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# Clone the code to local
git clone https://github.com/metadriverse/trafficgen.git
cd trafficgen

# Create virtual environment
conda create -n trafficgen python=3.8
conda activate trafficgen

# You should install pytorch by yourself to make them compatible with your GPU
# For cuda 11.0:
pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
# Install basic dependency
pip install -e .

If you find error messages related to geos when installing Shapely, checkout this post.

You can run the following scripts to test whether the setup is correct. These scripts do not require downloading data.

python train_init.py -c local

python train_act.py -c local 

Download from Waymo Dataset

• Register your Google account in: https://waymo.com/open/
• Open the following link with your Google account logged in: https://console.cloud.google.com/storage/browser/waymo_open_dataset_motion_v_1_1_0
• Download one or more proto files from waymo_open_dataset_motion_v_1_1_0/uncompressed/scenario/training_20s
• Move download files to PATH_A, where you store the raw tf_record files.

Note: it is not necessary to download all the files from Waymo. You can download one of them for a simple test.

Data Preprocess

python trafficgen/utils/trans20.py PATH_A PATH_B None

Note: PATH_B is where you store the processed data.

Please download two models from this link: https://drive.google.com/drive/folders/1TbCV6y-vssvG3YsuA6bAtD9lUX39DH9C?usp=sharing

And then put them into trafficgen/traffic_generator/ckpt folder.

Running following scripts will generate images and GIFs (if with --gif) visualizing the new traffic scenarios in traffic_generator/output/vis folder.

# change the data usage and set the data dir in debug.yaml

# First, you have to change working directory
cd TrafficGen/trafficgen

python generate.py [--gif] [--save_metadrive]

Set --gif flag to generate GIF files.

After running python generate.py --save_metadrive, a folder trafficgen/traffic_generator/output/scene_pkl will be created, and you will see many pickle files. Each .pkl file is a scenario created by TrafficGen.

We provide a script to create single-agent RL environment with TrafficGen generated data. Please refer to trafficgen/run_metadrive.py for details.

We also provide pre-generated scenarios from TrafficGen, so you can kick off RL training on TrafficGen-generated scenarios immediately. Please follow trafficgen/dataset/README.md to download the dataset.

cd trafficgen/

# Run generated scenarios:
python run_metadrive.py --dataset traffic_generator/output/scene_pkl

# Please read `trafficgen/dataset/README.md` to download pre-generated scenarios
# Then you can use them to create an RL environment:
python run_metadrive.py --dataset dataset/validation

# If you want to visualize the generated scenarios, with the ego car also replaying data, use:
python run_metadrive.py --dataset dataset/validation --replay

# If you want to create RL environment where traffic vehicles are not replaying 
# but are controlled by interactive IDM policy, use:
python run_metadrive.py --dataset dataset/validation --no_replay_traffic

You can then kick off RL training by utilizing the created environment showcased in the script above.

# Dependencies:
pip install ray==2.2.0
pip install ray[rllib]==2.2.0

# Install pytorch by yourself and make it compatible with your CUDA
# ...

# Kickoff training
cd trafficgen

python run_rl_training.py --exp-name EXPERIMENT_NAME --num-gpus 1 
# You can also specify the path to dataset. Currently we set:

--dataset_train  dataset/1385_training
--dataset_test  dataset/validation

# by default. Check the file for more details about the arguments. 

Use the sample data packed in the code repo directly

python train_init.py -c local

python train_act.py -c local

For training, we recommend to download all the files from: https://console.cloud.google.com/storage/browser/waymo_open_dataset_motion_v_1_1_0

PATH_A is the raw data path

PATH_B is the processed data path

Execute the data_trans.sh:

sh utils/data_trans.sh PATH_A PATH_B

Note: This will take about 2 hours.

Then modify the 'data_path' in init/configs and act/configs to PATH_B, run:

python init/uitls/init_dataset.py
python act/uitls/act_dataset.py

to get a processed cache for the model.

Modify cluster.yaml. Change data_path, data_usage, run:

python train_act.py -c cluster -d 0 1 2 3 -e exp_name

-d denotes which GPU to use

@inproceedings{feng2023trafficgen,
  title={Trafficgen: Learning to generate diverse and realistic traffic scenarios},
  author={Feng, Lan and Li, Quanyi and Peng, Zhenghao and Tan, Shuhan and Zhou, Bolei},
  booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={3567--3575},
  year={2023},
  organization={IEEE}
}