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

Res-GCNN: A Lightweight Residual Graph Convolutional Neural Networks for Human Trajectory Forecasting