Trajectory prediction is fundamental to various intelligent technologies,
such as autonomous driving and robotics. The motion prediction of pedestrians
and vehicles helps emergency braking, reduces collisions, and improves traffic
safety. Current trajectory prediction research faces problems of complex social
interactions, high dynamics and multi-modality. Especially, it still has
limitations in long-time prediction. We propose Attention-aware Social Graph
Transformer Networks for multi-modal trajectory prediction. We combine Graph
Convolutional Networks and Transformer Networks by generating stable resolution
pseudo-images from Spatio-temporal graphs through a designed stacking and
interception method. Furthermore, we design the attention-aware module to
handle social interaction information in scenarios involving mixed
pedestrian-vehicle traffic. Thus, we maintain the advantages of the Graph and
Transformer, i.e., the ability to aggregate information over an arbitrary
number of neighbors and the ability to perform complex time-dependent data
processing. We conduct experiments on datasets involving pedestrian, vehicle,
and mixed trajectories, respectively. Our results demonstrate that our model
minimizes displacement errors across various metrics and significantly reduces
the likelihood of collisions. It is worth noting that our model effectively
reduces the final displacement error, illustrating the ability of our model to
predict for a long time.

提出了一种关注社交图转换网络的多模态轨迹预测方法，它结合了图卷积网络和 Transformer 网络，并通过生成稳定分辨率的伪图像来处理空时图，还设计了关注社交交互信息的模块。实验证明该模型在多个度量指标上最大限度地降低了位移误差，并显著减少碰撞的可能性，同时具备长时间预测的能力。