---
_id: '13120'
abstract:
- lang: eng
  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.'
author:
- first_name: Aaron Agyapong
  full_name: Danso, Aaron Agyapong
  id: '84400'
  last_name: Danso
- first_name: Ulrich
  full_name: Büker, Ulrich
  id: '81453'
  last_name: Büker
  orcid: 0000-0002-4403-3889
citation:
  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>
  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>
  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.
  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>.'
  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>,
    .'
  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'
  havard: A.A. Danso, U. Büker, Automated Generation of Test Scenarios for Autonomous
    Driving Using LLMs, Electronics. 14 (2025) 3177.
  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>.'
  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>.
  short: A.A. Danso, U. Büker, Electronics 14 (2025) 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.'
  van: Danso AA, Büker U. Automated Generation of Test Scenarios for Autonomous Driving
    Using LLMs. Electronics. 2025;14(16):3177.
date_created: 2025-08-11T15:38:12Z
date_updated: 2025-08-12T07:38:15Z
department:
- _id: DEP5023
- _id: DEP5000
doi: 10.3390/electronics14163177
intvolume: '        14'
issue: '16'
keyword:
- large language models
- generation
- Operational Design Domain
- autonomous vehicles
- simulation
- CARLA
- ScenarioRunner
- prompt-engineering
- fine-tuning
language:
- iso: eng
page: '3177'
place: Basel
publication: Electronics
publication_identifier:
  eissn:
  - '2079-9292 '
publication_status: published
publisher: MDPI
quality_controlled: '1'
status: public
title: Automated Generation of Test Scenarios for Autonomous Driving Using LLMs
type: scientific_journal_article
user_id: '83781'
volume: 14
year: '2025'
...