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Phillipp Rigoll
- 10.09.2025
- Maneuver-based testing for the end-to-end creation, selection and execution of test scenarios for highly automated driving functions
- Group: Prof. Sax
- philipp rigoll ∂does-not-exist.fzi de
- www.fzi.de/team/philipp-rigoll/
Corrector: Prof. Dr.-Ing. Dr. h. c. Michael Weyrich (Universität Stuttgart)
Summary of the dissertation
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In his dissertation, Phillipp Rigoll examines the challenges of validating automated driving functions, particularly in complex urban traffic. Although automated driving offers great potential in terms of safety, comfort and mobility, the validation of the systems has not yet been adequately solved. As part of his work, he is developing a consistent, maneuver-based scenario description that uniformly supports all steps of the test process - from scenario creation to test case derivation and execution. This approach significantly improves the efficiency and systematics of validation and makes an important contribution to the practical validation of automated driving functions.
We congratulate Phillip Rigoll on this great achievement!
Data analysis and data mining
Driven by digitalization, data is now central to a wide variety of areas of life and business. They appear in the most diverse forms and characteristics. Data is being collected, processed and stored in more and more places. Data analysis is concerned with extracting valuable information from this data. The focus of data mining is on using statistical methods to find and describe patterns and hidden relationships in the data. At FZI/ITIV, we are researching to perform these analyses as agnostically as possible. The primary goal is the comprehensibility of the analyses and, associated with this, an understandable presentation of the results.
Augmentation with generating artificial neural networks.
Artificial neural networks, in addition to generalizing to unknown data, are capable of generating new data (the three images above were generated by text input using the stable diffusion architecture). In addition to purely artistic and creative creation, this approach also allows for the addition of missing data points in datasets. This data set augmentation is called augmentation. We at FZI/ITIV are researching the use of augmentation with generating artificial neural networks in the context of developing automated driving functions.
Machine learning
Building on data analysis and data mining and the associated understanding of the data, machine learning goes one step further. Here, algorithms learn the regularities of the data as a statistical model and can generalize to further data after a learning phase. Especially in the form of artificial neural networks, machine learning has proven its worth and is used, for example, for the prediction of time series, the detection of anomalies and object detection. At FZI/ITIV, we develop and investigate these methods, for example, in the context of automated driving.
Supervised Student Works
- BA: "Configuration of a pipeline for the augmentation of street images using machine learning"
- BA: " Feature analysis of automotive images augmented with Generative Adversarial Networks to evaluate their use as sample data"
- MA: "Evaluation of the applicability of augmentation using Generative Adversarial Networks in closed-loop integration testing of highly automated driving functions"
- MA: "Object-Based Latent Space Analysis to Obtain New Contexts"
Publications
Rigoll, P.
2025, October 22. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000185851
Reis, P.; Rigoll, P.; Sax, E.
2024. 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), Edmonton, AB, Canada, 24-27 September 2024, 1962–1967, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ITSC58415.2024.10920178
Rigoll, P.; Adolph, L.; Ries, L.; Sax, E.
2024. 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), Edmonton, AB, Canada, 24-27 September 2024, 2398–2404, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ITSC58415.2024.10920034
Rigoll, P.; Langner, J.; Ries, L.; Sax, E.
2024. 2024 IEEE Intelligent Vehicles Symposium (IV), Jeju Island, 2nd-5th June 2024, 1053–1059, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/IV55156.2024.10588869
Rigoll, P.; Petersen, P.; Stage, H.; Ries, L.; Sax, E.
2023. 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), 168–174, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ITSC57777.2023.10422271
Rigoll, P.; Petersen, P.; Ries, L.; Langner, J.; Sax, E.
2022. Fahrerassistenzsysteme und automatisiertes Fahren, 41–48, VDI Verlag. doi:10.51202/9783181023945-41
Rigoll, P.; Petersen, P.; Langner, J.; Sax, E.
2022. Advances in Systems Engineering : Proceedings of the 28th International Conference on Systems Engineering, ICSEng 2021, December 14-16, Wrocław, Poland. Ed.: L. Borzemski, 403–417, Springer International Publishing. doi:10.1007/978-3-030-92604-5_36
Rigoll, P.; Ries, L.; Sax, E.
2022. 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), 3139–3145, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ITSC55140.2022.9921868
Petersen, P.; Stage, H.; Langner, J.; Ries, L.; Rigoll, P.; Philipp Hohl, C.; Sax, E.
2022. 2022 IEEE International Symposium on Systems Engineering (ISSE), 1–8, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ISSE54508.2022.10005441
Ries, L.; Rigoll, P.; Braun, T.; Schulik, T.; Daube, J.; Sax, E.
2021. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 1251–1258, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ITSC48978.2021.9564636