HGCN-GJS：用于轨迹预测的具有分组联合采样的分层图卷积网络
HGCN-GJS: Hierarchical Graph Convolutional Network with Groupwise Joint Sampling for Trajectory Prediction
AI总结：
提出分层图卷积网络HGCN-GJS，通过分组联合采样方案建模群体交互与未来轨迹的联合分布，在多个轨迹预测数据集上取得最先进结果。
摘要：
准确的行人轨迹预测对自动驾驶和移动机器人导航等下游任务非常重要。充分研究人群中的社会交互对准确的行人轨迹预测至关重要。然而，大多数现有方法未能很好地捕捉群体级别的交互，仅关注成对交互而忽略了群体交互。在这项工作中，我们提出了一种用于轨迹预测的分层图卷积网络HGCN-GJS，它很好地利用了人群中的群体级别交互。此外，我们引入了一种新颖的联合采样方案，用于建模未来轨迹中多个行人的联合分布。基于群体信息，该方案将一个人的轨迹与群体中其他人的轨迹关联起来，但保持群体外人员轨迹的独立性。我们在几个轨迹预测数据集上展示了我们网络的性能，在所有考虑的数据集上都取得了最先进的结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
