EDGAR: An Autonomous Driving Research Platform -- From Feature Development to Real-World Application
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| Format: | Preprint |
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2023
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| author | Karle, Phillip Betz, Tobias Bosk, Marcin Fent, Felix Gehrke, Nils Geisslinger, Maximilian Gressenbuch, Luis Hafemann, Philipp Huber, Sebastian Hübner, Maximilian Huch, Sebastian Kaljavesi, Gemb Kerbl, Tobias Kulmer, Dominik Mascetta, Tobias Maierhofer, Sebastian Pfab, Florian Rezabek, Filip Rivera, Esteban Sagmeister, Simon Seidlitz, Leander Sauerbeck, Florian Tahiraj, Ilir Trauth, Rainer Uhlemann, Nico Würsching, Gerald Zarrouki, Baha Althoff, Matthias Betz, Johannes Bengler, Klaus Carle, Georg Diermeyer, Frank Ott, Jörg Lienkamp, Markus |
| author_facet | Karle, Phillip Betz, Tobias Bosk, Marcin Fent, Felix Gehrke, Nils Geisslinger, Maximilian Gressenbuch, Luis Hafemann, Philipp Huber, Sebastian Hübner, Maximilian Huch, Sebastian Kaljavesi, Gemb Kerbl, Tobias Kulmer, Dominik Mascetta, Tobias Maierhofer, Sebastian Pfab, Florian Rezabek, Filip Rivera, Esteban Sagmeister, Simon Seidlitz, Leander Sauerbeck, Florian Tahiraj, Ilir Trauth, Rainer Uhlemann, Nico Würsching, Gerald Zarrouki, Baha Althoff, Matthias Betz, Johannes Bengler, Klaus Carle, Georg Diermeyer, Frank Ott, Jörg Lienkamp, Markus |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_15492 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | EDGAR: An Autonomous Driving Research Platform -- From Feature Development to Real-World Application Karle, Phillip Betz, Tobias Bosk, Marcin Fent, Felix Gehrke, Nils Geisslinger, Maximilian Gressenbuch, Luis Hafemann, Philipp Huber, Sebastian Hübner, Maximilian Huch, Sebastian Kaljavesi, Gemb Kerbl, Tobias Kulmer, Dominik Mascetta, Tobias Maierhofer, Sebastian Pfab, Florian Rezabek, Filip Rivera, Esteban Sagmeister, Simon Seidlitz, Leander Sauerbeck, Florian Tahiraj, Ilir Trauth, Rainer Uhlemann, Nico Würsching, Gerald Zarrouki, Baha Althoff, Matthias Betz, Johannes Bengler, Klaus Carle, Georg Diermeyer, Frank Ott, Jörg Lienkamp, Markus Robotics 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. |
| title | EDGAR: An Autonomous Driving Research Platform -- From Feature Development to Real-World Application |
| topic | Robotics |
| url | https://arxiv.org/abs/2309.15492 |