SafeBench

📣 We are hosting the Secure and Safe Autonomous Driving (SSAD) Workshop and Challenge at CVPR 2023! Please check our website for more details.

[ Documentation | Demo Video | Website | Paper ]

This is the source code of Safebench platform, which is designed based on Carla to evaluate the security and safety of autonomous driving vehicles. More details about this platform can be found in this NeurIPS 2022 paper.

Installation

Setup conda environment

conda create -n safebench python=3.8
conda activate safebench

Clone this git repo in an appropriate folder

git clone git@github.com:trust-ai/SafeBench.git

Enter the repo root folder and install the packages:

pip install -r requirements.txt
pip install -e .

Download our CARLA_0.9.13 and extract it to your folder.
Run sudo apt install libomp5 as per this git issue.
Add the python API of CARLA to the PYTHONPATH environment variable. You can add the following commands to your ~/.bashrc:

export CARLA_ROOT={path/to/your/carla}
export PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI/carla/dist/carla-0.9.13-py3.8-linux-x86_64.egg
export PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI/carla/agents
export PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI/carla
export PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI

Usage

1. Desktop Users

Enter the CARLA root folder, launch the CARLA server and run our platform with

# Launch CARLA
./CarlaUE4.sh -prefernvidia -windowed -carla-port=2000

# Launch SafeBench in another terminal
python scripts/run.py --agent_cfg=basic.yaml --scenario_cfg=standard.yaml --mode=eval

2. Remote Server Users

Enter the CARLA root folder, launch the CARLA server with headless mode, and run our platform with

# Launch CARLA
./CarlaUE4.sh -prefernvidia -RenderOffScreen -carla-port=2000

# Launch SafeBench in another terminal
SDL_VIDEODRIVER="dummy" python scripts/run.py --agent_cfg=basic.yaml --scenario_cfg=standard.yaml --mode=eval

(Optional) You can also visualize the pygame window using TurboVNC. First, launch CARLA with headless mode, and run our platform on a virtual display.

# Launch CARLA
./CarlaUE4.sh -prefernvidia -RenderOffScreen -carla-port=2000

# Run a remote VNC-Xserver. This will create a virtual display "8".
/opt/TurboVNC/bin/vncserver :8 -noxstartup

# Launch SafeBench on the virtual display
DISPLAY=:8 python scripts/run.py --agent_cfg=basic.yaml --scenario_cfg=standard.yaml --mode=eval

You can use the TurboVNC client on your local machine to connect to the virtual display.

# Use the built-in SSH client of TurboVNC Viewer
/opt/TurboVNC/bin/vncviewer -via user@host localhost:n

# Or you can manually forward connections to the remote server by
ssh -L fp:localhost:5900+n user@host
# Open another terminal on local machine
/opt/TurboVNC/bin/vncviewer localhost::fp

where user@host is your remote server, fp is a free TCP port on the local machine, and n is the display port specified when you started the VNC server on the remote server ("8" in our example).

3. Scenic users

If you want to use scenic to control the surrounding adversarial agents, and use RL to control the ego, then first install scenic as follows:

# Download Scenic repository
git clone https://github.com/BerkeleyLearnVerify/Scenic.git
cd Scenic
python -m pip install -e .

Then you can create a directory in safebench/scenario/scenario_data/scenic_data, e.g., Carla_Challenge, and put your scenic files in that directory (the relative map path defined in scenic file should be ../maps/*.xodr).

Next, set the param scenic_dir in safebench/scenario/config/scenic.yaml with the directory where you store the scenic files, e.g., safebench/scenario/scenario_data/scenic_data/Carla_Challenge, and our code will automatically load all scenic files in that directory.

For selecting the most adversarial scenes, the param sample_num within the scenic.yaml serves to determine the number of scenes sampled for each scenic file and the param select_num is used to specify the number of the most adversarial scenes to be selected from among the sample_num scenes:

python scripts/run.py --agent_cfg=sac.yaml --scenario_cfg=scenic.yaml --num_scenario 1 --mode train_scenario

Now you can test the ego with these selected adversarial scenes:

python scripts/run.py --agent_cfg=sac.yaml --scenario_cfg=scenic.yaml --num_scenario 1 --mode eval

Or if you want to Launch it on the virtual display

DISPLAY=:8 python scripts/run.py --agent_cfg=sac.yaml --scenario_cfg=scenic.yaml --num_scenario 1 --mode eval

Running Argument

Argument	Choice	Usage
`mode`	`[train_agent\train_scenario\eval]`	We provide three modes for training agent, training scenario, and evaluation.
`agent_cfg`	path of .yaml	configuration file of agent.
`scenario_cfg`	path of .yaml	configuration file of scenario.
`max_episode_step`	int	Number of episode used for training agents and scenario.
`num_scenarios`	`{1, 2, 3, 4}`	We support running multiple scenarios in parallel. Current map allows at most 4 scenarios.
`save_video`	store_true	We support saving videos during the evaluation mode.
`auto_ego`	store_true	Overwrite the action of ego agent with auto-polit

hyyh28/SafeBench