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攸关的自动驾驶至关重要。现有工作大多聚焦于独立预测每个智能体的边缘轨迹，然而针对交互智能体联合轨迹预测的研究尚不多见。本文提出双层未来融合（BiFF）方法，以显式捕获交互智能体间的未来交互行为。具体而言，BiFF首先融合高层未来意图，继而融合低层未来行为。此外，我们专门设计了基于折线的坐标表达方式用于多智能体预测，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预测基准上取得了当前最优性能。