1 research outputs found
HGCN-GJS: Hierarchical Graph Convolutional Network with Groupwise Joint Sampling for Trajectory Prediction
Accurate pedestrian trajectory prediction is of great importance for
downstream tasks such as autonomous driving and mobile robot navigation. Fully
investigating the social interactions within the crowd is crucial for accurate
pedestrian trajectory prediction. However, most existing methods do not capture
group level interactions well, focusing only on pairwise interactions and
neglecting group-wise interactions. In this work, we propose a hierarchical
graph convolutional network, HGCN-GJS, for trajectory prediction which well
leverages group level interactions within the crowd. Furthermore, we introduce
a novel joint sampling scheme for modeling the joint distribution of multiple
pedestrians in the future trajectories. Based on the group information, this
scheme associates the trajectory of one person with the trajectory of other
people in the group, but maintains the independence of the trajectories of
outsiders. We demonstrate the performance of our network on several trajectory
prediction datasets, achieving state-of-the-art results on all datasets
considered.Comment: 8 pages, 5 figures, in submission to conferenc