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EDGAR: An Autonomous Driving Research Platform -- From Feature Development to Real-World Application

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arxiv 2309.15492 v2 pith:HKHXC3AO submitted 2023-09-27 cs.RO

Phillip Karle , Tobias Betz , Marcin Bosk , Felix Fent , Nils Gehrke , Maximilian Geisslinger , Luis Gressenbuch , Philipp Hafemann
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Sebastian Huber Maximilian Hübner Sebastian Huch Gemb Kaljavesi Tobias Kerbl Dominik Kulmer Tobias Mascetta Sebastian Maierhofer Florian Pfab Filip Rezabek Esteban Rivera Simon Sagmeister Leander Seidlitz Florian Sauerbeck Ilir Tahiraj Rainer Trauth Nico Uhlemann Gerald Würsching Baha Zarrouki Matthias Althoff Johannes Betz Klaus Bengler Georg Carle Frank Diermeyer Jörg Ott Markus Lienkamp
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keywords developmentautonomousresearchsoftwaredigitaledgarstackstwin
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While current research and development of autonomous driving primarily focuses on developing new features and algorithms, the transfer from isolated software components into an entire software stack has been covered sparsely. Besides that, due to the complexity of autonomous software stacks and public road traffic, the optimal validation of entire stacks is an open research problem. Our paper targets these two aspects. We present our autonomous research vehicle EDGAR and its digital twin, a detailed virtual duplication of the vehicle. While the vehicle's setup is closely related to the state of the art, its virtual duplication is a valuable contribution as it is crucial for a consistent validation process from simulation to real-world tests. In addition, different development teams can work with the same model, making integration and testing of the software stacks much easier, significantly accelerating the development process. The real and virtual vehicles are embedded in a comprehensive development environment, which is also introduced. All parameters of the digital twin are provided open-source at https://github.com/TUMFTM/edgar_digital_twin.

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Cited by 6 Pith papers

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    cs.RO 2025-05 conditional novelty 5.0 of 10

    A 69 kg, 1.17 m automated driving research platform with all-wheel steering and drive, modular ROS architecture, and optimization-based planning and control, demonstrated in two experimental scenarios.

  3. Multi-LiCa: A Motion and Targetless Multi LiDAR-to-LiDAR Calibration Framework

    cs.RO 2025-01 conditional novelty 5.0 of 10

    Multi-LiCa calibrates multiple LiDARs automatically from static scenes, with no targets or initial pose guess, using feature matching plus iterative scan merging to bridge non-overlapping fields of view.

  4. karl. -- A Research Vehicle for Automated and Connected Driving

    cs.AR 2026-02 conditional novelty 4.0 of 10

    A detailed hardware and system design of karl., an L4-capable research vehicle built on a VW T7 Multivan, with measurements of synchronization, latency, power, and vehicle control.

  5. A Hierarchical Test Platform for Vision Language Model (VLM)-Integrated Real-World Autonomous Driving

    cs.RO 2025-06 conditional novelty 4.0 of 10

    A hierarchical real-world testing platform for VLM-integrated autonomous driving is demonstrated on a by-wire vehicle, with a cloud GPT-4 agent making high-level decisions inside an Autoware stack.

  6. Simulation to Reality: Testbeds and Architectures for Connected and Automated Vehicles

    cs.MA 2025-05 conditional novelty 3.0 of 10

    A review of 97 CAV simulators and testbeds that derives eight software requirements and four testbed-selection recommendations for moving from simulation to reality.

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