TY - JOUR A1 - Heizmann, Michael A1 - Braun, Alexander A1 - Glitzner, Markus A1 - Günther, Matthias A1 - Hasna, Günther A1 - Klüver, Christina A1 - Krooß, Jakob A1 - Marquardt, Erik A1 - Overdick, Michael A1 - Ulrich, Markus T1 - Implementing machine learning: chances and challenges JF - at - Automatisierungstechnik Y1 - 2022 U6 - https://doi.org/10.1515/auto-2021-0149 SN - 2196-677X VL - 70 IS - 1 SP - 90 EP - 101 PB - De Gruyter CY - Berlin ER - TY - JOUR A1 - Heizmann, Michael A1 - Braun, Alexander A1 - Hüttel, Markus A1 - Klüver, Christina A1 - Marquardt, Erik A1 - Overdick, Michael A1 - Ulrich, Markus T1 - Artificial intelligence with neural networks in optical measurement and inspection systems JF - at - Automatisierungstechnik Y1 - 2020 U6 - https://doi.org/10.1515/auto-2020-0006 VL - 68 IS - 6 SP - 477 EP - 487 PB - De Gruyter ER - TY - RPRT A1 - Reims, Nancy A1 - Tophoven, Silke A1 - Tisch, Anita A1 - Jantzsch, Robert A1 - Nivorožkin, Anton M. A1 - Köhler, Markus A1 - Rauch, Angela A1 - Thomsen, Ulrich T1 - Aufbau und Analyse des LTA-Rehaprozessdatenpanels: Eine Prozessdatenbasis zur Untersuchung beruflicher Rehabilitation in Trägerschaft der Bundesagentur für Arbeit ; Modul 1 des Projekts "Evaluation von Leistungen zur Teilhabe behinderter Menschen am Arbeitsleben" Y1 - 2018 UR - http://www.bmas.de/DE/Service/Medien/Publikationen/Forschungsberichte/Forschungsberichte-Teilhabe/fb503-aufbau-und-analyse-des-lta-rehaprozessdatenpanels.html VL - Forschungsbericht IS - 503 PB - Bundesministerium für Arbeit und Soziales CY - Berlin ER - TY - JOUR A1 - Wolf, Dominik Werner A1 - Ulrich, Markus A1 - Braun, Alexander T1 - Novel developments of refractive power measurement techniques in the automotive world JF - Metrologia N2 - Refractive power measurements serve as the primary quality standard in the automotive glazing industry. In the light of autonomous driving new optical metrics are becoming more and more popular for specifying optical quality requirements for the windshield. Nevertheless, the link between those quantities and the refractive power needs to be established in order to ensure a holistic requirement profile for the windshield. As a consequence, traceable high-resolution refractive power measurements are still required for the glass quality assessment. Standard measurement systems using Moiré patterns for refractive power monitoring in the automotive industry are highly resolution limited, wherefore they are insufficient for evaluating the camera window area. Consequently, there is a need for more sophisticated refractive power measurement systems that provide a higher spatial resolution. In addition, a calibration procedure has to be developed in order to guarantee for comparability of the measurement results. For increasing the resolution, a measurement setup based on an auto-correlation algorithm is tested in this paper. Furthermore, a calibration procedure is established by using a single reference lens with a nominal refractive power of 100 km-1. For the calibration of the entire measurement range of the system, the lens is tilted by an inclination angle orthogonal to the optical axis. The effective refractive power is then given by the Kerkhof model. By adopting the measurement and calibration procedure presented in this paper, glass suppliers in the automotive industry will be able to detect relevant manufacturing defects within the camera window area more accurately paving the way for a holistic quality assurance of the windshield for future advanced driver-assistance system (ADAS) functionalities. Concurrently, the traceability of the measurement results is ensured by establishing a calibration chain based on a single reference lens, which is traced back to international standards. Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-41484 UR - http://iopscience.iop.org/article/10.1088/1681-7575/acf1a4 SN - 1681-7575 VL - 60 IS - 064001 PB - IOP Publishing ER - TY - JOUR A1 - Wolf, Dominik Werner A1 - Thielbeer, Boris A1 - Ulrich, Markus A1 - Braun, Alexander T1 - Wavefront aberration measurements based on the Background Oriented Schlieren method JF - Measurement: Sensors N2 - Applications based on neural networks tend to be very sensitive to dataset shifts. Hence, the perception chain for autonomous driving is safeguarded against perturbations by imposing exaggerated optical quality requirements. Due to the non-linear coupling of optical elements, system requirements of camera-based Advanced Driver Assistance Systems (ADAS) can not be easily decomposed into individual part tolerances of the objective lens and the windscreen. This holds true for intensity-based part measurements, e.g. slanted edge measurements according to ISO12233, which can fundamentally not capture interference effects of the complex light field. Instead wavefront-based part measurements are required. Unfortunately, state-of-the-art wavefront measurement techniques are limited by the spanned sensitive area of the Shack-Hartmann