{"id":4556,"date":"2023-07-31T11:17:58","date_gmt":"2023-07-31T11:17:58","guid":{"rendered":"https:\/\/safetrain-projekt.de\/?page_id=4556"},"modified":"2025-01-23T08:06:52","modified_gmt":"2025-01-23T08:06:52","slug":"publications","status":"publish","type":"page","link":"https:\/\/safetrain-projekt.de\/en\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"[vc_row type=”full_width_background” full_screen_row_position=”middle” column_margin=”20px” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” bg_color=”#000028″ bg_image=”4271″ bg_position=”center center” background_image_loading=”default” bg_repeat=”no-repeat” scene_position=”center” top_padding=”200″ bottom_padding=”200″ text_color=”light” text_align=”center” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” row_name=”Intro” enable_gradient=”true” color_overlay=”#0a0a0a” color_overlay_2=”#565656″ advanced_gradient_angle=”0″ 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shape_divider_position=”bottom” bg_image_animation=”none” gradient_type=”default” shape_type=””][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][tabbed_section style=”minimal_flexible” tab_color=”Accent-Color” vs_content_animation=”fade” vs_link_animation=”opacity” vs_navigation_alignment=”left” vs_navigation_width_2=”25%” vs_navigation_func=”default” vs_navigation_width=”regular” vs_navigation_spacing=”15px” vs_navigation_mobile_display=”visible” vs_tab_spacing=”5%” icon_size=”24″][tab icon_family=”none” title=”Publications” id=”1737619585528-8″ tab_id=”1737619585530-1″][vc_column_text css=”” text_direction=”default”]2025<\/strong><\/em><\/p>\n

Schnitzer, R., Kilian, L., Roessner, S., Theodorou, K., & Zillner, S. (2025). Landscape of AI safety concerns – A methodology to support safety assurance for AI-based autonomous systems. In proceedings of the 8th International Conference on System Reliability and Safety (ICSRS), preprint available<\/a><\/p>\n

2024<\/strong><\/em><\/p>\n

Ronecker, M. P., Diaz, X., Karner, M., & Watzenig, D. (2024). Deep Learning-Driven State Correction: A Hybrid Architecture for Radar-Based Dynamic Occupancy Grid Mapping. In 35th IEEE Intelligent Vehicles Symposium, IV 2024 (pp. 2184-2191). Institute of Electrical and Electronics Engineers. Link<\/a><\/p>\n

Zeller, M. et al. (2024). Continuous Development and Safety Assurance Pipeline for ML-Based Systems in the Railway Domain. In: Ceccarelli, A., Trapp, M., Bondavalli, A., Schoitsch, E., Gallina, B., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2024 Workshops. SAFECOMP 2024. Lecture Notes in Computer Science, vol 14989. Springer, Cham. Link<\/a><\/p>\n

Decker, T., Bhattarai, A.R., Gu, J., Tresp, V. & Buettner, F.. (2024). Provably Better Explanations with Optimized Aggregation of Feature Attributions. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:10267-10286, Link<\/a><\/p>\n

Otte, C.,\u00a0 Yang, Y., Oswan D. (2024), “Data-driven Verification of DNNs for Object Recognition”, Link<\/a><\/p>\n

Neumann, F., Deroo, F., Wichert, G., & Burschka, D. (2024). “Particle-based Dynamic Semantic Occupancy Mapping using Bayesian Generalized Kernel Inference.”, In\u00a0Proceedings of the 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)\u00a0.<\/p>\n

Bahlmann et al., “Vertrauen in KI basierte Mobilit\u00e4t – Technologische und ethische Aspekte”, Plattform Lernende Systeme, 2024, Link<\/a><\/p>\n

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<\/a><\/p>\n

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 \u2013 Software Engineering for AI (CAIN 2024), 2024.<\/p>\n

