  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.
