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Predicting motion of surrounding agents is critical to real-worldapplications of tactical path planning for autonomous driving. Due to thecomplex temporal dependencies and social interactions of agents, on-linetrajectory prediction is a challenging task. With the development of attentionmechanism in recent years, transformer model has been applied in naturallanguage sequence processing first and then image processing. In this paper, wepresent a Spatial-Channel Transformer Network for trajectory prediction withattention functions. Instead of RNN models, we employ transformer model tocapture the spatial-temporal features of agents. A channel-wise module isinserted to measure the social interaction between agents. We find that theSpatial-Channel Transformer Network achieves promising results on real-worldtrajectory prediction datasets on the traffic scenes.

Spatial-Channel Transformer Network for Trajectory Prediction on the Traffic Scenes