  Forecasting the future behaviors of dynamic actors is an important task in
many robotics applications such as self-driving. It is extremely challenging as
actors have latent intentions and their trajectories are governed by complex
interactions between the other actors, themselves, and the maps. In this paper,
we propose LaneRCNN, a graph-centric motion forecasting model. Importantly,
relying on a specially designed graph encoder, we learn a local lane graph
representation per actor (LaneRoI) to encode its past motions and the local map
topology. We further develop an interaction module which permits efficient
message passing among local graph representations within a shared global lane
graph. Moreover, we parameterize the output trajectories based on lane graphs,
a more amenable prediction parameterization. Our LaneRCNN captures the
actor-to-actor and the actor-to-map relations in a distributed and map-aware
manner. We demonstrate the effectiveness of our approach on the large-scale
Argoverse Motion Forecasting Benchmark. We achieve the 1st place on the
leaderboard and significantly outperform previous best results.
