  Pedestrian trajectory prediction is a key technology in autopilot, which
remains to be very challenging due to complex interactions between pedestrians.
However, previous works based on dense undirected interaction suffer from
modeling superfluous interactions and neglect of trajectory motion tendency,
and thus inevitably result in a considerable deviance from the reality. To cope
with these issues, we present a Sparse Graph Convolution Network~(SGCN) for
pedestrian trajectory prediction. Specifically, the SGCN explicitly models the
sparse directed interaction with a sparse directed spatial graph to capture
adaptive interaction pedestrians. Meanwhile, we use a sparse directed temporal
graph to model the motion tendency, thus to facilitate the prediction based on
the observed direction. Finally, parameters of a bi-Gaussian distribution for
trajectory prediction are estimated by fusing the above two sparse graphs. We
evaluate our proposed method on the ETH and UCY datasets, and the experimental
results show our method outperforms comparative state-of-the-art methods by 9%
in Average Displacement Error(ADE) and 13% in Final Displacement Error(FDE).
Notably, visualizations indicate that our method can capture adaptive
interactions between pedestrians and their effective motion tendencies.
