@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.},
}