  In this work, we aim to predict the future motion of vehicles in a traffic
scene by explicitly modeling their pairwise interactions. Specifically, we
propose a graph neural network that jointly predicts the discrete interaction
modes and 5-second future trajectories for all agents in the scene. Our model
infers an interaction graph whose nodes are agents and whose edges capture the
long-term interaction intents among the agents. In order to train the model to
recognize known modes of interaction, we introduce an auto-labeling function to
generate ground truth interaction labels. Using a large-scale real-world
driving dataset, we demonstrate that jointly predicting the trajectories along
with the explicit interaction types leads to significantly lower trajectory
error than baseline methods. Finally, we show through simulation studies that
the learned interaction modes are semantically meaningful.
