LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting

by   Wenyuan Zeng, et al.

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.


Learning Lane Graph Representations for Motion Forecasting

We propose a motion forecasting model that exploits a novel structured m...

Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting

In this paper, we address the important problem in self-driving of forec...

Path-Aware Graph Attention for HD Maps in Motion Prediction

The success of motion prediction for autonomous driving relies on integr...

What-If Motion Prediction for Autonomous Driving

Forecasting the long-term future motion of road actors is a core challen...

GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation

In this paper, we propose GOHOME, a method leveraging graph representati...

DAGMapper: Learning to Map by Discovering Lane Topology

One of the fundamental challenges to scale self-driving is being able to...

Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data

In this paper, we tackle the problem of relational behavior forecasting ...