@misc{13398,
  author       = {{Büker, Ulrich}},
  location     = {{Paderborn}},
  title        = {{{Autonomes Fahren auf Straße und Schiene: Wo stehen wir und wie geht es weiter?}}},
  year         = {{2026}},
}

@misc{13120,
  abstract     = {{This paper introduces an approach that leverages large language models (LLMs) to convert detailed descriptions of an Operational Design Domain (ODD) into realistic, executable simulation scenarios for testing autonomous vehicles. The method combines model-based and data-driven techniques to decompose ODDs into three key components: environmental, scenery, and dynamic elements. It then applies prompt engineering to generate ScenarioRunner scripts compatible with CARLA. The model-based component guides the LLM using structured prompts and a “Tree of Thoughts” strategy to outline the scenario, while a data-driven refinement process, drawing inspiration from red teaming, enhances the accuracy and robustness of the generated scripts over time. Experimental results show that while static components, such as weather and road layouts, are well captured, dynamic elements like vehicle and pedestrian behavior require further refinement. Overall, this approach not only reduces the manual effort involved in creating simulation scenarios but also identifies key challenges and opportunities for advancing safer and more adaptive autonomous driving systems.}},
  author       = {{Danso, Aaron Agyapong and Büker, Ulrich}},
  booktitle    = {{Electronics}},
  issn         = {{2079-9292 }},
  keywords     = {{large language models, generation, Operational Design Domain, autonomous vehicles, simulation, CARLA, ScenarioRunner, prompt-engineering, fine-tuning}},
  number       = {{16}},
  pages        = {{3177}},
  publisher    = {{MDPI}},
  title        = {{{Automated Generation of Test Scenarios for Autonomous Driving Using LLMs}}},
  doi          = {{10.3390/electronics14163177}},
  volume       = {{14}},
  year         = {{2025}},
}

@unpublished{11850,
  abstract     = {{Deployment of Level 3 and Level 4 autonomous vehicles (AVs) in urban environments is significantly constrained by adverse weather conditions, limiting their operation to clear weather due to safety concerns. Ensuring that AVs remain within their designated Operational Design Domain (ODD) is a formidable challenge, making boundary monitoring strategies essential for safe navigation. This study explores the critical role of an ODD monitoring system (OMS) in addressing these challenges. It reviews various methodologies for designing an OMS and presents a comprehensive visualization framework incorporating trigger points for ODD exits. These trigger points serve as essential references for effective OMS design. The study also delves into a specific use case concerning ODD exits: the reduction in road friction due to adverse weather conditions. It emphasizes the importance of contactless computer vision-based methods for road condition estimation (RCE), particularly using vision sensors such as cameras. The study details a timeline of methods involving classical machine learning and deep learning feature extraction techniques, identifying contemporary challenges such as class imbalance, lack of comprehensive datasets, annotation methods, and the scarcity of generalization techniques. Furthermore, it provides a factual comparison of two state-of-the-art RCE datasets. In essence, the study aims to address and explore ODD exits due to weather-induced road conditions, decoding the practical solutions and directions for future research in the realm of AVs.}},
  author       = {{Subramanian, Ramakrishnan and Büker, Ulrich}},
  booktitle    = {{Engineering}},
  publisher    = {{MDPI AG}},
  title        = {{{Study of Contactless Computer Vision-based Road Condition Estimation Methods within the Framework of an Operational Design Domain Monitoring System}}},
  doi          = {{10.20944/preprints202407.2591.v1}},
  year         = {{2024}},
}

@misc{12167,
  abstract     = {{Deployment of Level 3 and Level 4 autonomous vehicles (AVs) in urban environments is significantly constrained by adverse weather conditions, limiting their operation to clear weather due to safety concerns. Ensuring that AVs remain within their designated Operational Design Domain (ODD) is a formidable challenge, making boundary monitoring strategies essential for safe navigation. This study explores the critical role of an ODD monitoring system (OMS) in addressing these challenges. It reviews various methodologies for designing an OMS and presents a comprehensive visualization framework incorporating trigger points for ODD exits. These trigger points serve as essential references for effective OMS design. The study also delves into a specific use case concerning ODD exits: the reduction in road friction due to adverse weather conditions. It emphasizes the importance of contactless computer vision-based methods for road condition estimation (RCE), particularly using vision sensors such as cameras. The study details a timeline of methods involving classical machine learning and deep learning feature extraction techniques, identifying contemporary challenges such as class imbalance, lack of comprehensive datasets, annotation methods, and the scarcity of generalization techniques. Furthermore, it provides a factual comparison of two state-of-the-art RCE datasets. In essence, the study aims to address and explore ODD exits due to weather-induced road conditions, decoding the practical solutions and directions for future research in the realm of AVs.}},
  author       = {{Subramanian, Ramakrishnan and Büker, Ulrich}},
  booktitle    = {{Eng : advances in engineering}},
  issn         = {{2673-4117}},
  keywords     = {{autonomous vehicles, operational design domain, computer vision, machine learning, road surface detection}},
  number       = {{4}},
  pages        = {{2778--2804}},
  publisher    = {{MDPI AG}},
  title        = {{{Study of Contactless Computer Vision-Based Road Condition Estimation Methods Within the Framework of an Operational Design Domain Monitoring System}}},
  doi          = {{10.3390/eng5040145}},
  volume       = {{5}},
  year         = {{2024}},
}

@misc{12904,
  abstract     = {{It is crucial to identify defective machine components in production to ensure quality. Some components generate heat when defective, so automating the inspection process with a thermal imaging camera can provide qualitative measurements. This work aims to use computer vision methods to locate these components in thermal images. Since there is currently  no comparison of object detection and semantic segmentation algorithms for this use case, this study compares different architectures with the goal of localising these components for  further defect inspection. Moreover, as there are currently no datasets for this use case, this study contributes a novel annotated dataset of thermal images of combine harvester  components. The different algorithms are evaluated based on the quality of their predictions and their suitability for further defect inspection. As semantic segmentation and object  detection cannot be directly compared with each other, custom weighted metrics are used. The architectures evaluated include RetinaNet, YOLOV8 Detector, DeepLabV3+, and  SegFormer. Based on the experimental results, semantic segmentation outperforms object detection regarding the use case, and the SegFormer architecture achieves the best results  with a weighted MeanIOU of 0.853.  }},
  author       = {{Senke, Hanna and Sprute, Dennis and Büker, Ulrich and Flatt, Holger}},
  booktitle    = {{Forum Bildverarbeitung 2024 = Image Pocessing Forum 2024}},
  editor       = {{Längle, Thomas and Heizmann, Michael}},
  isbn         = {{978-3-7315-1386-5}},
  keywords     = {{industrial quality assurance, deep learning architectures, object localisation, Thermal images}},
  location     = {{Karlsruhe}},
  pages        = {{71--82}},
  publisher    = {{KIT Scientific Publishing}},
  title        = {{{Deep learning-based localisation of combine harvester components in thermal images}}},
  doi          = {{10.58895/ksp/1000174496-7}},
  year         = {{2024}},
}

@misc{12905,
  author       = {{Schünemann, Lennart and Büker, Ulrich}},
  location     = {{Lemgo}},
  title        = {{{Berechnung der Koplanarität und der stabilen Auflageflächen elektronischer, oberflächenmontierbarer Bauelemente}}},
  year         = {{2024}},
}

@misc{12906,
  author       = {{Subramanian, Ramakrishnan and Büker, Ulrich}},
  location     = {{Dortmund}},
  title        = {{{ODD monitoring in Autonomous Vehicles}}},
  year         = {{2024}},
}