  Motion prediction has been an essential component of autonomous driving
systems since it handles highly uncertain and complex scenarios involving
moving agents of different types. In this paper, we propose a Multi-Granular
TRansformer (MGTR) framework, an encoder-decoder network that exploits context
features in different granularities for different kinds of traffic agents. To
further enhance MGTR's capabilities, we leverage LiDAR point cloud data by
incorporating LiDAR semantic features from an off-the-shelf LiDAR feature
extractor. We evaluate MGTR on Waymo Open Dataset motion prediction benchmark
and show that the proposed method achieved state-of-the-art performance,
ranking 1st on its leaderboard
(https://waymo.com/open/challenges/2023/motion-prediction/).
