Understanding complex social interactions among agents is a key challenge for
trajectory prediction. Most existing methods consider the interactions between
pairwise traffic agents or in a local area, while the nature of interactions is
unlimited, involving an uncertain number of agents and non-local areas
simultaneously. Besides, they treat heterogeneous traffic agents the same,
namely those among agents of different categories, while neglecting people's
diverse reaction patterns toward traffic agents in ifferent categories. To
address these problems, we propose a simple yet effective Unlimited
Neighborhood Interaction Network (UNIN), which predicts trajectories of
heterogeneous agents in multiple categories. Specifically, the proposed
unlimited neighborhood interaction module generates the fused-features of all
agents involved in an interaction simultaneously, which is adaptive to any
number of agents and any range of interaction area. Meanwhile, a hierarchical
graph attention module is proposed to obtain category-to-category interaction
and agent-to-agent interaction. Finally, parameters of a Gaussian Mixture Model
are estimated for generating the future trajectories. Extensive experimental
results on benchmark datasets demonstrate a significant performance improvement
of our method over the state-of-the-art methods.

本研究提出了一种基于无限邻域交互网络的异构多类别交通态势预测方法，通过适应各种交互要素生成混合特征，并通过高层级图注意力模块实现类别间和代理间的交互，从而提高了预测性能。