Autonomous Driving with Diffusion Model

This repo is made to acheive controllable end-to-end autonomous driving through the Diffusion model. We provide the code for training the model and interacting with the model in the Carla simulator. We also provide the pre-trained model for users to evaluate the performance on the Carla leaderboard 1.0.

No Guidance	Classifier-free Guidance	Classifier Guidance

A. Setup 🚀

Please ensure you have installed the Carla simulator and the Python API.

Warning

You should checkout your own cuda version and install your own PyTorch version. We provide an example of PyTorch 2.2.2 with cuda 12.1.

git clone https://github.com/Justin900429/carla_diffusion.git
conda create -n carla-diffusion python=3.8 -y
conda activate carla-diffusion
conda install pytorch==2.2.2 torchvision==0.17.2 pytorch-cuda=12.1 -c pytorch -c nvidia -y
pip install -r requirements.txt

If you would like to collect data by yourself, please install the Carla Python API and Carla simulator:

# Carla version < 0.9.12
easy_install install {CARLA_ROOT}/PythonAPI/carla/dist/carla-{CARLA_VERSION}-py{CHECK_THIS_VERSION}-linux-x86_64.egg

# Carla version >= 0.9.12
pip install carla=={CARLA_VERSION}

Afterwards, please modify the carla_sh_path in config/train_rl.yaml to yours.

B. Data setup 📊

Note

Users can choose to download the provided data or collect the data by themselves.

(Option 1) Downloading the data 📦

gdown 1JfHD3bW0oBrjwQJ-nZz5GhVfLN7Nkn8R -O data.zip
unzip -q data.zip && rm data.zip

(Option 2) Collecting the data 📡

python misc/data_collect.py --save-path {PLACE_TO_SAVE_DATA} --save-num {NUM_OF_DATA}

# Concrete example
python misc/data_collect.py --save-path data/ --save-num 5000

If you would like to collect data under off-screen mode, please add the flag --off-screen.

If you find the data collection process fail during the simulation, please try the following way (this always run in off-screen mode):

python misc/collect_loop.py --save-path {PLACE_TO_SAVE_DATA} --save-num {NUM_OF_DATA}

# Concrete example
python misc/collect_loop.py --save-path data/ --save-num 5000

This helps restart the simulation when the simulation is crashed but the number of data does not reach the target.

C. Usage 🛠

C-1. Model training 🧠

Users can choose the config file as shown below to train the model.

Config path	Description
configs/default.yaml	Train the model without any guidance. (For ablation study)
configs/guidance/free_guidance.yaml	Train the model with classifier-free guidance.
configs/guidance/classifier_guidance.yaml	Train the model with classifier guidance.

# with single-gpu
python train.py --config {CONFIG_PATH}

# with multi-gpus
accelerate launch --multi_gpu --num_processes={NUM_OF_GPU} train.py --config {CONFIG_PATH}

C-2. Interact with the model 🕹

Tip

Check the description above to choose the config file.

python interact.py --config {CONFIG_PATH} --plot-on-world --save-bev-path {PATH_TO_SAVE_BEV_IMAGES} --opts EVAL.CHECKPOINT final.pth

# Concrete example
# 1. without any guidance
python interact.py --config configs/default.yaml --plot-on-world --save-bev-path bev_images  --opts EVAL.CHECKPOINT final.pth

# 2. with classifier-free guidance
python interact.py --config configs/guidance/free_guidance.yaml --plot-on-world --save-bev-path bev_images  --opts EVAL.CHECKPOINT final.pth

# 3. with classifier guidance
python interact.py --config configs/guidance/classifier_guidance.yaml --plot-on-world --save-bev-path bev_images  --opts EVAL.CHECKPOINT final.pth

Note

Both --plot-on-world and --save-bev-path are optional.

D. Leaderboard 🏆

(Optional) D-0. Download the pre-trained model 📦

We provide our pre-trained model for users to evaluate the performance on the Carla leaderboard.

Model	Link
Classifier-free guidance	drive
Classifier guidance	drive
Classifier guidance (with more data)	drive

mkdir checkpoints

# Classifier-free guidance
gdown 12jZFmxaNRq2NhY2cHL95KvydbEuiJH4Y -O checkpoints/free_guidance.pth

# Classifier guidance
gdown 1FZT1XsSuTUN5MawNsJaFNbBIt2xwPLgA -O checkpoints/classifier_guidance.pth

# Classifier gudiance (with more training data)
gdown 1_a3fjs9M6MS4ofQuyStn3flT1kS_lbMC -O checkpoints/classifier_guidance_plus.pth

D-1. Environment setup

Tip

This environment is different from the previous one and requires python 3.7 with Carla 0.9.10.

conda create -n carla-leaderboard python=3.7 -y
conda activate carla-leaderboard
conda install pytorch==1.13.0 torchvision==0.14.0 pytorch-cuda=11.7 -c pytorch -c nvidia -y
pip install -r requirements-leaderboard.txt

D-2. Run the evaluator

Please check the below table and command to run different benchmarks.

Benchmark	Scenario file	Route file
Longest 6	leaderboard/data/scenarios/eval_scenarios.json	leaderboard/data/evaluation_routes/longest6_tiny.xml

# Open this in another terminal
bash {carla_server_root}/CarlaUE4.sh --world-port=2000 -opengl

bash leaderboard/scripts/run_evaluation.sh <carla_server_root> <scenario_file> <route_file> <agent_config_file> <save_folder> <save_file>

# Concrete example (take longest 6 as an example)
bash leaderboard/scripts/run_evaluation.sh \
     /path/to/carla/0.9.10\
     leaderboard/data/scenarios/eval_scenarios.json \
     leaderboard/data/evaluation_routes/longest6_tiny.xml \
     configs/guidance/free_guidance.yaml \
     free_guidance_longest_6 \
     free_guidance_longest_6/result_longest_6.json

Important

Users should specify the checkpoint directly in the agent config file (see C-1) by setting EVAL.CHECKPOINT.

...
EVAL:
    CHECKPOINT: /path/to/checkpoint.pth

D-3. Generate the statistics

After obtaining the <save_file> results, users can generate the statistics by running the following command:

python e2e_driving/statistics.py --json-file <save_file>

# Concrete example
python e2e_driving/statistics.py --json-file free_guidance_longest_6/result_longest_6.json

Approach	Score			Collision			Red light	Vehicle Blocked	Outside Road
	Composed	Penalty	Route	Layout	Pedestrian	Vehicle
Classifier-free guidance	0.00	0.00	100.00	0.53	0.76	8.77	2.60	0.00	0.17
classifier guidance	2.66	0.10	72.80	0.25	0.00	4.53	2.82	0.37	0.00
classifier guidance*	14.89	0.23	84.84	0.00	0.00	0.91	1.90	0.23	0.00

Acknowledgement 🙏

• Our environment is adapted from Carla Roach.
• The project template is obtained from deep-learning-template.
• The agent for carla leaderboard is adapted from TCP.