---
_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: '11330'
abstract:
- lang: eng
  text: With the increasing complexity in manual assembly and a demographic decline
    in skilled workforce, the importance of well-documented processes through assembly
    instructions has grown. Creating these instructions is a time-consuming and knowledge-intensive
    task that typically relies on experienced employees. Although various automation
    solutions have been proposed to assist in generating assembly instructions, they
    often fall short in providing detailed textual guidance. With the rise of generative
    artificial intelligence (AI), new potentials arise in this domain. Therefore,
    this paper explores these potentials by employing various large language models
    (LLMs), prompting techniques and input data in an experimental setup for generating
    detailed assembly instructions, including the planning of assembly sequences as
    well as textual guidance on tools, assembly activities, and quality assurance
    measures. The findings reveal promising opportunities in leveraging LLMs but also
    substantial challenges, particularly in assembly sequence planning. To improve
    the reliability of generating assembly instructions, we propose a multi-agent
    concept that decomposes the complex task into simpler subtasks, each managed by
    specialized agents.
author:
- first_name: Frederic
  full_name: Meyer, Frederic
  id: '70963'
  last_name: Meyer
- first_name: Lennart
  full_name: Freitag, Lennart
  id: '73431'
  last_name: Freitag
- first_name: Sven
  full_name: Hinrichsen, Sven
  id: '49010'
  last_name: Hinrichsen
- first_name: Oliver
  full_name: Niggemann, Oliver
  id: '10876'
  last_name: Niggemann
citation:
  ama: Meyer F, Freitag L, Hinrichsen S, Niggemann O. <i>Potentials of Large Language
    Models for Generating Assembly Instructions</i>. Vol 78. (IEEE, ed.). IEEE; 2024.
    doi:<a href="https://doi.org/10.1109/ETFA61755.2024.10710806">https://doi.org/10.1109/ETFA61755.2024.10710806</a>
  apa: Meyer, F., Freitag, L., Hinrichsen, S., &#38; Niggemann, O. (2024). Potentials
    of Large Language Models for Generating Assembly Instructions. In IEEE (Ed.),
    <i>2024 IEEE 29th International Conference on Emerging Technologies and Factory
    Automation (ETFA)</i> (Vol. 78). IEEE. <a href="https://doi.org/10.1109/ETFA61755.2024.10710806">https://doi.org/10.1109/ETFA61755.2024.10710806</a>
  bjps: '<b>Meyer F <i>et al.</i></b> (2024) <i>Potentials of Large Language Models
    for Generating Assembly Instructions</i>, IEEE (ed.). Piscataway, NJ: IEEE.'
  chicago: 'Meyer, Frederic, Lennart Freitag, Sven Hinrichsen, and Oliver Niggemann.
    <i>Potentials of Large Language Models for Generating Assembly Instructions</i>.
    Edited by IEEE. <i>2024 IEEE 29th International Conference on Emerging Technologies
    and Factory Automation (ETFA)</i>. Vol. 78. Piscataway, NJ: IEEE, 2024. <a href="https://doi.org/10.1109/ETFA61755.2024.10710806">https://doi.org/10.1109/ETFA61755.2024.10710806</a>.'
  chicago-de: 'Meyer, Frederic, Lennart Freitag, Sven Hinrichsen und Oliver Niggemann.
    2024. <i>Potentials of Large Language Models for Generating Assembly Instructions</i>.
    Hg. von IEEE. <i>2024 IEEE 29th International Conference on Emerging Technologies
    and Factory Automation (ETFA)</i>. Bd. 78. Piscataway, NJ: IEEE. doi:<a href="https://doi.org/10.1109/ETFA61755.2024.10710806">https://doi.org/10.1109/ETFA61755.2024.10710806</a>,
    .'
  din1505-2-1: '<span style="font-variant:small-caps;">Meyer, Frederic</span> ; <span
    style="font-variant:small-caps;">Freitag, Lennart</span> ; <span style="font-variant:small-caps;">Hinrichsen,
    Sven</span> ; <span style="font-variant:small-caps;">Niggemann, Oliver</span>
    ; <span style="font-variant:small-caps;">IEEE</span> (Hrsg.): <i>Potentials of
    Large Language Models for Generating Assembly Instructions</i>. Bd. 78. Piscataway,
    NJ : IEEE, 2024'
  havard: F. Meyer, L. Freitag, S. Hinrichsen, O. Niggemann, Potentials of Large Language
    Models for Generating Assembly Instructions, IEEE, Piscataway, NJ, 2024.
