DyHGDAT: Dynamic Hypergraph Dual Attention Network for multi-agent trajectory prediction

Modeling the interactions among agents based on their historical trajectories is key to precise multi-agent trajectory prediction. Hypergraph Convolutional Networks (HGCN) have become a proper choice for capturing high-order interactions among agents in this field. However, most existing works only consider static hypergraphs, and ignore that in a hypergraph, the power of influence varies between vertices (or hyperedges). Therefore, we propose DyHGDAT, a dynamic hypergraph dual attention network to capture the high-order interactions among agents, which not only models the evolution of hypergraph over time but also highlights the vertices and hyperedges with larger impacts. We apply DyHGDAT to a CVAE-based prediction system for predicting plausible trajectories. To validate the effectiveness of prediction, we evaluate our proposed method on two well-established trajectory prediction datasets: the ETH/UCY datasets and the Stanford Drone Dataset (SDD). The experimental results show that with DyHGDAT, the CVAE-based prediction system out-performs state-of-the-art methods by 12.5%/5.3% in ADE/FDE on ETH/UCY, and the improvement on SDD is 6.4%/7.4%.