  Predicting the behaviors of other agents on the road is critical for
autonomous driving to ensure safety and efficiency. However, the challenging
part is how to represent the social interactions between agents and output
different possible trajectories with interpretability. In this paper, we
introduce a neural prediction framework based on the Transformer structure to
model the relationship among the interacting agents and extract the attention
of the target agent on the map waypoints. Specifically, we organize the
interacting agents into a graph and utilize the multi-head attention
Transformer encoder to extract the relations between them. To address the
multi-modality of motion prediction, we propose a multi-modal attention
Transformer encoder, which modifies the multi-head attention mechanism to
multi-modal attention, and each predicted trajectory is conditioned on an
independent attention mode. The proposed model is validated on the Argoverse
motion forecasting dataset and shows state-of-the-art prediction accuracy while
maintaining a small model size and a simple training process. We also
demonstrate that the multi-modal attention module can automatically identify
different modes of the target agent's attention on the map, which improves the
interpretability of the model.
