LaneGCN learns lane graph representations to forecast motion trajectories for autonomous vehicles. It employs a graph convolutional architecture to model the topology of road networks and predict future agent movements. The system achieved 1st place in the Argoverse Motion Forecasting Competition and is built using PyTorch.