Papers › BiTraP: Bi-directional Pedestrian Trajectory Prediction with Multi-modal Goal Estimation
BiTraP: Bi-directional Pedestrian Trajectory Prediction with Multi-modal Goal Estimation
Yu Yao, Ella Atkins, Matthew Johnson-Roberson, Ram Vasudevan, Xiaoxiao Du
Pedestrian trajectory prediction is an essential task in robotic applications such as autonomous driving and robot navigation. State-of-the-art trajectory predictors use a conditional variational autoencoder (CVAE) with recurrent neural networks (RNNs) to encode observed trajectories and decode multi-modal future trajectories. This process can suffer from accumulated errors over long prediction horizons (>=2 seconds). This paper presents BiTraP, a goal-conditioned bi-directional multi-modal trajectory prediction method based on the CVAE. BiTraP estimates the goal (end-point) of trajectories and introduces a novel bi-directional decoder to improve longer-term trajectory prediction accuracy. Extensive experiments show that BiTraP generalizes to both first-person view (FPV) and bird's-eye view (BEV) scenarios and outperforms state-of-the-art results by ~10-50%. We also show that different choices of non-parametric versus parametric target models in the CVAE directly influence the predicted multi-modal trajectory distributions. These results provide guidance on trajectory predictor design for robotic applications such as collision avoidance and navigation systems.
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Code
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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 | JAAD | BiTrap-D | CF_MSE(1.5) | 4565 | #2 of 5 | Archive leaderboard | report |
| Trajectory Prediction | JAAD | BiTrap-D | C_MSE(1.5) | 1105 | #2 of 5 | Archive leaderboard | report |
| Trajectory Prediction | JAAD | BiTrap-D | MSE(0.5) | 93 | #2 of 5 | Archive leaderboard | report |
| Trajectory Prediction | JAAD | BiTrap-D | MSE(1.0) | 378 | #2 of 5 | Archive leaderboard | report |
| Trajectory Prediction | JAAD | BiTrap-D | MSE(1.5) | 1206 | #2 of 5 | Archive leaderboard | report |
| Trajectory Prediction | PIE | Bitrap-D | CF_MSE(1.5) | 1949 | #2 of 5 | Archive leaderboard | report |
| Trajectory Prediction | PIE | Bitrap-D | C_MSE(1.5) | 481 | #2 of 5 | Archive leaderboard | report |
| Trajectory Prediction | PIE | Bitrap-D | MSE(0.5) | 41 | #2 of 5 | Archive leaderboard | report |
| Trajectory Prediction | PIE | Bitrap-D | MSE(1.0) | 161 | #2 of 5 | Archive leaderboard | report |
| Trajectory Prediction | PIE | Bitrap-D | MSE(1.5) | 511 | #2 of 5 | 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.
Methods
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