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.

翻译：准确的行人轨迹预测对于自动驾驶和移动机器人导航等下游任务具有重要意义。充分研究人群中的社交交互是实现精确轨迹预测的关键。然而，现有方法大多未能有效捕捉群体层面的交互，仅关注成对交互而忽略群体间交互。本文提出一种层级图卷积网络HGCN-GJS，通过充分利用人群中的群体层面交互实现轨迹预测。此外，我们引入一种新颖的联合采样方案，用于建模多个行人未来轨迹的联合分布。该方案基于群体信息，将行人的轨迹与同组其他行人的轨迹相关联，同时保持组外行人轨迹的独立性。我们在多个轨迹预测数据集上验证了网络性能，在全部数据集上均取得了最先进的结果。