---
_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'
...
---
_id: '12167'
abstract:
- lang: eng
  text: '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.'
article_type: original
author:
- first_name: Ramakrishnan
  full_name: Subramanian, Ramakrishnan
  id: '85499'
  last_name: Subramanian
- first_name: Ulrich
  full_name: Büker, Ulrich
  id: '81453'
  last_name: Büker
  orcid: 0000-0002-4403-3889
citation:
  ama: 'Subramanian R, Büker U. Study of Contactless Computer Vision-Based Road Condition
    Estimation Methods Within the Framework of an Operational Design Domain Monitoring
    System. <i>Eng : advances in engineering</i>. 2024;5(4):2778-2804. doi:<a href="https://doi.org/10.3390/eng5040145">10.3390/eng5040145</a>'
  apa: 'Subramanian, R., &#38; Büker, U. (2024). Study of Contactless Computer Vision-Based
    Road Condition Estimation Methods Within the Framework of an Operational Design
    Domain Monitoring System. <i>Eng : Advances in Engineering</i>, <i>5</i>(4), 2778–2804.
    <a href="https://doi.org/10.3390/eng5040145">https://doi.org/10.3390/eng5040145</a>'
  bjps: '<b>Subramanian R and Büker U</b> (2024) Study of Contactless Computer Vision-Based
    Road Condition Estimation Methods Within the Framework of an Operational Design
    Domain Monitoring System. <i>Eng : advances in engineering</i> <b>5</b>, 2778–2804.'
  chicago: 'Subramanian, Ramakrishnan, and Ulrich Büker. “Study of Contactless Computer
    Vision-Based Road Condition Estimation Methods Within the Framework of an Operational
    Design Domain Monitoring System.” <i>Eng : Advances in Engineering</i> 5, no.
    4 (2024): 2778–2804. <a href="https://doi.org/10.3390/eng5040145">https://doi.org/10.3390/eng5040145</a>.'
  chicago-de: 'Subramanian, Ramakrishnan und Ulrich Büker. 2024. Study of Contactless
    Computer Vision-Based Road Condition Estimation Methods Within the Framework of
    an Operational Design Domain Monitoring System. <i>Eng : advances in engineering</i>
    5, Nr. 4: 2778–2804. doi:<a href="https://doi.org/10.3390/eng5040145">10.3390/eng5040145</a>,
    .'
  din1505-2-1: '<span style="font-variant:small-caps;">Subramanian, Ramakrishnan</span>
    ; <span style="font-variant:small-caps;">Büker, Ulrich</span>: Study of Contactless
    Computer Vision-Based Road Condition Estimation Methods Within the Framework of
    an Operational Design Domain Monitoring System. In: <i>Eng : advances in engineering</i>
    Bd. 5. Basel, MDPI AG (2024), Nr. 4, S. 2778–2804'
  havard: 'R. Subramanian, U. Büker, Study of Contactless Computer Vision-Based Road
    Condition Estimation Methods Within the Framework of an Operational Design Domain
    Monitoring System, Eng : Advances in Engineering. 5 (2024) 2778–2804.'
  ieee: 'R. Subramanian and U. Büker, “Study of Contactless Computer Vision-Based
    Road Condition Estimation Methods Within the Framework of an Operational Design
    Domain Monitoring System,” <i>Eng : advances in engineering</i>, vol. 5, no. 4,
    pp. 2778–2804, 2024, doi: <a href="https://doi.org/10.3390/eng5040145">10.3390/eng5040145</a>.'
  mla: 'Subramanian, Ramakrishnan, and Ulrich Büker. “Study of Contactless Computer
    Vision-Based Road Condition Estimation Methods Within the Framework of an Operational
    Design Domain Monitoring System.” <i>Eng : Advances in Engineering</i>, vol. 5,
    no. 4, 2024, pp. 2778–804, <a href="https://doi.org/10.3390/eng5040145">https://doi.org/10.3390/eng5040145</a>.'
  short: 'R. Subramanian, U. Büker, Eng : Advances in Engineering 5 (2024) 2778–2804.'
  ufg: '<b>Subramanian, Ramakrishnan/Büker, Ulrich</b>: Study of Contactless Computer
    Vision-Based Road Condition Estimation Methods Within the Framework of an Operational
    Design Domain Monitoring System, in: <i>Eng : advances in engineering</i> 5 (2024),
    H. 4,  S. 2778–2804.'
  van: 'Subramanian R, Büker U. Study of Contactless Computer Vision-Based Road Condition
    Estimation Methods Within the Framework of an Operational Design Domain Monitoring
    System. Eng : advances in engineering. 2024;5(4):2778–804.'
date_created: 2024-12-04T16:46:30Z
date_updated: 2024-12-05T13:19:17Z
department:
- _id: DEP5023
- _id: DEP5000
doi: 10.3390/eng5040145
intvolume: '         5'
issue: '4'
keyword:
- autonomous vehicles
- operational design domain
- computer vision
- machine learning
- road surface detection
language:
- iso: eng
page: 2778-2804
place: Basel
publication: 'Eng : advances in engineering'
publication_identifier:
  eissn:
  - 2673-4117
publication_status: published
publisher: MDPI AG
quality_controlled: '1'
status: public
title: Study of Contactless Computer Vision-Based Road Condition Estimation Methods
  Within the Framework of an Operational Design Domain Monitoring System
type: scientific_journal_article
user_id: '83781'
volume: 5
year: '2024'
...