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Motion prediction has been an essential component of autonomous drivingsystems since it handles highly uncertain and complex scenarios involvingmoving agents of different types. In this paper, we propose a Multi-GranularTRansformer (MGTR) framework, an encoder-decoder network that exploits contextfeatures in different granularities for different kinds of traffic agents. Tofurther enhance MGTR's capabilities, we leverage LiDAR point cloud data byincorporating LiDAR semantic features from an off-the-shelf LiDAR featureextractor. We evaluate MGTR on Waymo Open Dataset motion prediction benchmarkand show that the proposed method achieved state-of-the-art performance,ranking 1st on its leaderboard(https://waymo.com/open/challenges/2023/motion-prediction/).

MGTR: Multi-Granular Transformer for Motion Prediction with LiDAR