  ieee: 'F. Meyer, L. Freitag, S. Hinrichsen, and O. Niggemann, <i>Potentials of Large
    Language Models for Generating Assembly Instructions</i>, vol. 78. Piscataway,
    NJ: IEEE, 2024. doi: <a href="https://doi.org/10.1109/ETFA61755.2024.10710806">https://doi.org/10.1109/ETFA61755.2024.10710806</a>.'
  mla: Meyer, Frederic, et al. “Potentials of Large Language Models for Generating
    Assembly Instructions.” <i>2024 IEEE 29th International Conference on Emerging
    Technologies and Factory Automation (ETFA)</i>, edited by IEEE, vol. 78, IEEE,
    2024, <a href="https://doi.org/10.1109/ETFA61755.2024.10710806">https://doi.org/10.1109/ETFA61755.2024.10710806</a>.
  short: F. Meyer, L. Freitag, S. Hinrichsen, O. Niggemann, Potentials of Large Language
    Models for Generating Assembly Instructions, IEEE, Piscataway, NJ, 2024.
  ufg: '<b>Meyer, Frederic u. a.</b>: Potentials of Large Language Models for Generating
    Assembly Instructions, Bd. 78, hg. von IEEE, Piscataway, NJ 2024.'
  van: 'Meyer F, Freitag L, Hinrichsen S, Niggemann O. Potentials of Large Language
    Models for Generating Assembly Instructions. IEEE, editor. Vol. 78, 2024 IEEE
    29th International Conference on Emerging Technologies and Factory Automation
    (ETFA). Piscataway, NJ: IEEE; 2024.'
conference:
  end_date: 2024-09-13
  location: Padova, Italy
  name: 29th International Conference on Emerging Technologies and Factory Automation
    (ETFA)
  start_date: 2024-09-10
corporate_editor:
- IEEE
date_created: 2024-04-12T07:06:41Z
date_updated: 2024-10-22T07:28:35Z
department:
- _id: DEP7020
- _id: DEP1305
doi: https://doi.org/10.1109/ETFA61755.2024.10710806
intvolume: '        78'
keyword:
- assembly instruction
- GPT
- large language model
- LLM
- prompt
language:
- iso: eng
place: Piscataway, NJ
publication: 2024 IEEE 29th International Conference on Emerging Technologies and
  Factory Automation (ETFA)
publication_identifier:
  eisbn:
  - 979-8-3503-6122-3
  isbn:
  - 979-8-3503-6123-0
publication_status: published
publisher: IEEE
quality_controlled: '1'
status: public
title: Potentials of Large Language Models for Generating Assembly Instructions
type: conference_editor_article
user_id: '83781'
volume: 78
year: '2024'
...
---
_id: '12811'
abstract:
- lang: eng
  text: For environmental, sustainable economic and political reasons, recycling processes
    are becoming increasingly important, aiming at a much higher use of secondary
    raw materials. Currently, for the copper and aluminum industries, no method for
    the non-destructive online analysis of heterogeneous materials is available. The
    prompt gamma neutron activation analysis (PGNAA) has the potential to overcome
    this challenge. A difficulty when using PGNAA for online classification arises
    from the small amount of noisy data, due to short-term measurements. In this case,
    classical evaluation methods using detailed peak by peak analysis fail. Therefore,
    we propose to view spectral data as probability distributions. Then, we can classify
    material using maximum log-likelihood with respect to kernel density estimation
    and use discrete sampling to optimize hyperparameters. For measurements of pure
    aluminum alloys we achieve near-perfect classification of aluminum alloys under
    0.25 s.
author:
- first_name: Helmand
  full_name: Shayan, Helmand
  id: '79365'
  last_name: Shayan
- first_name: Kai
  full_name: Krycki, Kai
  last_name: Krycki
- first_name: Marco
  full_name: Doemeland, Marco
  last_name: Doemeland
- first_name: Markus
  full_name: Lange-Hegermann, Markus
  id: '71761'
  last_name: Lange-Hegermann
citation:
  ama: Shayan H, Krycki K, Doemeland M, Lange-Hegermann M. PGNAA Spectral Classification
    of Metal With Density Estimations. <i>IEEE Transactions on Nuclear Science</i>.
