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VUT-Centered environmental Dynamics Inference

This repo is the implementation of the following paper:

Towards Interactive Autonomous Vehicle Testing: Vehicle-Under-Test-Centered Traffic Simulation
Yiru Liu, Xiaocong Zhao, Jian Sun
[arXiv]

Dataset

Download the Waymo Open Motion Dataset v1.1; only the files in uncompressed/scenario/training_20s are needed. Place the downloaded files into training and testing folders separately.

Installation

Install dependency

sudo apt-get install libsuitesparse-dev

Create conda env

conda env create -f environment.yml
conda activate VCDI

Install Theseus

pip install functorch theseus-ai

Usage

Data Processing

Run data_process.py to process the raw data for training. This will convert the original data format into a set of .npz files, each containing the data of a scene with the AV and surrounding agents. You need to specify the file path to the original data --load_path and the path to save the processed data --save_path . You can optionally set --use_multiprocessing to speed up the processing.

python data_process.py \
--load_path /path/to/original/data \
--save_path /output/path/to/processed/data \
--use_multiprocessing

Training

Run train.py to learn the predictor and planner (if set --use_planning). You need to specify the file paths to training data --train_set. Leave other arguments vacant to use the default setting. You can choose different model types --model_type to train the three models mentioned in the paper.

python train.py \
--name VCDI \
--train_set /path/to/train/data \
--pretrain_epochs 5 \
--train_epochs 30 \
--batch_size 32 \
--learning_rate 2e-4 \
--use_planning \
--model_type VCDI \
--device cuda

Validating

Run test_model.py to evaluate the performance of the three models you trained with two key metrics: the average displacement error (ADE) and the final displacement error (FDE) over three timesteps (1, 3, and 5 seconds). You need to specify the path to the test dataset --test_set and also the file path to the trained model --model_path.

python test_model.py \
--name VCDI \
--model_path /path/to/saved/model \
--test_set /path/to/test/data \
--batch_size 32 \
--use_planning \
--model_type VCDI \
--device cuda

Open-loop testing

Run open_loop_test.py to test the trained planner in an open-loop manner. You need to specify the path to the original test dataset --open_loop_test_set (path to the folder) and also the file path to the trained model --model_path. Set --render to visualize the results and set --save to save the rendered images.

python open_loop_test.py \
--name open_loop \
--open_loop_test_set /path/to/original/test/data \
--model_path /path/to/saved/model \
--use_planning \
--model_type VCDI \
--render \
--save \
--device cpu

Citation

If you find our repo or our paper useful, please use the following citation:

@article{liu2024towards,
  title={Towards Interactive Autonomous Vehicle Testing: Vehicle-Under-Test-Centered Traffic Simulation},
  author={Liu, Yiru and Zhao, Xiaocong and Sun, Jian},
  journal={arXiv preprint arXiv:2406.02860},
  year={2024}
}

Acknowledgment

This project borrows methods and concepts from the DIPP project by Zhiyu Huang et al. I would like to thank the original authors for their contributions to the community.