式捕捉交互智能体之间的未来交互。具体而言，BiFF 融合高层未来意图与低层未来行为，并专门设计基于折线的坐标系统用于多智能体预测，以确保数据效率、帧鲁棒性和预测精度。实验表明，BiFF 在 Waymo 开放运动数据集的交互式预测基准上达到了最先进的性能。