  Predicting motion of surrounding agents is critical to real-world
applications of tactical path planning for autonomous driving. Due to the
complex temporal dependencies and social interactions of agents, on-line
trajectory prediction is a challenging task. With the development of attention
mechanism in recent years, transformer model has been applied in natural
language sequence processing first and then image processing. In this paper, we
present a Spatial-Channel Transformer Network for trajectory prediction with
attention functions. Instead of RNN models, we employ transformer model to
capture the spatial-temporal features of agents. A channel-wise module is
inserted to measure the social interaction between agents. We find that the
Spatial-Channel Transformer Network achieves promising results on real-world
trajectory prediction datasets on the traffic scenes.
