Veröffentlichungen

Nachstehend sind wissenschaftliche Beiträge und Veröffentlichungen aus dem Projekt KI Absicherung aufgeführt:

1. Matthias Rottmann, Robin Chan, Peter Schlicht, Fabian Hüger: Detection of False Positive and False Negative Samples in Semantic Segmentation. In: DATE2020, Proceedings DATE 2020, Grenoble, 9.-13.03.2020
2. Marco Hoffman, Dr. Alexander Pohl, Patrick Prill, Dr. Michael Mlynarski: Die Gefahren lauern vor allem hinter den Ecken – Corner Cases und ihre Tücken. In: German Testing Magazin, April 2020
3. Timo Sämann, Peter Schlicht, Fabian Hüger: Strategy to Increase the Safety of a DNN-based Perception for HAD Systems. In: arXiv preprint 20.02.2020
4. Andreas Bär, Marvin Klingner, Serin Varghese, Fabian Hüger, Peter Schlicht, Tim Fingscheidt: Robust Semantic Segmentation by Redundant Networks With a Layer-Specific Loss Contribution and Majority Vote. In: Proc. of CVPR - Workshop on Safe Artificial Intelligence for Automated Driving (CVPR SAIAD 2020), Seattle, WA, USA, Juni 2020
5. Christoph Gladisch, Christian Heinzemann, Martin Herrmann, Matthias Woehrle: Leveraging combinatorial testing for safety-critical computer vision datasets. In: Workshop on Safe Artificial Intelligence for Automated Driving (SAIAD) 2020, Seattle (USA), 14.06.2020
6. Oliver Grau, Korbinian Hagn, Qutub Syed Sha: Computational validation of perceptional functions. In: Safe AI for automated Driving – IEEE Computer Society Conference on  Computer Vision and Pattern Recognition (CVPR), Proceedings of Computer Vision and Pattern Recognition – Workshop, Seattle (USA), 14. – 19.06.2020
7. Fabian Küppers, Jan Kronenberger, Amirhossein Shantia, Anselm Haselhoff: Multivariate Confidence Calibration for Object Detection. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle (USA), 16. – 18.06.2020
8. Jonas Löhdefink, Justin Fehrling, Marvin Klingner, Fabian Hüger, Peter Schlicht, Nico M. Schmidt, Tim Fingscheidt: Self-Supervised Domain Mismatch Estimation for Autonomous Perception. In: Workshop on Safe Artificial Intelligence for Automated Driving (SAIAD) 2020, Seattle (USA), 14.06.2020
9. Serin Varghese, Yasin Bayzidi, Andreas Bär, Nikhil Kapoor, Sounak Lahiri, Jan David Schneider, Nico Schmidt, Peter Schlicht, Fabian Hüger, Tim Fingscheidt: Unsupervised Temporal Consistency Metric for Video Segmentation in Highly-Automated Driving. In: Proc. of CVPR - Workshop on Safe Artificial Intelligence for Automated Driving (CVPR SAIAD 2020), Seattle, WA, USA, Juni 2020
10. Joachim Sicking, Maram Akila, Tim Wirtz, Sebastian Houben, Asja Fischer: Characteristics of Monte Carlo Dropout in Wide Neural Networks. In Workshop on Uncertainty & Robustness in Deep Learning (at ICML); Wien (Österreich), 17.07.2020
11. Michael Fürst, Emil Schreiber: KIA – Annotations Format (V2.1) Design Process and Decisions. In: OpenLABEL Project Meeting, online, 23. – 24.07.2020
12. Stephanie Abrecht, Lydia Gauerhof, Christoph Gladisch, Konrad Groh, Christian Heinzemann, Matthias Woehrle: Testing Deep Learning-based Visual Perception for Automated Driving. In: Journal ACM Transactions on Cyber-Physical Systems, Speical Issue on Artificial Intelligence and Cyber-Physical Systems
