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Veröffentlichungen

2024

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

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.

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Das Projekt wird von Bundesministerium für Wirtschaft und Klimaschutz im Rahmen des Fachprogramms „Neue Fahrzeug- und Systemtechnologien“ gefördert.

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