  This paper proposes a novel deep learning framework for multi-modal motion
prediction. The framework consists of three parts: recurrent neural networks to
process the target agent's motion process, convolutional neural networks to
process the rasterized environment representation, and a distance-based
attention mechanism to process the interactions among different agents. We
validate the proposed framework on a large-scale real-world driving dataset,
Waymo open motion dataset, and compare its performance against other methods on
the standard testing benchmark. The qualitative results manifest that the
predicted trajectories given by our model are accurate, diverse, and in
accordance with the road structure. The quantitative results on the standard
benchmark reveal that our model outperforms other baseline methods in terms of
prediction accuracy and other evaluation metrics. The proposed framework is the
second-place winner of the 2021 Waymo open dataset motion prediction challenge.
