[CVPR 2024] Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-specific Token Memory

Please follow below steps to run our code

Installation

Create virtual environment in Anaconda with env.yml

conda env create --file env.yaml -n t4p
conda activate t4p

Install T4P (This repo)

git clone https://github.com/daeheepark/T4P
cd T4P
mkdir outputs && mkdir datasets
pip install -r requirements.txt

Install a customized version of trajdata

git clone https://github.com/daeheepark/trajdata-t4p unified-av-data-loader

• Download raw datasets and follow the installation step of the above repo.
• The datasets should be located like this:

├── conf
├── ...
├── datasets
│   ├── nuScenes
│   ├── waymo
│   ├── interaction_single
│   ├── interaction_multi
│   └── lyft
├── train_test.py
└── train_ttt.py

Preprocess datasets

TBD

Train ForecastMAE

TBD

Test-time train and online evaluation

TBD

Acknowledgements

This repo is mostly built on top of ForecastMAE and trajdata. Thanks for their great works.

Citation

If you found this repository useful, please consider citing our work:

@article{park2024t4p,
  title={T4P: Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-specific Token Memory},
  author={Park, Daehee and Jeong, Jaeseok and Yoon, Sung-Hoon and Jeong, Jaewoo and Yoon, Kuk-Jin},
  journal={arXiv preprint arXiv:2403.10052},
  year={2024}
}