Attention-aware Social Graph Transformer Networks for Stochastic
  Trajectory Prediction
Yao Liu Binghao Li Xianzhi Wang Claude Sammut Lina Yao
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.