  Human trajectory forecasting is an inherently multi-modal problem.
Uncertainty in future trajectories stems from two sources: (a) sources that are
known to the agent but unknown to the model, such as long term goals and
(b)sources that are unknown to both the agent & the model, such as intent of
other agents & irreducible randomness indecisions. We propose to factorize this
uncertainty into its epistemic & aleatoric sources. We model the epistemic
un-certainty through multimodality in long term goals and the aleatoric
uncertainty through multimodality in waypoints& paths. To exemplify this
dichotomy, we also propose a novel long term trajectory forecasting setting,
with prediction horizons upto a minute, an order of magnitude longer than prior
works. Finally, we presentY-net, a scene com-pliant trajectory forecasting
network that exploits the pro-posed epistemic & aleatoric structure for diverse
trajectory predictions across long prediction horizons.Y-net significantly
improves previous state-of-the-art performance on both (a) The well studied
short prediction horizon settings on the Stanford Drone & ETH/UCY datasets and
(b) The proposed long prediction horizon setting on the re-purposed Stanford
Drone & Intersection Drone datasets.
