GameFormer

This repository contains the code for the ICCV'23 paper:

GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving
Zhiyu Huang, Haochen Liu, Chen Lv
AutoMan Research Lab, Nanyang Technological University
[arXiv] [Project Website]

Overview

In this repository, you can expect to find the following features:

Included 🤟:

• Code for interaction prediction using a joint model on Waymo Open Motion Dataset (WOMD)
• Code for open-loop planning on selected dynamic scenarios within WOMD

Not included 😵:

• Code for the marginal model with EM ensemble for interaction prediction on WOMD
• Code for closed-loop planning on WOMD. Please refer to our previous work DIPP for that.
• Code for packaging and submitting prediction results to the WOMD Interaction Prediction Challenge

For those interested in the nuPlan dataset experimentation, we invite you to visit the GameFormer Planner repository, which provides a more comprehensive planning framework.

Dataset and Environment

1. Download

• Download the Waymo Open Motion Dataset v1.1. Utilize data from scenario/training_20s or scenario/training for training, and data from scenario/validation and scenario/validation_interactive for testing.
• Clone this repository and navigate to the directory:

git clone https://github.com/MCZhi/GameFormer.git && cd GameFormer

2. Environment Setup

• Create a conda environment:

conda create -n gameformer python=3.8

• Activate the conda environment:

conda activate gameformer

• Install the required packages:

pip install -r requirements.txt

Interaction Prediction

Navigate to the interaction_prediction directory:

cd interaction_prediction

1. Data Process

Preprocess data for model training using the following command:

python data_process.py \
--load_path path/to/your/dataset/scenario/set_path \
--save_path path/to/your/processed_data/set_path \
--use_multiprocessing \
--processes=8

Specify --load_path to the location of the downloaded set path, --save_path to the desired processed data path, and enable --use_multiprocessing for parallel data processing. You can perform this separately for the training and validation_interactive sets.

2. Training & Evaluation

Train the model using the command:

bash train.sh 4 #number of GPUs

NOTE: Before training, specify the processed paths for --train_set and --valid_set inside the script file. Set --name to save logs and checkpoints. As referred in train.py, you can also adjust other arguments like --seed, --train_epochs, --batch_size for customed training.

Open-loop Planning

Navigate to the open_loop_planning directory:

cd open_loop_planning

1. Data Process

Preprocess data for model training using the following command:

python data_process.py \
--load_path path/to/your/dataset/training_20s \
--save_path path/to/your/processed_data \
--use_multiprocessing \

Set --load_path to the location of the downloaded dataset, --save_path to the desired processed data path, and enable --use_multiprocessing for parallel data processing. You can perform this separately for the training and validation sets.

2. Training

Train the model using the command:

python train.py \
--train_set path/to/your/processed_data/train \
--valid_set path/to/your/processed_data/valid

Specify the paths for --train_set and --valid_set. You can set the --levels to determine the number of interaction levels. Adjust other parameters like --seed, --train_epochs, --batch_size, and --learning_rate as needed for training.

The training log and models will be saved in training_log/{name}.

3. Testing

For testing, run:

python open_loop_test.py \
--test_set path/to/your/dataset/validation \
--model_path path/to/your/saved/model

Specify --test_set as the path to the scenario/validation data, and --model_path as the path to your trained model. Use --render to visualize planning and prediction results.

The testing result will be saved in testing_log/{name}.

Citation

If you find this repository useful for your research, please consider giving us a star 🌟 and citing our paper.

@article{huang2023gameformer,
  title={GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving},
  author={Huang, Zhiyu and Liu, Haochen and Lv, Chen},
  journal={arXiv preprint arXiv:2303.05760},
  year={2023}
}