A Joint Approach Towards Data-Driven Virtual Testing for Automated Driving: The AVEAS Project
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| Format: | Preprint |
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2024
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| author | Eisemann, Leon Fehling-Kaschek, Mirjam Forkert, Silke Forster, Andreas Gommel, Henrik Guenther, Susanne Hammer, Stephan Hermann, David Klemp, Marvin Lickert, Benjamin Luettner, Florian Moss, Robin Neis, Nicole Pohle, Maria Schreiber, Dominik Sowa, Cathrina Stadler, Daniel Stompe, Janina Strobelt, Michael Unger, David Ziehn, Jens |
| author_facet | Eisemann, Leon Fehling-Kaschek, Mirjam Forkert, Silke Forster, Andreas Gommel, Henrik Guenther, Susanne Hammer, Stephan Hermann, David Klemp, Marvin Lickert, Benjamin Luettner, Florian Moss, Robin Neis, Nicole Pohle, Maria Schreiber, Dominik Sowa, Cathrina Stadler, Daniel Stompe, Janina Strobelt, Michael Unger, David Ziehn, Jens |
| contents | With growing complexity and responsibility of automated driving functions in road traffic and growing scope of their operational design domains, there is increasing demand for covering significant parts of development, validation, and verification via virtual environments and simulation models.
If, however, simulations are meant not only to augment real-world experiments, but to replace them, quantitative approaches are required that measure to what degree and under which preconditions simulation models adequately represent reality, and thus allow their usage for virtual testing of driving functions. Especially in research and development areas related to the safety impacts of the "open world", there is a significant shortage of real-world data to parametrize and/or validate simulations - especially with respect to the behavior of human traffic participants, whom automated vehicles will meet in mixed traffic.
This paper presents the intermediate results of the German AVEAS research project (www.aveas.org) which aims at developing methods and metrics for the harmonized, systematic, and scalable acquisition of real-world data for virtual verification and validation of advanced driver assistance systems and automated driving, and establishing an online database following the FAIR principles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_06286 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Joint Approach Towards Data-Driven Virtual Testing for Automated Driving: The AVEAS Project Eisemann, Leon Fehling-Kaschek, Mirjam Forkert, Silke Forster, Andreas Gommel, Henrik Guenther, Susanne Hammer, Stephan Hermann, David Klemp, Marvin Lickert, Benjamin Luettner, Florian Moss, Robin Neis, Nicole Pohle, Maria Schreiber, Dominik Sowa, Cathrina Stadler, Daniel Stompe, Janina Strobelt, Michael Unger, David Ziehn, Jens Robotics Computer Vision and Pattern Recognition Computers and Society Machine Learning Systems and Control With growing complexity and responsibility of automated driving functions in road traffic and growing scope of their operational design domains, there is increasing demand for covering significant parts of development, validation, and verification via virtual environments and simulation models. If, however, simulations are meant not only to augment real-world experiments, but to replace them, quantitative approaches are required that measure to what degree and under which preconditions simulation models adequately represent reality, and thus allow their usage for virtual testing of driving functions. Especially in research and development areas related to the safety impacts of the "open world", there is a significant shortage of real-world data to parametrize and/or validate simulations - especially with respect to the behavior of human traffic participants, whom automated vehicles will meet in mixed traffic. This paper presents the intermediate results of the German AVEAS research project (www.aveas.org) which aims at developing methods and metrics for the harmonized, systematic, and scalable acquisition of real-world data for virtual verification and validation of advanced driver assistance systems and automated driving, and establishing an online database following the FAIR principles. |
| title | A Joint Approach Towards Data-Driven Virtual Testing for Automated Driving: The AVEAS Project |
| topic | Robotics Computer Vision and Pattern Recognition Computers and Society Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2405.06286 |