In order to predict a pedestrian's trajectory in a crowd accurately, one has
to take into account her/his underlying socio-temporal interactions with other
pedestrians consistently. Unlike existing work that represents the relevant
information separately, partially, or implicitly, we propose a complete
representation for it to be fully and explicitly captured and analyzed. In
particular, we introduce a Directed Acyclic Graph-based structure, which we
term Socio-Temporal Graph (STG), to explicitly capture pair-wise socio-temporal
interactions among a group of people across both space and time. Our model is
built on a time-varying generative process, whose latent variables determine
the structure of the STGs. We design an attention-based model named STGformer
that affords an end-to-end pipeline to learn the structure of the STGs for
trajectory prediction. Our solution achieves overall state-of-the-art
prediction accuracy in two large-scale benchmark datasets. Our analysis shows
that a person's past trajectory is critical for predicting another person's
future path. Our model learns this relationship with a strong notion of
socio-temporal localities. Statistics show that utilizing this information
explicitly for prediction yields a noticeable performance gain with respect to
the trajectory-only approaches.

为了准确预测人群中行人的轨迹，需一直考虑其与其他行人的社会 - 时间相互作用。我们提出了一种完整的、明确的捕捉并分析该信息的表示方法，即基于有向无环图的社会 - 时间图（STG）。利用 STGformer 模型，我们实现了端到端的流程，学习 STG 的结构用于轨迹预测，并在两个大规模基准数据集上取得了最先进的预测精度。统计数据表明，利用这种信息明确进行预测相对于仅使用轨迹的方法能够明显提高性能。