  Autonomous driving vehicles (ADVs) hold great hopes to solve traffic
congestion problems and reduce the number of traffic accidents. Accurate
trajectories prediction of other traffic agents around ADVs is of key
importance to achieve safe and efficient driving. Pedestrians, particularly,
are more challenging to forecast due to their complex social in-teractions and
randomly moving patterns. We propose a Residual Graph Convolutional Neural
Network (Res-GCNN), which models the interactive behaviors of pedes-trians by
using the adjacent matrix of the constructed graph for the current scene.
Though the proposed Res-GCNN is quite lightweight with only about 6.4 kilo
parameters which outperforms all other methods in terms of parameters size, our
experimental results show an improvement over the state of art by 13.3% on the
Final Displacement Error (FDE) which reaches 0.65 meter. As for the Average
Dis-placement Error (ADE), we achieve a suboptimal result (the value is 0.37
meter), which is also very competitive. The Res-GCNN is evaluated in the
platform with an NVIDIA GeForce RTX1080Ti GPU, and its mean inference time of
the whole dataset is only about 2.2 microseconds. Compared with other methods,
the proposed method shows strong potential for onboard application accounting
for forecasting accuracy and time efficiency. The code will be made publicly
available on GitHub.
