  We propose a motion forecasting model that exploits a novel structured map
representation as well as actor-map interactions. Instead of encoding
vectorized maps as raster images, we construct a lane graph from raw map data
to explicitly preserve the map structure. To capture the complex topology and
long range dependencies of the lane graph, we propose LaneGCN which extends
graph convolutions with multiple adjacency matrices and along-lane dilation. To
capture the complex interactions between actors and maps, we exploit a fusion
network consisting of four types of interactions, actor-to-lane, lane-to-lane,
lane-to-actor and actor-to-actor. Powered by LaneGCN and actor-map
interactions, our model is able to predict accurate and realistic multi-modal
trajectories. Our approach significantly outperforms the state-of-the-art on
the large scale Argoverse motion forecasting benchmark.
