  While the modeling of pair-wise relations has been widely studied in
multi-agent interacting systems, its ability to capture higher-level and
larger-scale group-wise activities is limited. In this paper, we propose a
group-aware relational reasoning approach (named EvolveHypergraph) with
explicit inference of the underlying dynamically evolving relational
structures, and we demonstrate its effectiveness for multi-agent trajectory
prediction. In addition to the edges between a pair of nodes (i.e., agents), we
propose to infer hyperedges that adaptively connect multiple nodes to enable
group-aware relational reasoning in an unsupervised manner without fixing the
number of hyperedges. The proposed approach infers the dynamically evolving
relation graphs and hypergraphs over time to capture the evolution of
relations, which are used by the trajectory predictor to obtain future states.
Moreover, we propose to regularize the smoothness of the relation evolution and
the sparsity of the inferred graphs or hypergraphs, which effectively improves
training stability and enhances the explainability of inferred relations. The
proposed approach is validated on both synthetic crowd simulations and multiple
real-world benchmark datasets. Our approach infers explainable, reasonable
group-aware relations and achieves state-of-the-art performance in long-term
prediction.
