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.

本文提出了一种双级未来融合 (BiFF) 方法，以显式捕捉交互代理之间的未来互动，适用于自动驾驶的多代理预测任务，实验证明 BiFF 在 Waymo Open Motion 数据集的交互预测基准上实现了最先进的性能。

BiFF: 基于折线坐标的双层未来融合交互轨迹预测

BiFF: Bi-level Future Fusion with Polyline-based Coordinate for  Interactive Trajectory Prediction

The task of motion forecasting is critical for self-driving vehicles (SDVs)
to be able to plan a safe maneuver. Towards this goal, modern approaches reason
about the map, the agents' past trajectories and their interactions in order to
produce accurate forecasts. The predominant approach has been to encode the map
and other agents in the reference frame of each target agent. However, this
approach is computationally expensive for multi-agent prediction as inference
needs to be run for each agent. To tackle the scaling challenge, the solution
thus far has been to encode all agents and the map in a shared coordinate frame
(e.g., the SDV frame). However, this is sample inefficient and vulnerable to
domain shift (e.g., when the SDV visits uncommon states). In contrast, in this
paper, we propose an efficient shared encoding for all agents and the map
without sacrificing accuracy or generalization. Towards this goal, we leverage
pair-wise relative positional encodings to represent geometric relationships
between the agents and the map elements in a heterogeneous spatial graph. This
parameterization allows us to be invariant to scene viewpoint, and save online
computation by re-using map embeddings computed offline. Our decoder is also
viewpoint agnostic, predicting agent goals on the lane graph to enable diverse
and context-aware multimodal prediction. We demonstrate the effectiveness of
our approach on the urban Argoverse 2 benchmark as well as a novel highway
dataset.

本文提出了一种有效的多个自主行驶目标预测方法，该方法使用空间图形表示推理中各种几何关系，并节省在线计算通过离线计算得到的地图嵌入。