lenslet array or the aperture stop of a corresponding interferometrical setup, respectively. Further, both measurements are limited by using collimated light only, whereas the target application has a large (angular) field of view, requiring many different measurements. We address those bottlenecks by proposing a novel wavefront aberration measurement procedure based on the Background Oriented Schlieren (BOS) method utilizing image auto-correlation. We analytically derive the governing equations for determining the Zernike coefficients of a wavefront aberration map in the knowledge of the local refractive power map obtained by a high-resolution BOS measurement. Furthermore, we experimentally demonstrate the feasibility of the measurement technique. Applying this novel method yields the promise of affordable wavefront aberration measurements only requiring a high-resolution camera and a sophisticated alignment strategy. KW - Wavefront aberration measurements KW - Deep optics KW - Dataset shifts KW - Data augmentation KW - Autonomous driving Y1 - 2024 U6 - https://doi.org/10.1016/j.measen.2024.101509 SN - 2665-9174 N1 - In Press, Corrected Proof PB - Elsevier ER - TY - INPR A1 - Wolf, Dominik Werner A1 - Balaji, Prasannavenkatesh A1 - Braun, Alexander A1 - Ulrich, Markus T1 - Decoupling of neural network calibration measures T2 - German Conference on Pattern Recognition (GCPR) 2024 N2 - A lot of effort is currently invested in safeguarding autonomous driving systems, which heavily rely on deep neural networks for computer vision. We investigate the coupling of different neural network calibration measures with a special focus on the Area Under the Sparsification Error curve (AUSE) metric. We elaborate on the well-known inconsistency in determining optimal calibration using the Expected Calibration Error (ECE) and we demonstrate similar issues for the AUSE, the Uncertainty Calibration Score (UCS), as well as the Uncertainty Calibration Error (UCE). We conclude that the current methodologies leave a degree of freedom, which prevents a unique model calibration for the homologation of safety-critical functionalities. Furthermore, we propose the AUSE as an indirect measure for the residual uncertainty, which is irreducible for a fixed network architecture and is driven by the stochasticity in the underlying data generation process (aleatoric contribution) as well as the limitation in the hypothesis space (epistemic contribution). Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-50104 PB - arXiv ER - TY - INPR A1 - Wolf, Dominik Werner A1 - Braun, Alexander A1 - Ulrich, Markus T1 - Optical aberrations in autonomous driving: Physics-informed parameterized temperature scaling for neural network uncertainty calibration T2 - International Journal of Computer Vision (IJCV) N2 - 'A trustworthy representation of uncertainty is desirable and should be considered as a key feature of any machine learning method' (Huellermeier and Waegeman, 2021). This conclusion of Huellermeier et al. underpins the importance of calibrated uncertainties. Since AI-based algorithms are heavily impacted by dataset shifts, the automotive industry needs to safeguard its system against all possible contingencies. One important but often neglected dataset shift is caused by optical aberrations induced by the windshield. For the verification of the perception system performance, requirements on the AI performance need to be translated into optical metrics by a bijective mapping (Braun, 2023). Given this bijective mapping it is evident that the optical system characteristics add additional information about the magnitude of the dataset shift. As a consequence, we propose to incorporate a physical inductive bias into the neural network calibration architecture to enhance the robustness and the trustworthiness of the AI target application, which we demonstrate by using a semantic segmentation task as an example. By utilizing the Zernike coefficient vector of the optical system as a physical prior we can significantly reduce the mean expected calibration error in case of optical aberrations. As a result, we pave the way for a trustworthy uncertainty representation and for a holistic verification strategy of the perception chain. Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-50120 PB - arXiv ER - TY - BOOK A1 - Marzi, Thomas A1 - Deerberg, Görge A1 - Dötsch, Christian A1 - Grevé, Anna A1 - Hiebel, Markus A1 - Kaluza, Stefan A1 - Knappertsbusch, Volker A1 - Maga, Daniel A1 - Müller, Torsten A1 - Pflaum, Hartmut A1 - Pohlig, Astrid A1 - Renner, Manfred A1 - Seifert, Ulrich A1 - Stießel, Sebastian A1 - Unger, Christoph A1 - Wack, Thorsten A1 - Weidner, Eckhard T1 - Kohlendioxid, Biomasse und Regenerativer Strom - Ressourcen einer neuen Kohlenstoffwirtschaft? Y1 - 2017 SN - 978-3-87468-358-6 U6 - https://doi.org/10.24406/UMSICHT-N-473349 VL - Umsicht-Diskurs IS - 1 PB - Fraunhofer-Institut für Umwelt-, Sicherheit- und Energietechnik CY - Oberhausen ER -