2023
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.
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
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.