    2023;70(6):1171-1177. doi:<a href="https://doi.org/10.1109/tns.2023.3242626">10.1109/tns.2023.3242626</a>
  apa: Shayan, H., Krycki, K., Doemeland, M., &#38; Lange-Hegermann, M. (2023). PGNAA
    Spectral Classification of Metal With Density Estimations. <i>IEEE Transactions
    on Nuclear Science</i>, <i>70</i>(6), 1171–1177. <a href="https://doi.org/10.1109/tns.2023.3242626">https://doi.org/10.1109/tns.2023.3242626</a>
  bjps: <b>Shayan H <i>et al.</i></b> (2023) PGNAA Spectral Classification of Metal
    With Density Estimations. <i>IEEE Transactions on Nuclear Science</i> <b>70</b>,
    1171–1177.
  chicago: 'Shayan, Helmand, Kai Krycki, Marco Doemeland, and Markus Lange-Hegermann.
    “PGNAA Spectral Classification of Metal With Density Estimations.” <i>IEEE Transactions
    on Nuclear Science</i> 70, no. 6 (2023): 1171–77. <a href="https://doi.org/10.1109/tns.2023.3242626">https://doi.org/10.1109/tns.2023.3242626</a>.'
  chicago-de: 'Shayan, Helmand, Kai Krycki, Marco Doemeland und Markus Lange-Hegermann.
    2023. PGNAA Spectral Classification of Metal With Density Estimations. <i>IEEE
    Transactions on Nuclear Science</i> 70, Nr. 6: 1171–1177. doi:<a href="https://doi.org/10.1109/tns.2023.3242626">10.1109/tns.2023.3242626</a>,
    .'
  din1505-2-1: '<span style="font-variant:small-caps;">Shayan, Helmand</span> ; <span
    style="font-variant:small-caps;">Krycki, Kai</span> ; <span style="font-variant:small-caps;">Doemeland,
    Marco</span> ; <span style="font-variant:small-caps;">Lange-Hegermann, Markus</span>:
    PGNAA Spectral Classification of Metal With Density Estimations. In: <i>IEEE Transactions
    on Nuclear Science</i> Bd. 70. New York, NY, IEEE (2023), Nr. 6, S. 1171–1177'
  havard: H. Shayan, K. Krycki, M. Doemeland, M. Lange-Hegermann, PGNAA Spectral Classification
    of Metal With Density Estimations, IEEE Transactions on Nuclear Science. 70 (2023)
    1171–1177.
  ieee: 'H. Shayan, K. Krycki, M. Doemeland, and M. Lange-Hegermann, “PGNAA Spectral
    Classification of Metal With Density Estimations,” <i>IEEE Transactions on Nuclear
    Science</i>, vol. 70, no. 6, pp. 1171–1177, 2023, doi: <a href="https://doi.org/10.1109/tns.2023.3242626">10.1109/tns.2023.3242626</a>.'
  mla: Shayan, Helmand, et al. “PGNAA Spectral Classification of Metal With Density
    Estimations.” <i>IEEE Transactions on Nuclear Science</i>, vol. 70, no. 6, 2023,
    pp. 1171–77, <a href="https://doi.org/10.1109/tns.2023.3242626">https://doi.org/10.1109/tns.2023.3242626</a>.
  short: H. Shayan, K. Krycki, M. Doemeland, M. Lange-Hegermann, IEEE Transactions
    on Nuclear Science 70 (2023) 1171–1177.
  ufg: '<b>Shayan, Helmand u. a.</b>: PGNAA Spectral Classification of Metal With
    Density Estimations, in: <i>IEEE Transactions on Nuclear Science</i> 70 (2023),
    H. 6,  S. 1171–1177.'
  van: Shayan H, Krycki K, Doemeland M, Lange-Hegermann M. PGNAA Spectral Classification
    of Metal With Density Estimations. IEEE Transactions on Nuclear Science. 2023;70(6):1171–7.
date_created: 2025-04-16T12:38:21Z
date_updated: 2025-06-26T07:45:59Z
department:
- _id: DEP5023
doi: 10.1109/tns.2023.3242626
external_id:
  isi:
  - '001012981300044'
intvolume: '        70'
isi: '1'
issue: '6'
keyword:
- Classification of metal
- kernel density estimation
- maximum log-likelihood
- online classification
- prompt gamma neutron activation analysis (PGNAA) spectral classification
- random sampling
language:
- iso: eng
page: 1171-1177
place: New York, NY
publication: IEEE Transactions on Nuclear Science
publication_identifier:
  eissn:
  - 1558-1578
  issn:
  - 0018-9499
publication_status: published
publisher: IEEE
status: public
title: PGNAA Spectral Classification of Metal With Density Estimations
type: scientific_journal_article
user_id: '83781'
volume: 70
year: '2023'
...