yangkaia123 / dipp

Differentiable Integrated Prediction and Planning Framework for Urban Autonomous Driving https://mczhi.github.io/DIPP/

DIPP-Differentiable Integrated Prediction and Planning

This repo is the implementation of the following paper:

Differentiable Integrated Motion Prediction and Planning with Learnable Cost Function for Autonomous Driving
Zhiyu Huang, Haochen Liu, Jingda Wu, Chen Lv
AutoMan Research Lab, Nanyang Technological University
[arXiv] [Project Website]

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 DIPP

Install Theseus

Install the Theseus library, follow the guidelines.

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 and validation data --valid_set. Leave other arguments vacant to use the default setting.

python train.py \
--name DIPP \
--train_set /path/to/train/data \
--valid_set /path/to/valid/data \
--use_planning \
--seed 3407 \
--num_workers 8 \
--pretrain_epochs 5 \
--train_epochs 20 \
--batch_size 32 \
--learning_rate 2e-4 \
--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 --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 \
--test_set /path/to/original/test/data \
--model_path /path/to/saved/model \
--use_planning \
--render \
--save \
--device cpu

Closed-loop testing

Run closed_loop_test.py to do closed-loop testing. You need to specify the file path to the original test data --test_file (a single file) and also the file path to the trained model --model_path. Set --render to visualize the results and set --save to save the videos.

python closed_loop_test.py \
--name closed_loop \
--test_file /path/to/original/test/data \
--model_path /path/to/saved/model \
--use_planning \
--render \
--save \
--device cpu

Citation

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

@article{huang2022differentiable,
  title={Differentiable Integrated Motion Prediction and Planning with Learnable Cost Function for Autonomous Driving},
  author={Huang, Zhiyu and Liu, Haochen and Wu, Jingda and Lv, Chen},
  journal={arXiv preprint arXiv:2207.10422},
  year={2022}
}