- This is the official implementation of the paper: S2TNet: Spatio-Temporal Transformer Networks for Trajectory Prediction in Autonomous Driving (ACML 2021).
Requires:
- adamod==0.0.3
- ConfigArgParse==1.5.2
- numpy==1.19.0
- PyYAML==6.0
- scipy==1.7.1
- tensorboardX==2.5.1
- torch==1.9.0
- tqdm==4.31.1
pip install -r requirements.txtWe use Apollo Scape Trajectory dataset
Results on Apollo Scape:
| WSADE | ADEv | ADEp | ADEb | WSFDE | FDEv | FDEp | FDEb |
|---|---|---|---|---|---|---|---|
| 1.1679 | 1.9874 | 0.6834 | 1.7000 | 2.1798 | 3.5783 | 1.3048 | 3.2151 |
You can train our model by below command:
python3 main.py --config ./config/apolloscape/train.yaml
You can test our model by below command:
python3 main.py --config ./config/apolloscape/test.yaml
The result file, named as prediction_result.zip, is generated after testing phase. Then, you can directly upload the file to (http://apolloscape.auto/trajectory.html) to obtain the official results.
If you find our work useful for your research, please consider citing the paper:
@inproceedings{pmlr-v157-chen21a,
title = {S2TNet: Spatio-Temporal Transformer Networks for Trajectory Prediction in Autonomous Driving},
author = {Chen, Weihuang and Wang, Fangfang and Sun, Hongbin},
booktitle = {Proceedings of The 13th Asian Conference on Machine Learning},
pages = {454--469},
year = {2021},
volume = {157},
month = {17--19 Nov}
}
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