  We propose a motion forecasting model called BANet, which means
Boundary-Aware Network, and it is a variant of LaneGCN. We believe that it is
not enough to use only the lane centerline as input to obtain the embedding
features of the vector map nodes. The lane centerline can only provide the
topology of the lanes, and other elements of the vector map also contain rich
information. For example, the lane boundary can provide traffic rule constraint
information such as whether it is possible to change lanes which is very
important. Therefore, we achieved better performance by encoding more vector
map elements in the motion forecasting model.We report our results on the 2022
Argoverse2 Motion Forecasting challenge and rank 1st on the test leaderboard.

Computer Vision and Pattern Recognition;Robotics