Papers › It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction
It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction
Karttikeya Mangalam, Harshayu Girase, Shreyas Agarwal, Kuan-Hui Lee, Ehsan Adeli, Jitendra Malik, Adrien Gaidon
Human trajectory forecasting with multiple socially interacting agents is of critical importance for autonomous navigation in human environments, e.g., for self-driving cars and social robots. In this work, we present Predicted Endpoint Conditioned Network (PECNet) for flexible human trajectory prediction. PECNet infers distant trajectory endpoints to assist in long-range multi-modal trajectory prediction. A novel non-local social pooling layer enables PECNet to infer diverse yet socially compliant trajectories. Additionally, we present a simple "truncation-trick" for improving few-shot multi-modal trajectory prediction performance. We show that PECNet improves state-of-the-art performance on the Stanford Drone trajectory prediction benchmark by ~20.9% and on the ETH/UCY benchmark by ~40.8%. Project homepage: https://karttikeya.github.io/publication/htf/
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Trajectory Prediction | ETH/UCY | PECNet | ADE-8/12 | 0.29 | #11 of 20 | Archive leaderboard | report |
| Trajectory Prediction | ETH/UCY | PECNet | FDE-8/12 | 0.48 | #11 of 20 | Archive leaderboard | report |
| Trajectory Prediction | Stanford Drone | PECNet | ADE-8/12 @K = 20 | 9.96 | #9 of 24 | Archive leaderboard | report |
| Trajectory Prediction | Stanford Drone | PECNet | FDE-8/12 @K= 20 | 15.88 | #9 of 24 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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