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<\/p>\n

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<\/a><\/p>\n

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<\/p>\n

2023<\/strong><\/em><\/p>\n

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<\/strong>. https:\/\/arxiv.org\/abs\/2310.16727<\/a><\/p>\n

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<\/a><\/p>\n

Weiss, G. (2023) “How autonomous systems become reality \u2013 Operational Design Domains for Highly Automated Functions of Embedded Systems”, In Proceedings of Embedded Software Engineering (ESE) Kongress 2023.<\/p>\n

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<\/p>\n

Thirugnana Sambandham, V., Kirchheim, K., Ortmeier, F. (2023). Evaluating and\u00a0Increasing Segmentation Robustness in\u00a0CARLA. 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<\/a><\/p>\n

Kirchheim, K. (2023). Towards Deep Anomaly Detection with\u00a0Structured 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<\/a><\/p>\n

Decker, T., Bhattarai, A.R., Lebacher, M. (2023). Towards Scenario-Based Safety Validation for\u00a0Autonomous Trains with\u00a0Deep 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<\/a><\/p>\n

Schwaiger, F., Matic, A., Roscher, K., & G\u00fcnnemann, 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<\/a><\/p>\n

Zeller, M., Rothfelder, M., & Klein, C. (2023, May). safe. trAIn\u2013Engineering and Assurance of a Driverless Regional Train. In\u00a02023 IEEE\/ACM 2nd International Conference on AI Engineering\u2013Software Engineering for AI (CAIN)\u00a0(pp. 197-197). IEEE. https:\/\/doi.org\/10.1109\/CAIN58948.2023.00036<\/a><\/p>\n

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<\/a><\/p>\n

Zeller, M., Sorokos, I., Reich, J., Adler, R., & Schneider, D. (2023, January). Open Dependability Exchange Metamodel: A Format to Exchange Safety Information. In\u00a02023 Annual Reliability and Maintainability Symposium (RAMS)\u00a0(pp. 1-7). IEEE.<\/p>\n

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<\/a><\/p>\n

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<\/a><\/p>\n

Geerkens, S., Sieberichs, C., Braun, A., Waschulzik, T. (2023). QI\u00b2 – an Interactive Tool for Data Quality Assurance. arXiv arXiv:2307.03419 Link: https:\/\/arxiv.org\/abs\/2307.03419<\/a><\/p>\n

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<\/a><\/p>\n

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<\/a><\/p>\n

2022<\/strong><\/em><\/p>\n

Kirchheim K., Ortmeier F. (2022) “On Outlier Exposure with Generative Models”, NeurIPS ML Safety Workshop, 2022, https:\/\/openreview.net\/forum?id=SU7OAfhc8OM<\/a><\/p>\n

Zeller, M. (2022). Component Fault and\u00a0Deficiency Tree (CFDT): Combining Functional Safety and\u00a0SOTIF 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<\/a><\/p>\n

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.<\/p>\n

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.[\/vc_column_text][\/tab][tab icon_family=”none” title=”Press” id=”1737619585571-10″ tab_id=”1737619585572-10″][vc_column_text]2022<\/strong><\/em><\/p>\n

06.10.2022 – safe.trAIn: Forschungsprojekt f\u00fcr fahrerlosen Regionalverkehr<\/a><\/p>\n

29.09.2022 –\u00a0Siemens and partners launch \u2018safe.trAIn\u2019 research project<\/a><\/p>\n

29.09.2022 – safe.trAIn Project to Advance Development of AI-Enabled Automated Trains<\/a><\/p>\n[\/vc_column_text][\/tab][\/tabbed_section][\/vc_column][\/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none”][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][vc_row_inner column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” text_align=”left” row_position=”default” row_position_tablet=”inherit” row_position_phone=”inherit” overflow=”visible” pointer_events=”all”][vc_column_inner column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” overflow=”visible” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][nectar_global_section id=”4207″][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row]\n","protected":false},"excerpt":{"rendered":"

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