SafeBench: A Benchmark for Evaluating Autonomous Vehicles in Safety-critical Scenarios

Perception Evaluation	Control Evaluation

Installation

Recommended system: Ubuntu 20.04 or 22.04

1. Local Installation

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Step 1: Setup conda environment

conda create -n safebench python=3.8
conda activate safebench

Step 2: Clone this git repo in an appropriate folder

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

Step 3: Enter the repo root folder and install the packages:

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

Step 4: Download our CARLA_0.9.13 and extract it to your folder.

Step 5: Run sudo apt install libomp5 as per this git issue.

Step 6: 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

2. Docker Installation (Beta)

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We also provide a docker image with CARLA and SafeBench installed. Use the following command to launch a docker container:

bash docker/run_docker.sh

The CARLA simulator is installed at /home/safebench/carla and SafeBench is installed at /home/safebench/SafeBench.

Usage

1. Desktop Users

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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

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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. Visualization with CarlaViz

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CarlaViz is a convenient visualization tool for CARLA developed by a former member mjxu96 of our team. To use CarlaViz, please open another terminal and follow the intructions:

# pull docker image from docker hub
docker pull mjxu96/carlaviz:0.9.13

# run docker container of CarlaViz
cd Safebench/scripts
sh start_carlaviz.sh

Then, you can open the CarlaViz window at http://localhost:8080. You can also remotely access the CarlaViz window by forwarding the port 8080 to your local machine.

4. Scenic users

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

Running Arguments

Argument	Choice	Usage
mode	{train_agent, train_scenario, eval}	We provide three modes for training agent, training scenario, and evaluation.
agent_cfg	str	path to the configuration file of agent.
scenario_cfg	str	path to the configuration file of scenario.
max_episode_step	int	Number of episode used for training agents and scenario.
num_scenario	{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
port	int	Port used by Carla, default 2000