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A Unified Environmental Network for Pedestrian Trajectory Prediction
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Proposes UEN, a CNN-based unified environmental network that encodes scene, motion, and polar (distance/direction) maps plus a local multimodal window to predict pedestrian trajectories in feature-map form.
Problem
Pedestrian trajectory prediction in complex scenes must model scene constraints, social interactions, and multimodal motion, but sequential models and prior CNN methods mishandle scene-trajectory modality mismatch and lack target-pedestrian spatial relations.
Approach
Represent scene, past motions, and a polar map (distance and direction from the target pedestrian to every location) as aligned feature maps processed by a CNN, and generate per-time-step multimodal positions via a local multimodal window inside a CVAE.
Key results
- On ETH/UCY long-term prediction, UEN reaches average mADE/mFDE 0.29/0.41 versus 0.32/0.51 for Y-net.
- On SDD long-term, UEN achieves 42.8/54.6 mADE/mFDE versus 47.9/66.7 for Y-net.
- Versus latest models LED, SICNet, and MSRL on short-term prediction, UEN gets 14% mADE and 21% mFDE relative improvements on the averages reported.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Multimodal Modeling instead of goal-guided prediction (ablation) | UEN · ETH+UCY long-term | mADE: 0.50 -> 0.46 (ETH); 0.37 -> 0.35 (univ); 0.29 -> 0.28 (zara1); 0.22 -> 0.20 (zara2) | Helped |
| Use Multimodal Modeling instead of goal-guided prediction (ablation) | UEN · inD and SDD long-term | mADE: 14.3 -> 13.9 (inD); 43.2 -> 42.8 (SDD) | Helped |
| Add Environmental Modeling (ablation) | UEN · inD and SDD long-term | mADE: 14.9 -> 13.9 (inD); 47.1 -> 42.8 (SDD) | Helped |
| Add Environmental Modeling (ablation) | UEN · ETH+UCY long-term | mADE: 0.48 -> 0.46 (ETH); 0.17 -> 0.15 (hotel); 0.37 -> 0.35 (univ); 0.29 -> 0.28 (zara1); 0.21 -> 0.20 (zara2) | Mixed |
| Use UEN rather than Y-net | UEN · SDD short-term | mADE: 7.9 -> 7.3 | Helped |
| Use UEN rather than Y-net | UEN · inD long-term | mFDE: 21.1 -> 15.2 | Helped |
Surprising findings
- UEN is slightly worse than BiTraP on short-term UCY prediction in mADE, and ties Y-net there.
Limitations
- Short-term UCY results are not best; multimodal modeling gives little gain when motions are unimodal.
- Relies on a pretrained semantic segmentation model for scene maps, so scene quality depends on it.
- Latest-model comparisons on ETH/UCY and SDD use numbers cited from the original papers rather than re-runs.
Experimental setup
Datasets: ETH UCY inD SDD
Benchmarks: ETH UCY inD SDD
Metrics: mADE mFDE
Baselines: TPNSTA AgentFormer VDRGCN Trajectron++ BiTraP PECNet Y-net LED SICNet MSRL
Builds on: Y-net PECNet CVAE U-Net
Closest papers by meaning
- Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction (CVPR 2020 · similarity 0.91)
- A Set of Control Points Conditioned Pedestrian Trajectory Prediction (AAAI 2023 · similarity 0.90)
- TrajCLIP: Pedestrian trajectory prediction method using contrastive learning and idempotent networks (NeurIPS 2024 · similarity 0.87)
- Learning to Generate Diverse Pedestrian Movements from Web Videos with Noisy Labels (ICLR 2025 · similarity 0.87)
- Towards Predicting Any Human Trajectory In Context (NeurIPS 2025 · similarity 0.86)
- Social-Transmotion: Promptable Human Trajectory Prediction (ICLR 2024 · similarity 0.86)
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