Predicting the trajectories of surrounding agents is an essential ability for
autonomous vehicles navigating through complex traffic scenes. The future
trajectories of agents can be inferred using two important cues: the locations
and past motion of agents, and the static scene structure. Due to the high
variability in scene structure and agent configurations, prior work has
employed the attention mechanism, applied separately to the scene and agent
configuration to learn the most salient parts of both cues. However, the two
cues are tightly linked. The agent configuration can inform what part of the
scene is most relevant to prediction. The static scene in turn can help
determine the relative influence of agents on each other's motion. Moreover,
the distribution of future trajectories is multimodal, with modes corresponding
to the agent's intent. The agent's intent also informs what part of the scene
and agent configuration is relevant to prediction. We thus propose a novel
approach applying multi-head attention by considering a joint representation of
the static scene and surrounding agents. We use each attention head to generate
a distinct future trajectory to address multimodality of future trajectories.
Our model achieves state of the art results on the nuScenes prediction
benchmark and generates diverse future trajectories compliant with scene
structure and agent configuration.

利用多头注意力机制生成多模态未来轨迹预测，以最佳地利用静态场景和环境的信息，实现自动驾驶车辆的精准导航，该模型在 nuScenes 预测基准测试中取得了最佳结果。