  Predicting the future paths of an agent's neighbors accurately and in a
timely manner is central to the autonomous applications for collision
avoidance. Conventional approaches, e.g., LSTM-based models, take considerable
computational costs in the prediction, especially for the long sequence
prediction. To support more efficient and accurate trajectory predictions, we
propose a novel CNN-based spatial-temporal graph framework GraphTCN, which
models the spatial interactions as social graphs and captures the
spatio-temporal interactions with a modified temporal convolutional network. In
contrast to conventional models, both the spatial and temporal modeling of our
model are computed within each local time window. Therefore, it can be executed
in parallel for much higher efficiency, and meanwhile with accuracy comparable
to best-performing approaches. Experimental results confirm that our model
achieves better performance in terms of both efficiency and accuracy as
compared with state-of-the-art models on various trajectory prediction
benchmark datasets.
