pyauto

A python package for accessing the Automotive Urban Traffic Ontology using owlready2. It basically provides access to

• loading A.U.T.O. into owlready2
• A.U.T.O.'s set of IRIs
• retrieving a single ontology of A.U.T.O. based on a given IRI
• creating scenarios, scenes, and sceneries within A.U.T.O.
• saving such models to an OWL file
• a web-based visualizer for A.U.T.O. scenarios and scenes

Install

pyauto requires Python >= 3.10. First, initialize submodules: git submodule update --init --recursive. Install the requirements using pip install -r requirements.txt, and then this package via pip install ..

Example

This small example loads A.U.T.O., creates a vehicle in the ABox, saves, and visualizes it.

import logging

from pyauto import auto
from pyauto.models.scenario import Scenario
from pyauto.visualizer import visualizer

logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s: %(message)s")

# creates a scenario with two empty scenes (and loads A.U.T.O.)
sc = Scenario(1, name="Example Scenario")

# scene: creates ego vehicle & pedestrian
l4_de_1 = sc[0].ontology(auto.Ontology.L4_DE)
l4_co_1 = sc[0].ontology(auto.Ontology.L4_Core)
ego_1 = l4_de_1.Passenger_Car()
ego_1.set_geometry(5, 10, 5.1, 2.2)
ego_1.set_velocity(6, 0)
ped_1 = l4_co_1.Pedestrian()
ped_1.set_geometry(9, 1, 0.6, 0.3)
ped_1.set_velocity(0, 3)

# saves the ABoxes - also creates /tmp/scenario.kbs
sc.save_abox("/tmp/scenario.owl")

# visualizes the ABox
visualizer.visualize(sc)

Visualization

pyauto can be used to visualize saved A.U.T.O. ABoxes in form .kbs files. For this, call pyauto /tmp/scenario.kbs after installing pyauto (for the .kbs file created by the example above). More information are available in pyauto -h.

More examples

For more examples on API usage, have a look at the files in the examples folder.