2024
Gannamaneni, S. S., Klein, F., Mock, M., & Akila, M. (2024). Exploiting CLIP Self-Consistency to Automate Image Augmentation for Safety Critical Scenarios. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 3594-3604). Link
Weiss, G., Zeller, M. ,Schoenhaar, H., Kreutz, A., Drabek, C., „Approach for Argumenting Safety on Basis of an Operational Design Domain“, 3rd International Conference on AI Engineering – Software Engineering for AI (CAIN 2024), 2024.
Kreutz, A., Weiss. G., Trapp, M., „Automatic Deduction of the Impact of Context Variability on System Safety Goals „, In Proceedings of the 19th European Dependable Computing Conference, 8-11 April 2024, Leuven, Belgium
Article on safe.trAIn: „Besser unterwegs mit Bus und Bahn“., in Fraunhofer-Magazin 1/2024, April 2024. https://www.fraunhofer.de/s/ePaper/Magazin/2024/01/index.html#58
Kirchheim K., Gonschorek T., Ortmeier F.; „Out-of-Distribution Detection With Logical Reasoning“; In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 2122-2131
2023
Schnitzer, R., Hapfelmeier, A., Gaube, S., & Zillner, S. (2023). AI Hazard Management: A framework for the systematic management of root causes for AI risks. Springer 1st International Conference on Frontiers of Artificial Intelligence, Ethics, and Multidisciplinary Applications (FAIEMA 2023). Best Runner-Up Paper Award. https://arxiv.org/abs/2310.16727
M. Weber, P. Swazinna, D. Hein, S. Udluft and V. Sterzing, „Learning Control Policies for Variable Objectives from Offline Data,“ 2023 IEEE Symposium Series on Computational Intelligence (SSCI), Mexico City, Mexico, 2023, pp. 1674-1681, https://ieeexplore.ieee.org/document/10371978
Weiss, G. (2023) „How autonomous systems become reality – Operational Design Domains for Highly Automated Functions of Embedded Systems“, In Proceedings of Embedded Software Engineering (ESE) Kongress 2023.
M. Zeller, T. Waschulzik, M. Rothfelder and C. Klein, „Safety Assurance of a Driverless Regional Train -Insight in the safe.trAIn Project,“ in 2023 IEEE 34th International Symposium on Software Reliability Engineering Workshops (ISSREW), Florence, Italy, 2023 pp. 41-42. doi: 10.1109/ISSREW60843.2023.00043
Thirugnana Sambandham, V., Kirchheim, K., Ortmeier, F. (2023). Evaluating and Increasing Segmentation Robustness in CARLA. In: Guiochet, J., Tonetta, S., Schoitsch, E., Roy, M., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2023 Workshops. SAFECOMP 2023. Lecture Notes in Computer Science, vol 14182. Springer, Cham. https://doi.org/10.1007/978-3-031-40953-0_33
Kirchheim, K. (2023). Towards Deep Anomaly Detection with Structured Knowledge Representations. In: Guiochet, J., Tonetta, S., Schoitsch, E., Roy, M., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2023 Workshops. SAFECOMP 2023. Lecture Notes in Computer Science, vol 14182. Springer, Cham. https://doi.org/10.1007/978-3-031-40953-0_32
Decker, T., Bhattarai, A.R., Lebacher, M. (2023). Towards Scenario-Based Safety Validation for Autonomous Trains with Deep Generative Models. In: Guiochet, J., Tonetta, S., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2023. Lecture Notes in Computer Science, vol 14181. Springer, Cham. https://doi.org/10.1007/978-3-031-40923-3_20
Schwaiger, F., Matic, A., Roscher, K., & Günnemann, S. (2023). Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework. arXiv preprint arXiv:2307.04533. Link: https://arxiv.org/abs/2307.04533
Zeller, M., Rothfelder, M., & Klein, C. (2023, May). safe. trAIn–Engineering and Assurance of a Driverless Regional Train. In 2023 IEEE/ACM 2nd International Conference on AI Engineering–Software Engineering for AI (CAIN) (pp. 197-197). IEEE. https://doi.org/10.1109/CAIN58948.2023.00036
Koenig, A., Schambach M., Otterbach J. (2023). Uncovering the Inner Workings of STEGO for Safe Unsupervised Semantic Segmentation. In: Workshop on Safe Artificial Intelligence for All Domains at IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR). https://arxiv.org/abs/2304.07314
Zeller, M., Sorokos, I., Reich, J., Adler, R., & Schneider, D. (2023, January). Open Dependability Exchange Metamodel: A Format to Exchange Safety Information. In 2023 Annual Reliability and Maintainability Symposium (RAMS) (pp. 1-7). IEEE.
Zeller, M., Waschulzik, T., Schmid, R., & Bahlmann, C. (2023). Towards a safe MLOps Process for the Continuous Development and Safety Assurance of ML-based Systems in the Railway Domain. arXiv arXiv:2307.02867 Link: https://arxiv.org/abs/2307.02867
Gannamaneni, S. S., Sadaghiani, A., Rao, R. P., Mock, M., & Akila, M. (2023). Investigating CLIP Performance for Meta-data Generation in AD Datasets. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 3839-3849). Link
Geerkens, S., Sieberichs, C., Braun, A., Waschulzik, T. (2023). QI² – an Interactive Tool for Data Quality Assurance. arXiv arXiv:2307.03419 Link: https://arxiv.org/abs/2307.03419
Sieberichs, C., Geerkens, S., Braun, A., Waschulzik, T. (2023). ECS – an Interactive Tool for Data Quality Assurance. arXiv arXiv:2307.04368 Link: https://arxiv.org/abs/2307.04368
Gannamaneni, S. S., Mock, M., & Akila, M. (2023). Assessing Systematic Weaknesses of DNNs using Counterfactuals. arXiv preprint arXiv:2308.01614 Link: https://arxiv.org/abs/2308.01614
2022
Kirchheim K., Ortmeier F. (2022) „On Outlier Exposure with Generative Models“, NeurIPS ML Safety Workshop, 2022, https://openreview.net/forum?id=SU7OAfhc8OM
Zeller, M. (2022). Component Fault and Deficiency Tree (CFDT): Combining Functional Safety and SOTIF Analysis. In: Seguin, C., Zeller, M., Prosvirnova, T. (eds) Model-Based Safety and Assessment. IMBSA 2022. Lecture Notes in Computer Science, vol 13525. Springer, Cham. https://doi.org/10.1007/978-3-031-15842-1_11
Schleiss, P., Carella, F., & Kurzidem, I. (2022, November). Towards continuous safety assurance for autonomous systems. In 2022 6th International Conference on System Reliability and Safety (ICSRS) (pp. 457-462). IEEE.
Schleiss, P., Hagiwara, Y., Kurzidem, I., & Carella, F. (2022, October). Towards the quantitative verification of deep learning for safe perception. In 2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) (pp. 208-215). IEEE.
