@techreport{LoefflerGlogerTolksdorfetal.2021, author = {L{\"o}ffler, Christian and Gloger, Timm and Tolksdorf, Leon and Weihmayr, Daniel and Vogl, Carina and Watanabe, Hiroki and Stoll, Johann and Labenski, Volker and Koebe, Markus}, title = {D3.2 Demo 2 Vehicle demonstrator for object detection in adverse weather conditions}, url = {https://www.safe-up.eu/resources}, pages = {25}, year = {2021}, language = {en} } @techreport{MensaWimmerSchoriesetal.2021, author = {Mensa, Gen{\´i}s and Wimmer, Peter and Schories, Lars and B{\´a}lint, Andr{\´a}s and Djukic, Tamara and {\"O}stling, Martin and L{\"o}ffler, Christian and Nikolaou, Stella and Rial, Sergio and Merdivan, David and Birkner, Christian and Weihmayr, Daniel and Kirschbichler, Stefan}, title = {D5.1 Requirements for impact assessment}, pages = {44}, year = {2021}, language = {en} } @techreport{LoefflerGlogerSilvasetal.2021, author = {L{\"o}ffler, Christian and Gloger, Timm and Silvas, Emilia and Mu{\~n}oz S{\´a}nchez, Manuel and Tolksdorf, Leon and Weihmayr, Daniel and Labenski, Volker and Koebe, Markus and Stoll, Johann and Vogl, Carina and Watanabe, Hiroki and Smit, Robin}, title = {D3.3 Vehicle demonstrator for trajectory planning and control for combined automatic emergency braking and steering manoeuvres including system for VRU detection, motion planning and trajectory control to enhance real world performance}, url = {https://www.safe-up.eu/resources}, pages = {45}, year = {2021}, language = {en} } @inproceedings{WeihmayrSezginTolksdorfetal.2024, author = {Weihmayr, Daniel and Sezgin, Fatih and Tolksdorf, Leon and Birkner, Christian and Jazar, Reza}, title = {Predicting the Influence of Adverse Weather on Pedestrian Detection with Automotive Radar and Lidar Sensors}, booktitle = {2024 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-4881-1}, doi = {https://doi.org/10.1109/IV55156.2024.10588472}, pages = {2591 -- 2597}, year = {2024}, language = {en} } @techreport{TolksdorfSchoriesCastellsetal.2023, author = {Tolksdorf, Leon and Schories, Lars and Castells, Jacint and Gragopoulos, Ioannis and Karoui, Mouna and Weihmayr, Daniel and Smit, Robin and Gloger, Timm and Mayrargue, Sylvie and Stoll, Johann and Blum, Kristin and Munoz Sanchez, Manuel and Mannoni, Valerian and Denis, Benoit}, title = {D3.8 Verification Report for Demos 2, 3 and 4}, url = {https://www.safe-up.eu/deliverables}, pages = {111}, year = {2023}, language = {en} } @techreport{LoefflerVoglLabenskietal.2022, author = {L{\"o}ffler, Christian and Vogl, Carina and Labenski, Volker and Weihmayr, Daniel and Gloger, Timm and Schwaderer, Till and Firmbach, Markus and Stoll, Johann and Blum, Kristin and Koebe, Markus}, title = {D3.5 Demo 2 Vehicle demonstrator for object detection in adverse weather conditions update}, url = {https://www.safe-up.eu/deliverables}, pages = {59}, year = {2022}, language = {en} } @techreport{KovacevaFlannaganKolketal.2023, author = {Kovaceva, Jordanka and Flannagan, Carol and Kolk, Harald and Wimmer, Peter and Schories, Lars and Ramasamy, Ellankavi and Becker, Julian and L{\"o}ffler, Christian and Lorente Mallada, Jorge and Weihmayr, Daniel and Castells, Jacint and Davidsson, Johan}, title = {D5.8 Safety Impact Assessment-Updated Report}, url = {https://www.safe-up.eu/deliverables}, pages = {84}, year = {2023}, language = {en} } @article{WeihmayrBirknerMarzbanietal.2025, author = {Weihmayr, Daniel and Birkner, Christian and Marzbani, Hormoz and Jazar, Reza}, title = {Data-Driven Vehicle Dynamics: Lever-Aging SINDy for Optimization-Based Vehicular Motion Planning}, volume = {13}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York}, issn = {2169-3536}, doi = {https://doi.org/10.1109/ACCESS.2025.3594892}, pages = {136584 -- 136597}, year = {2025}, abstract = {Motion planning remains a crucial challenge for the widespread adoption of autonomous vehicles. This paper presents a novel approach that integrates an empirical plant model within an optimization-based motion planning architecture. The model prioritizes performance and efficiency while maintaining interpretability. We introduce a methodology that utilizes a data-driven approach to derive an interpretable description of the evolution of vehicle states over time using sparse regression. This method allows effective learning from limited datasets, eliminating the need for extensive and expensive data collection. Our approach addresses the trade-off between performance and accuracy, enabling adaptation to diverse driving scenarios. We affirm the efficacy of our methodology via an extensive analysis, evaluating the independent prediction performance across diverse metrics. Additionally, we examine the overall tracking performance when incorporated into an optimization-based framework. Finally, we present a comparative analysis and discuss the subsequent impact on overall motion planning and decision-making in relation to a state-of-the-art single-track model.}, language = {en} }