Nachstehend sind wissenschaftliche Beiträge und Veröffentlichungen aus dem Projekt KI Absicherung aufgeführt:
- 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
- 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
- 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
- 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
- Christoph Gladisch, Christian Heinzemann, Martin Hermann, 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
- 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
- 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
- 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
- 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
- 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
- Michael Fürst, Emil Schreiber: KIA – Annotations Format (V2.1) Design Process and Decisions. In: OpenLABEL Project Meeting, online, 23. – 24.07.2020
- Stephanie Abrecht, Lydia Gauerhof, Christoph Gladisc, 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
Publikationen
publications
Nachfolgend finden Sie öffentlich gehaltene Präsentation zum Projekt KI Absicherung:
27.10.2020 - Fraunhofer Solution Days: Dr. Michael Mock, IAIS: Absicherung und Zertifizierung von KI
26.10.2020 - The Connected Car and Autonomous Driving: Dr. Sebastian Houben, IAIS: KI Absicherung - Safe AI for Automated Driving
05.10.2020 - TÜV AI Conference - Meet the Expert: Dr. Michael Mock, IAIS: Projektvorstellung KI Absicherung
26.06.2020 - XR EXPO 2020: Markus Huber, Mackevision Medien Design GmbH: Enabling Autonomous Driving Simulations through Virtual Worlds
Präsentationen
presentations
Hier stehen die folgenden Projektmaterialien zum Download bereit:
Projektmaterialien
project-material
Presse-Kit
Hier finden Sie zukünftig unser Presse-Kit.
Presse-Kit
press
Pressemitteilungen
Pressemitteilungen des Projektes und unserer Partner:
Pressemitteilungen
press
Medien Echo
Beiträge über KI Absicherung:
https://www.die-stadtzeitung.de/index.php/2019/09/06/bergische-uni-beteiligt-sich-an-ki-absicherung/
https://idw-online.de/de/news722994
https://www.uni-heidelberg.de/de/newsroom/kuenstliche-intelligenz-fuer-automatisiertes-fahren
https://www.iais.fraunhofer.de/de/presse/presseinformationen/presseinformationen-2020/presseinformation-200526.html (Official press release by IAIS)
https://idw-online.de/de/news747957
https://www.internationales-verkehrswesen.de/ki-absicherung-wie-autonomes-fahren-sicherer-wird/
https://www.intelligent-mobility-xperience.com/xxx-a-949073/ (English)