To accurately predict trajectories in multi-agent settings, e.g. team games,
it is important to effectively model the interactions among agents. Whereas a
number of methods have been developed for this purpose, existing methods
implicitly model these interactions as part of the deep net architecture.
However, in the real world, interactions often exist at multiple levels, e.g.
individuals may form groups, where interactions among groups and those among
the individuals in the same group often follow significantly different
patterns. In this paper, we present a novel formulation for multi-agent
trajectory prediction, which explicitly introduces the concept of interactive
group consensus via an interactive hierarchical latent space. This formulation
allows group-level and individual-level interactions to be captured jointly,
thus substantially improving the capability of modeling complex dynamics. On
two multi-agent settings, i.e. team sports and pedestrians, the proposed
framework consistently achieves superior performance compared to existing
methods.

该论文提出了一个新的多智能体轨迹预测模型，它通过交互式分层潜空间明确引入组内共识的概念，可以更好地捕捉群体层面和个体层面的互动，从而在两个多智能体场景中均实现了优异的性能表现。