BeamNGpy is an official library providing a Python interface to BeamNG.research, the research-oriented fork of the video game BeamNG.drive.

It allows remote control of the simulation, including vehicles contained in it:

Vehicles and the environment can be equipped with various sensors that provide simulated sensor data such as a camera feed, with options for depth values and pixel-perfect semantic annotation or a simulated Lidar sensor:

Usage of BeamNGpy requires BeamNG.research to be installed. Builds of BeamNG.research are made available for non-commercial use upon request using this form. For commercial use, contact us at [email protected]. Once downloaded (and extracted, depending on whether or no BeamNG.research was obtained as a .zip), you can set an environment variable BNG_HOME to where BeamNG.research can be run from, or provide a path to the BeamNGpy library during initialization.

The regular Steam release of BeamNG.drive is compatible to an extent as well. Certain sensors like the simulated LiDAR or camera will not work, but most of the functions that are not exclusive to a research build will likely work.

The library itself is available on PyPI and can therefore be installed using common methods like pip:

pip install beamngpy

Once installed, the library can be imported using import beamngpy. A short usage example setting up a scenario with one vehicle in the West Coast USA map that spans the area is:

from beamngpy import BeamNGpy, Scenario, Vehicle

# Instantiate BeamNGpy instance running the simulator from the given path,
# communicating over localhost:64256
bng = BeamNGpy('localhost', 64256, home='/path/to/bng/research')
# Create a scenario in west_coast_usa called 'example'
scenario = Scenario('west_coast_usa', 'example')
# Create an ETK800 with the licence plate 'PYTHON'
vehicle = Vehicle('ego_vehicle', model='etk800', licence='PYTHON')
# Add it to our scenario at this position and rotation
scenario.add_vehicle(vehicle, pos=(-717, 101, 118), rot=None, rot_quat=(0, 0, 0.3826834, 0.9238795))
# Place files defining our scenario for the simulator to read
scenario.make(bng)

# Launch BeamNG.research
bng.open()
# Load and start our scenario
bng.load_scenario(scenario)
bng.start_scenario()
# Make the vehicle's AI span the map
vehicle.ai_set_mode('span')
input('Hit enter when done...')

More examples can be found in the examples/ folder of this repository and the documentation of the library is available here..