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

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

2 Dec 2020ICCV 2021 10arXiv:2012.01526archive 2025-07-28

Karttikeya Mangalam, Yang An, Harshayu Girase, Jitendra Malik

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.

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Code

HarshayuGirase/PECNet mentioned on GitHubpytorch report
harshayugirase/human-path-prediction mentioned on GitHubpytorch report

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Tasks

PredictionTrajectory ForecastingTrajectory Prediction

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Trajectory Prediction ETH/UCY Y-Net ADE-8/12 0.18 #2 of 20 Archive leaderboard report
Trajectory Prediction ETH/UCY Y-Net FDE-8/12 0.27 #2 of 20 Archive leaderboard report
Trajectory Prediction Stanford Drone Y-Net ADE-8/12 @K = 20 7.85 #5 of 24 Archive leaderboard report
Trajectory Prediction Stanford Drone Y-Net FDE-8/12 @K= 20 11.85 #5 of 24 Archive leaderboard report

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