CommonRoad-CARLA Interface

The CommonRoad-CARLA Interface provides APIs to use CommonRoad-based tools together with the 3D simulator CARLA.

Installation

Carla

Install Carla 9.15 via as described in the CARLA documentation (either use the system installation or download a release from their GitHub repository). Also add the additional maps if you want to use them. You can test the successful download/usage of CARLA via running ./CarlaEU4.sh within the CARLA installation.

CommonRoad-CARLA Interface

Usage in other projects

We provide an PyPI package which can be installed with the following command

pip install commonroad-carla-interface

License

The CommonRoad-CARLA-Interface is published under GNU3 license (since this is the most restrictive license of a Python package we import). We base some parts of our code on CARLA's example scripts (MIT license): https://github.com/carla-simulator/carla/tree/dev/PythonAPI/examples, e.g. the head-up display.

Development

It is recommended to use poetry as an environment manager. Clone the repository and install it with poetry.

git clone git@github.com:commonroad/commonroad-carla-interface.git
poetry shell
poetry install

We recommend to use PyCharm (Professional) as IDE.

Getting started

We provide tutorial script for different features we support. A deployed version of the documentation can be found here. You can use these tutorial scripts as the starting point. The code for converting a map from CommonRoad to OpenDRIVE is located in the CommonRoad Scenario Designer.

Supported Features

We support five ways of interacting with CARLA:

• CommonRoad scenario replay: 2D/3D visualization of CommonRoad scenarios together with solutions.
• Keyboard control: Controlling an ego vehicle via keyboard in a 2D/3D visualization.
• Scenario Generation: Generation of CommonRoad scenarios with different configurations.
• Motion planning: Driving in a CARLA simulation with a CommonRoad-based motion planner.
• Wheel control: Controlling an ego vehicle via steering wheel and pedals in a 2D/3D visualization.

The default configuration can be found under crcarla.helper.config and in our documentation. The CARLA interface can take care of starting the CARLA server. The path to CARLA can either be provided manually via the config parameters or the CARLA release folder path corresponds to one of our default locations: /opt/carla-simulator/, //CARLA_0.9.14_RSS/, //CARLA_0.9.15/ (default paths work only for Ubuntu; for a complete list of default paths, see our configuration options).

Documentation

A deployed version of the documentation can be found here. You can also generate the documentation within your activated Poetry environment using.

poetry shell
mkdocs build

The documentation will be located under site, where you can open index.html in your browser to view it. For updating the documentation you can also use the live preview:

poetry shell
mkdocs serve

Authors

Responsible: Sebastian Maierhofer Contributors (in alphabetic order by last name): Linge Ai, Yassine El Alami, Rupert Bauernfeind, Vy Hong, Miguel Marcano, Valentin Merle, Fen Shi, Dávid Tokár Zhihao Yang

Citation

If you use our code for research, please consider to cite our papers and the CARLA publication:

@inproceedings{Maierhofer2024a,
	author = {Maierhofer, Sebastian and  Althoff, Matthias},
	title = {CommonRoad-CARLA Interface: Bridging the Gap between Motion Planning and 3D Simulation},
	booktitle = {2024 IEEE Intelligent Vehicles Symposium (IV)},
	year = {2024},
	pages = {tbd},
	abstract = {Motion planning algorithms should be tested on a large, diverse, and realistic set of scenarios before
	            deploying them in real vehicles. However, existing 3D simulators usually focus on perception and
	            end-to-end learning, lacking specific interfaces for motion planning. We present an interface for the
	            CARLA simulator focusing on motion planning, e.g., to create configurable test scenarios and execute
	            motion planners in interactive environments. Additionally, we introduce a converter from lanelet-based maps
	            to OpenDRIVE, making it possible to use CommonRoad and Lanelet2 maps in CARLA. Our evaluation shows that
	            our interface is easy to use, creates new scenarios efficiently, and can successfully integrate motion
	            planners to solve CommonRoad scenarios. Our tool is published as an open-source toolbox at commonroad.in.tum.de.},
}

@inproceedings{Maierhofer2021,
	author = {Sebastian Maierhofer, Moritz Klischat, and Matthias Althoff},
	title = {CommonRoad Scenario Designer: An Open-Source Toolbox for Map Conversion and Scenario Creation for Autonomous Vehicles},
	booktitle = {Proc. of the IEEE Int. Conf. on Intelligent Transportation Systems },
	year = {2021},
	pages = {3176-3182},
	abstract = {Maps are essential for testing autonomous driving functions. Several map and scenario formats are
                    available. However, they are usually not compatible with each other, limiting their usability.
                    In this paper, we address this problem using our open-source toolbox that provides map converters
                    from different formats to the well-known CommonRoad format. Our toolbox provides converters for
                    OpenStreetMap, Lanelet/Lanelet2, OpenDRIVE, and SUMO. Additionally, a graphical user interface is
                    included, which allows one to efficiently create and manipulate CommonRoad maps and scenarios.
                    We demonstrate the functionality of the toolbox by creating CommonRoad maps and scenarios based on
                    other map formats and manually-created map data.},
}

@InProceedings{Dosovitskiy2017,
  author    = {Alexey Dosovitskiy and German Ros and Felipe Codevilla and Antonio Lopez and Vladlen Koltun},
  booktitle = {Proc. of the 1st Annual Conference on Robot Learning},
  title     = {{CARLA}: {An} Open Urban Driving Simulator},
  year      = {2017},
  pages     = {1--16},
  abstract  = {We introduce CARLA, an open-source simulator for autonomous driving research.
               CARLA has been developed from the ground up to support development, training,
               and validation of autonomous urban driving systems. In addition to open-source
               code and protocols, CARLA provides open digital assets (urban layouts, buildings,
               vehicles) that were created for this purpose and can be used freely.
               The simulation platform supports flexible specification of sensor suites and
               environmental conditions. We use CARLA to study the performance of three
               approaches to autonomous driving: a classic modular pipeline, an end-to-end model
               trained via imitation learning, and an end-to-end model trained via reinforcement learning.
               The approaches are evaluated in controlled scenarios of increasing difficulty,
               and their performance is examined via metrics provided by CARLA, illustrating the platform’s utility for autonomous driving research.},
}