[{"volume":14,"citation":{"din1505-2-1":"<span style=\"font-variant:small-caps;\">Danso, Aaron Agyapong</span> ; <span style=\"font-variant:small-caps;\">Büker, Ulrich</span>: Automated Generation of Test Scenarios for Autonomous Driving Using LLMs. In: <i>Electronics</i> Bd. 14. Basel, MDPI (2025), Nr. 16, S. 3177","chicago-de":"Danso, Aaron Agyapong und Ulrich Büker. 2025. Automated Generation of Test Scenarios for Autonomous Driving Using LLMs. <i>Electronics</i> 14, Nr. 16: 3177. doi:<a href=\"https://doi.org/10.3390/electronics14163177\">10.3390/electronics14163177</a>, .","short":"A.A. Danso, U. Büker, Electronics 14 (2025) 3177.","mla":"Danso, Aaron Agyapong, and Ulrich Büker. “Automated Generation of Test Scenarios for Autonomous Driving Using LLMs.” <i>Electronics</i>, vol. 14, no. 16, 2025, p. 3177, <a href=\"https://doi.org/10.3390/electronics14163177\">https://doi.org/10.3390/electronics14163177</a>.","havard":"A.A. Danso, U. Büker, Automated Generation of Test Scenarios for Autonomous Driving Using LLMs, Electronics. 14 (2025) 3177.","bjps":"<b>Danso AA and Büker U</b> (2025) Automated Generation of Test Scenarios for Autonomous Driving Using LLMs. <i>Electronics</i> <b>14</b>, 3177.","ufg":"<b>Danso, Aaron Agyapong/Büker, Ulrich</b>: Automated Generation of Test Scenarios for Autonomous Driving Using LLMs, in: <i>Electronics</i> 14 (2025), H. 16,  S. 3177.","ama":"Danso AA, Büker U. Automated Generation of Test Scenarios for Autonomous Driving Using LLMs. <i>Electronics</i>. 2025;14(16):3177. doi:<a href=\"https://doi.org/10.3390/electronics14163177\">10.3390/electronics14163177</a>","van":"Danso AA, Büker U. Automated Generation of Test Scenarios for Autonomous Driving Using LLMs. Electronics. 2025;14(16):3177.","chicago":"Danso, Aaron Agyapong, and Ulrich Büker. “Automated Generation of Test Scenarios for Autonomous Driving Using LLMs.” <i>Electronics</i> 14, no. 16 (2025): 3177. <a href=\"https://doi.org/10.3390/electronics14163177\">https://doi.org/10.3390/electronics14163177</a>.","ieee":"A. A. Danso and U. Büker, “Automated Generation of Test Scenarios for Autonomous Driving Using LLMs,” <i>Electronics</i>, vol. 14, no. 16, p. 3177, 2025, doi: <a href=\"https://doi.org/10.3390/electronics14163177\">10.3390/electronics14163177</a>.","apa":"Danso, A. A., &#38; Büker, U. (2025). Automated Generation of Test Scenarios for Autonomous Driving Using LLMs. <i>Electronics</i>, <i>14</i>(16), 3177. <a href=\"https://doi.org/10.3390/electronics14163177\">https://doi.org/10.3390/electronics14163177</a>"},"title":"Automated Generation of Test Scenarios for Autonomous Driving Using LLMs","page":"3177","status":"public","author":[{"full_name":"Danso, Aaron Agyapong","first_name":"Aaron Agyapong","id":"84400","last_name":"Danso"},{"orcid":"0000-0002-4403-3889","id":"81453","first_name":"Ulrich","last_name":"Büker","full_name":"Büker, Ulrich"}],"year":"2025","date_updated":"2025-08-12T07:38:15Z","date_created":"2025-08-11T15:38:12Z","publication":"Electronics","publication_identifier":{"eissn":["2079-9292 "]},"language":[{"iso":"eng"}],"keyword":["large language models","generation","Operational Design Domain","autonomous vehicles","simulation","CARLA","ScenarioRunner","prompt-engineering","fine-tuning"],"publication_status":"published","quality_controlled":"1","publisher":"MDPI","department":[{"_id":"DEP5023"},{"_id":"DEP5000"}],"issue":"16","abstract":[{"text":"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.","lang":"eng"}],"user_id":"83781","intvolume":"        14","type":"scientific_journal_article","place":"Basel","doi":"10.3390/electronics14163177","_id":"13120"}]