  Predicting the future behavior of moving agents is essential for real world
applications. It is challenging as the intent of the agent and the
corresponding behavior is unknown and intrinsically multimodal. Our key insight
is that for prediction within a moderate time horizon, the future modes can be
effectively captured by a set of target states. This leads to our target-driven
trajectory prediction (TNT) framework. TNT has three stages which are trained
end-to-end. It first predicts an agent's potential target states $T$ steps into
the future, by encoding its interactions with the environment and the other
agents. TNT then generates trajectory state sequences conditioned on targets. A
final stage estimates trajectory likelihoods and a final compact set of
trajectory predictions is selected. This is in contrast to previous work which
models agent intents as latent variables, and relies on test-time sampling to
generate diverse trajectories. We benchmark TNT on trajectory prediction of
vehicles and pedestrians, where we outperform state-of-the-art on Argoverse
Forecasting, INTERACTION, Stanford Drone and an in-house
Pedestrian-at-Intersection dataset.