13. Michael Fürst, Oliver Wasenmüller, Didier Stricker: LRPD: Long Range 3D Pedestrian Detection Leveraging Specific Strengths of LiDAR and RGB. In IEEE International Conference on Intelligent Transportation Systems, Rhodes (Griechenland), 20. – 23.09.2020
14. Juncong Fei, Wenbo Chen, Philipp Heidenreich, Sascha Wirges, Christoph Stiller: Semantic Voxels: Sequential Fusion for 3D PedestrianDetection using LiDAR Point Cloud and Semantic Segmentation. In: IEEE International Conference on Multisensor Fusion and Integration, Karlsruhe (Deutschland), 14. – 16.09.2020
15. Oliver Willers, Sebastian Sudholt, Shervin Raafatnia, Stephanie Abrecht: Safety Concerns and Mitigation Approaches Regarding the Use of Deep Learning in Safety-Critical Perception Tasks. In: SAFECOMP 2020, Lecture Notes on Computer Science Lissabon (Portugal), 15. – 18.09.2020
16. Stephanie Abrecht, Maram Akila, Sujan Sai Gannamaneni, Konrad Groh, Christian Heinzemann, Sebastian Houben, Matthias Woehrle: Revisiting Neuron Coverage and its Application to Test Generation. In: Third International Workshop on Artificial Intelligence Safety Engineering, Computer Safety, Reliability and Security: SAFECOMP 2020 Worshops, Lissabon (Portugal), 15.09.2020
17. Gesina Schwalbe, Bernhard Knie, Timo Sämann, Timo Dobberphul, Lydia Gauerhof, Shervin Raaftnia, Oliver Willers: Structuring the Safety Argumentation for Deep Neural Networks. In: SafeComp 2020, Computer Safety, Reliability and Security, Lissabon (Portugal), 15. – 18.09.2020
18. Michael Weber, Christof Wendenius, J. Marius Zöllner: Runtime Optimization of a CNN for Environment Perception. In: IEEE Intelligent Vehicles Symposium (IV) 2020, Proceedings of  the IEEE Intelligent Vehicles Symposium, Las Vegas (USA), 21.-23.10.2020
19. Peter Nöst, Korbinian Hagn, Oliver Grau: Characterizing Data Sets for training and validation in automated driving. In: 4. ACM Computer Science in Cars Symposium (CSCS 2020), Ingolstadt (online), 02.12.2020
20. Korbinian Hagn, Oliver Grau: Increasing realism of synthetic datasets through additive sensor and lens artefacts. In: 4. ACM Computer Science in Cars Symposium (CSCS 2020), Ingolstadt (online), 02.12.2020
21. Nikhil Kapoor, Chun Yuan, Serin Varghese, Jonas Löhdefink, Roland Zimmermann, Serin Varghese, Fabian Hüger, Nico Schmidt, Peter Schlicht, Tim Fingscheidt: A Self-Supervised Feature Map Augmentation (FMA) Loss and Combined Augmentations to Efficiently Improve the Robustness of CNNs. In: 4. ACM Computer Science in Cars Symposium (CSCS 2020), Ingolstadt (online), 02.12.2020
22. Qutub Syed Sha, Oliver Grau, Korbinian Hagn: DNN Analysis through Synthetic Data Variation. In: 4. ACM Computer Science in Cars Symposium (CSCS 2020), Ingolstadt (online), 02.12.2020
23. Michael Fürst, Shriya T.P. Gupta, René Schuster, Oliver Wasenmüller, Didier Stricker: HPERL: 3D Human Pose Estimation from RGB and LiDAR. In: 25th IEEE International Conference on Pattern Recognition, Milan, Italy (online), 10.-13.01.2021
24. Timo Sämann, Horst-Michael Gross: Online Out-of-Domain Detection for Automated Driving. In: Machine Learning in Certified Systems Workshop (https://mlcertifiedsystems.deel.ai/), 14.-15.01.2021
