Publikationen

Im Rahmen des DZM-Projekts wurden verschiedene wissenschaftliche und praxisorientierte Publikationen erarbeitet, die die Ergebnisse und Entwicklungen der einzelnen Teilprojekte dokumentieren. Dazu zählen Beiträge in Fachveröffentlichungen, Konferenzbeiträge sowie projektbezogene Berichte, die sowohl den aktuellen Forschungsstand als auch die erzielten Fortschritte im Bereich digitaler und nachhaltiger Mobilitätslösungen im Bahnsektor abbilden. Die Publikationen dienen der fachlichen Einordnung der Projektergebnisse und fördern den Wissenstransfer zwischen Forschung, Industrie und Praxis.

• Aimiyekagbon, O. K., Löwen, A., Hanselle, R., Rief, T., Beck, M., & Sextro, W. (2025). Multilevel fault diagnostics for railway applications using limited historical data. PHM Society Asia-Pacific Conference, 5(1). https://doi.org/10.36001/phmap.2025.v5i1.4449

• Gamal, O., & Büker, U. (2026). A Comparative Study of Semantic Segmentation Models for Railway Scene Understanding Under Adverse Weather Conditions. 30th International Conference on System Theory, Control and Computing. 30th International Conference on System Theory, Control and Computing.

• Kelber, M., Brück, S., Bhardwaj, N., Aimiyekagbon, O. K., Naumann, R., & Sextro, W. (2026). Methodik zur Untersuchung der Fahrwerksparameter von Schienenfahrzeugen auf Basis optischer Schwingungsmessungen an einer ortsfesten Messstelle. In Hochschule für Technik und Wirtschaft Dresden, Fakultät Maschinenbau (Ed.), Tagungsband Rad-Schiene-Tagung 2026 (pp. 206–208). DVV Media Group GmbH – Eurailpress.

• Linneweber, J. M., Schultz, A. M., Müller, L., Aimiyekagbon, O. K., Mozgova, I., & Sextro, W. (2026). A Framework for the Integration of Hybrid Models in Digital Twin Architectures for PHM. PHM Society European Conference, 9(1), 1–13. https://doi.org/10.36001/phme.2026.v9i1.4877

• Lück, S., & Reinold, P. (2026). Eco-Driving for Connected and Autonomous Electric Vehicles in Urban and Suburban Environments. Automotive Meets Electronics & Control (AmEC 2026), 16th GMM/GMA Symposium, GMM-Technical Report.

• Mensendiek, C., Preuß, O. L., Rook, J., Chicano, F., Whitley, D., & Trautmann, H. (2026). Digging to the Ground Truth: Solving Multi-objective Gray-Box Optimization Problems through Hyperplane Elimination. Proceedings of the Genetic and Evolutionary Computation Conference, 134–142. https://doi.org/10.1145/3795095.3805136
  
  
• Mensendiek, C., Zeipel, H., Preuß, O. L., Seiler, M. V., Sextro, W., & Trautmann, H. (2026). Multi-Objective Pipeline Optimisation and Configuration of Automatic Train Operation Trajectories. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 641–644. https://doi.org/10.1145/3795101.3805368
  
• Niemann, C., Leins, D., Lach, L., & Haschke, R. (2024). Learning When to Stop: Efficient Active Tactile Perception with Deep Reinforcement Learning. 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 685–692. https://doi.org/10.1109/IROS58592.2024.10801966
  
  
• Reiling, F., Bause, M., Gröger, S., Henke, C., Koppert, S., & Trächtler, A. (2025). Development of a Learning-Based Control System for Robot-Assisted Grinding of Rubber Roller. 2025 11th International Conference on Automation, Robotics, and Applications (ICARA), 183–188. https://doi.org/10.1109/ICARA64554.2025.10977606

• Reiling, F., Henke, C., Hunstig, M., Gröger, S., & Trächtler, A. (2024). Batch constrained multi-objective Bayesian optimization using the example of ultrasonic wire bonding. 2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 1616–1622. https://doi.org/10.1109/AIM55361.2024.10637123

• Sang, C. L., Drawe, M., Siekmann, T., Hesse, M., & Rückert, U. (2025). Public 5G-Aided Robust Smartphone GNSS Localization in Challenging Environments. 2025 IEEE Future Networks World Forum (FNWF), 1–6. https://doi.org/10.1109/FNWF66845.2025.11317753

• Wermuth, S., Ahmed, Q. A., & Jungeblut, T. (2025). From Fisheye to Fusion: Towards Efficient AI-based Indoor Perception for Public Transportation. Workshop on AI and Its Applications.

• Wermuth, S. K., Ahmed, Q. A., Neumann, K., & Jungeblut, T. (2026a). PMOF: A Dataset and Benchmark for Passenger Monitoring Using Overhead Fisheye Cameras [Dataset]. Hugging Face. https://huggingface.co/datasets/swermuth/PMOF

• Wermuth, S. K., Ahmed, Q. A., Neumann, K., & Jungeblut, T. (2026b). PMOF: A Dataset and Benchmark for Passenger Monitoring Using Overhead Fisheye Cameras. 2026 IEEE International Conference on Advanced Visual and Signal-Based Systems (AVSS). Preprint: arXiv. 2026 IEEE International Conference on Advanced Visual and Signal-Based Systems (AVSS). https://doi.org/10.48550/ARXIV.2606.13910

• Wermuth, S. K., & Jungeblut, T. (2025). Bridging the Dataset Gap: Domain Adaptation for Fisheye Passenger Detection. Schriftenreihe des Institute for Data Science Solutions. https://doi.org/10.60802/SIDAS.2025.2

• Wermuth, S. K., & Jungeblut, T. (2026). Privacy-Enhanced Passenger Monitoring Using Global Image Blurring. Sail Closing Conference 2026.

• Witte, S. (2025). Solutions for autonomous rail-based mobility in rural areas. UITP Summit.

• Yang, Q., Heutger, S., Niemann, C., Jung, M., Al-Hamadi, A., & Wachsmuth, S. (2026). Long-Term Prediction of Local and Global Human Motion with Occlusion Recovery. In G. Bebis, J. Ye, Y. Wang, M. Konaković Luković, N. K. Kalantari, I. Cho, Y. Yang, E. Dimara, & M. Brehmer (Eds.), Advances in Visual Computing (Vol. 16396, pp. 141–153). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-14492-8_11

• Zeipel, H., Mensendiek, C., Seiler, M. V., Schütte, J., Trautmann, H., & Sextro, W. (2026). Trade-offs in Trajectory Planning for Autonomous Rail Vehicles under Multi-Objective Optimization. Proceedings of the 11th International Conference on Intelligent Transportation Engineering (ICITE 2026).

• Zeipel, H., Schnückel, V., Schütte, J., & Sextro, W. (2026). Data-Gap Resistant Learning of Partially Known Dynamics Using Physics-Informed Neural Networks. 17th World Congress on Computational Mechanics and 10th European Congress on Computational Methods in Applied Sciences and Engineering (WCCM-ECCOMAS 2026).

• Zeipel, H., Schütte, J., & Sextro, W. (2026). Physics-Informed Recurrent Neural Networks for Efficient Modeling of Rail-Vehicle Dynamics. Proceedings of the 23rd IFAC World Congress (IFAC 2026).