Awesome

s2tnet

Paper

This is the official implementation of the paper: S2TNet: Spatio-Temporal Transformer Networks for Trajectory Prediction in Autonomous Driving (ACML 2021).

Quick Start

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

1) Install Packages

 pip install -r requirements.txt

2) Dataset

We use Apollo Scape Trajectory dataset

Performance

Results on Apollo Scape:

<table class="tg"> <thead> <tr> <th class="tg-c3ow">WSADE</th> <th class="tg-c3ow">ADEv</th> <th class="tg-c3ow">ADEp</th> <th class="tg-c3ow">ADEb</th> <th class="tg-c3ow">WSFDE</th> <th class="tg-c3ow">FDEv</th> <th class="tg-c3ow">FDEp</th> <th class="tg-c3ow">FDEb</th> </tr> </thead> <tbody> <tr> <td class="tg-c3ow">1.1679</td> <td class="tg-c3ow">1.9874</td> <td class="tg-c3ow">0.6834</td> <td class="tg-c3ow">1.7000</td> <td class="tg-c3ow">2.1798</td> <td class="tg-c3ow">3.5783</td> <td class="tg-c3ow">1.3048</td> <td class="tg-c3ow">3.2151</td> </tr> </tbody> </table>

S2TNet

Training & Evaluation

You can train our model by below command:

python3 main.py --config ./config/apolloscape/train.yaml

Testing & Uploading to Leaderboard

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.

Citation

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}
}