论文标题
HGCN-GJS：分层图卷积网络，具有轨迹预测的群集关节采样
HGCN-GJS: Hierarchical Graph Convolutional Network with Groupwise Joint Sampling for Trajectory Prediction
论文作者
论文摘要
对于自动驾驶和移动机器人导航等下游任务，准确的行人轨迹预测非常重要。充分研究人群中的社交互动对于准确的行人轨迹预测至关重要。但是，大多数现有方法不能很好地捕获组级别的交互，仅关注成对的交互和忽略小组交互。在这项工作中，我们提出了一个层次图卷积网络HGCN-GJS，用于轨迹预测，该预测很好地利用了人群中的组级别交互。此外，我们引入了一种新型的关节抽样方案，用于在未来的轨迹中对多个行人的联合分布进行建模。根据小组信息，该方案将一个人的轨迹与小组中其他人的轨迹相关联，但保持了局外人轨迹的独立性。我们证明了网络在几个轨迹预测数据集上的性能，从而在所有考虑的数据集中实现了最新的结果。
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.
