Awesome
[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}
}