Streaming Real-Time Trajectory Prediction Using Endpoint-Aware Modeling
Alexander Prutsch, David Schinagl, Horst Possegger Graz University of Technology
WACV 2026 Oral
conda create -n seam python=3.11.10
conda activate seam
We tested our implementation with torch 2.1.1 and CUDA 12.1.
Install PyTorch e.g.
conda install pytorch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 pytorch-cuda=12.1 -c pytorch -c nvidia
pip install -r ./requirements.txt
Download Download Argoverse 2 Motion Forecasting Dataset
The expected structure of the AV2 data should be:
data_root
├── train
│ ├── 0000b0f9-99f9-4a1f-a231-5be9e4c523f7
│ ├── 0000b6ab-e100-4f6b-aee8-b520b57c0530
│ ├── ...
├── val
│ ├── 00010486-9a07-48ae-b493-cf4545855937
│ ├── 00062a32-8d6d-4449-9948-6fedac67bfcd
│ ├── ...
├── test
│ ├── 0000b329-f890-4c2b-93f2-7e2413d4ca5b
│ ├── 0008c251-e9b0-4708-b762-b15cb6effc27
│ ├── ...
Preprocess the Argoverse 2 dataset by executing
python preprocess.py --data_root=/path/to/data_root -p
Train SEAM model using
python train.py datamodule.pl_module.data_root=/path/to/data_root/seam_processed/
Checkpoint provided in the repository.
Evaluate SEAM model using
python eval.py datamodule.pl_module.data_root=/path/to/data_root/seam_processed/ checkpoint=checkpoints/seam.ckpt
Expected results:
| MR | minADE1 | minADE6 | minFDE1 | minFDE6 | b-minFDE6 |
|---|---|---|---|---|---|
| 0.153 | 1.598 | 0.663 | 3.962 | 1.249 | 1.848 |
Code included, checkpoint and instructions coming soon.
Visualize the prediction results using
python visualize.py
Please update the data_root, chkpt_dir, and av2_raw_data_dir variable in the script.
@inproceedings{prutsch2026streaming,
title={{Streaming Real-Time Trajectory Prediction Using Endpoint-Aware Modeling}},
author={Prutsch, Alexander and Schinagl, David and Possegger, Horst},
booktitle={In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
year={2026}
}This repository is based on RealMotion and integrates code from Forecast-MAE and EMP. We thank them for their work!
