@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}},
}

@inproceedings{2071,
  abstract     = {{In this paper, a fuzzy pattern classification tuning approach is proposed, which is based on fusion concept. In this method, tuning parameters are learned in a training procedure, enabling system to be capable of managing individual classification task. Fuzzy c-means, as a specific instance of Tuning Reference, is employed as a tool to offer membership function which is used for making decisions and its membership function fuses (tunes) another membership function captured from fuzzy pattern classification and then final decisions are made upon fused one. Experiments are taken on five benchmark datasets, one of them shows an equal performance and the other four present better results than each single classifier.}},
  author       = {{Li, Rui and Lohweg, Volker}},
  isbn         = {{978-3-8007-3092-6}},
  keywords     = {{tuning parameter, information fusion, fuzzy cmeans, membership function, fuzzy pattern classification}},
  publisher    = {{In: The 11th Conference on Information Fusion, June 30 - July 3, Cologne, Germany}},
  title        = {{{Fuzzy Pattern Classification Tuning by Parameter Learning based on Fusion Concept}}},
  year         = {{2008}},
}