Collision avoidance, kinematic feasibility, and road-compliance must be validated to ensure the drivability of planned motions for autonomous vehicles. The CommonRoad Drivability Checker toolbox unifies these checks in order to simplify the development and validation of motion planning algorithms. It is compatible with the CommonRoad benchmark suite, which additionally facilitates and drastically reduces the effort of the development of motion planning algorithms.
Please post questions, bug reports, etc. related to our tools or website in our forum.
We provide two installation options: Installation as a Python package or building from source.
Python Package: Install the python package via pip in your Conda environment:
pip install commonroad-drivability-checkerNote for MacOS M1 users: You need to use the 64-bit Anaconda Installer (graphical or command-line) in order to install the MacOS PyPi package.
Build from source: To build the drivability checker from source, please refer to the installation description in the documentation.
The software is written in Python 3.7 and C++17 and tested on MacOS and Linux (Ubuntu 18.04, 20.04 and 22.04).
The usage of the Anaconda Python distribution is strongly recommended.
For building the code from source, the following minimum versions are required:
Note for MacOS users (M1 or Intel):
The following third-party dependencies of the C++ code are only required for building the project from source!
Essential dependencies:
Not included as submodules:
Included as submodules:
Optional dependencies:
Note: Please be aware of the specific licensing conditions when including the optional dependencies Triangle and CGAL.
See also notes.txt and the licensing information on the respective package websites for more details.
The Python dependencies are listed in requirements.txt.
A full documentation as well as tutorials to get started with the tool can be found on our toolpage.
Christian Pek, Vitaliy Rusinov, Stefanie Manzinger, Murat Can Üste, and Matthias Althoff
Abstract— Collision avoidance, kinematic feasibility, and road-compliance must be validated to ensure the drivability of planned motions for autonomous vehicles. Although these tasks are highly repetitive, computationally efficient toolboxes are still unavailable. The CommonRoad Drivability Checker—an open-source toolbox—unifies these mentioned checks. It is compatible with the CommonRoad benchmark suite, which additionally facilitates the development of motion planners. Our toolbox drastically reduces the effort of developing and validating motion planning algorithms. Numerical experiments show that our toolbox is real-time capable and can be used in real test vehicles.
Fulltext is available on mediaTUM.
@inproceedings{ PekIV20.pdf,
author = "Christian Pek, Vitaliy Rusinov, Stefanie Manzinger, Murat Can Üste, and Matthias Althoff",
title = "CommonRoad Drivability Checker: Simplifying the Development and Validation of Motion Planning Algorithms",
pages = "1-8",
booktitle = "Proc. of the IEEE Intelligent Vehicles Symposium",
year = "2020",
abstract = "Collision avoidance, kinematic feasibility, and road-compliance must be validated to ensure the drivability of planned motions for autonomous vehicles. Although these tasks are highly repetitive, computationally efficient toolboxes are still unavailable. The CommonRoad Drivability Checker— an open-source toolbox—unifies these mentioned checks. It is compatible with the CommonRoad benchmark suite, which additionally facilitates the development of motion planners. Our toolbox drastically reduces the effort of developing and validating motion planning algorithms. Numerical experiments show that our toolbox is real-time capable and can be used in real test vehicles."
}