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Learning Lane Graph Representations for Motion Forecasting

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12347)

Abstract

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.

Keywords

HD map Motion forecasting Autonomous driving 

Supplementary material

504434_1_En_32_MOESM1_ESM.zip (1.4 mb)
Supplementary material 1 (zip 1385 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Uber ATGPittsburghUSA
  2. 2.University of TorontoTorontoCanada

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