25. Sebastian Houben, Stephanie Abrecht, Maram Akila, Andreas Bär, Felix Brockherde, Patrick Feifel, Tim Fingscheidt, Sujan Sai Gannamaneni, Seyed Eghbal Ghobadi, Ahmed Hammam, Anselm Haselhoff, Felix Hauser, Christian Heinzemann, Marco Hoffmann, Nikhil Kapoor, Falk Kappel, Marvin Klingner, Jan Kronenberger, Fabian Küppers, Jonas Löhdefink, Michael Mlynarski, Michael Mock, Firas Mualla, Svetlana Pavlitskaya, Maximilian Poretschkin, Alexander Pohl, Varun Ravi-Kumar, Julia Rosenzweig, Matthias Rottmann, Stefan Rüping, Timo Sämann, Jan David Schneider, Elena Schulz, Gesina Schwalbe, Joachim Sicking, Toshika Srivastava, Serin Varghese, Michael Weber, Sebastian Wirkert, Tim Wirtz, and Matthias Woehrle: Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety. KI Absicherung 2020
26. Michael Mock, Fraunhofer IAIS, Stephan Scholz, Volkswagen AG, Loren Schwarz, BMW AG, Thomas Stauner, BMW AG, Fabian Hüger, Volkswagen AG, Frédérik Blank, Robert Bosch GmbH, Andreas Rohatschek, Robert Bosch GmbH, KI-Absicherung: Proof of Project Concept conducted, 01.04.202!
27. Andreas Blattmann, Timo Milbich, Michael Dorkenwald, Björn Ommer, Heidelberg Collaboratory for Image Processing, IWR- Heidelberg University Germany: Behavior-Driven Synthesis of Human Dynamics, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (Home | CVPR 2021 (thecvf.com), 19-25.06.2021
28. Andreas Blattmann, Timo Milbich, Michael Dorkenwald, Björn Ommer, Interdisciplinary Center for Scientific Computing, HCI- Heidelberg University Germany: Understanding Object Dynamics for Interactive Image-to-Video Synthesis, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (Home | CVPR 2021 (thecvf.com), 19-25.06.2021
29. Anselm Haselhoff , Jan Kronenberger, Fabian Kuppers, Jonas Schneider, Ruhr West University of Applied Sciences Bottrop, Germany & Elektronische Fahrwerksysteme GmbH, Gaimersheim, Germany: Towards Black-Box Explainability with Gaussian Discriminant Knowledge Distillation, SAIAD Workshop at IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (Home | CVPR 2021 (thecvf.com), 19-25.06.2021
30. Patrick Feifel, Frank Bonarens, Frank Koster, Stellantis, Opel Automobile GmbH, Carl von Ossietzky Universitat Oldenburg, Deutsches Zentrum fur Luft- und Raumfahrt, Germany: Reevaluating the Safety Impact of Inherent Interpretability on Deep Neural Networks for Pedestrian Detection, SAIAD Workshop at IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (Home | CVPR 2021 (thecvf.com), 19-25.06.2021
31. Julia Rosenzweig , Joachim Sicking, Sebastian Houben, Michael Mock, Maram Akila, Fraunhofer IAIS, Fraunhofer Center for Machine Learning, Germany: Patch Shortcuts: Interpretable Proxy Models Efficiently Find Black-Box Vulnerabilities, SAIAD Workshop at IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (Home | CVPR 2021 (thecvf.com), 19-25.06.2021
32. Serin Varghese, Sharat Gujamagadi, Marvin Klingner, Nikhil Kapoor, Andreas Bar, Jan David Schneider, Kira Maag, Peter Schlicht, Fabian Huger, Tim Fingscheidt, Volkswagen Group Automation, University of Wuppertal, Technische Universitat Braunschweig, Germany: An Unsupervised Temporal Consistency (TC) Loss to Improve the Performance of Semantic Segmentation Networks, SAIAD Workshop at IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (Home | CVPR 2021 (thecvf.com), 19-25.06.2021