From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting

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 and the model, such as the intent of other agents and irreducible randomness in decisions. We propose to factorize this uncertainty into its epistemic and aleatoric sources. We model the epistemic uncertainty through multimodality in long term goals and the aleatoric uncertainty through multimodality in way-points and paths. To exemplify this dichotomy, we also propose a novel long term trajectory forecasting setting, with prediction horizons up to a minute, up to an order of magnitude longer than prior works. Finally, we present Y-net, a scene compliant trajectory forecasting network that exploits the proposed epistemic and aleatoric structure for diverse trajectory predictions across long prediction horizons. Y-net significantly improves previous state-of-the-art performance on both (a) The short prediction horizon setting on the Stanford Drone (31.7% in FDE) and ETH/UCY datasets (7.4% in FDE) and (b) The proposed long horizon setting on the re-purposed Stanford Drone and Intersection Drone datasets.