---
_id: '13398'
author:
- first_name: Ulrich
full_name: Büker, Ulrich
id: '81453'
last_name: Büker
orcid: 0000-0002-4403-3889
citation:
ama: 'Büker U. Autonomes Fahren auf Straße und Schiene: Wo stehen wir und wie
geht es weiter?; 2026.'
apa: 'Büker, U. (2026). Autonomes Fahren auf Straße und Schiene: Wo stehen wir
und wie geht es weiter? Mobilitätssymposium 2026, Paderborn.'
bjps: 'Büker U (2026) Autonomes Fahren auf Straße und Schiene: Wo stehen
wir und wie geht es weiter? .'
chicago: 'Büker, Ulrich. Autonomes Fahren auf Straße und Schiene: Wo stehen wir
und wie geht es weiter?, 2026.'
chicago-de: 'Büker, Ulrich. 2026. Autonomes Fahren auf Straße und Schiene: Wo
stehen wir und wie geht es weiter?'
din1505-2-1: 'Büker, Ulrich: Autonomes
Fahren auf Straße und Schiene: Wo stehen wir und wie geht es weiter?, 2026'
havard: 'U. Büker, Autonomes Fahren auf Straße und Schiene: Wo stehen wir und wie
geht es weiter?, 2026.'
ieee: 'U. Büker, Autonomes Fahren auf Straße und Schiene: Wo stehen wir und wie
geht es weiter? 2026.'
mla: 'Büker, Ulrich. Autonomes Fahren auf Straße und Schiene: Wo stehen wir und
wie geht es weiter? 2026.'
short: 'U. Büker, Autonomes Fahren auf Straße und Schiene: Wo stehen wir und wie
geht es weiter?, 2026.'
ufg: 'Büker, Ulrich: Autonomes Fahren auf Straße und Schiene: Wo stehen wir
und wie geht es weiter?, o. O. 2026.'
van: 'Büker U. Autonomes Fahren auf Straße und Schiene: Wo stehen wir und wie geht
es weiter? 2026.'
conference:
end_date: 2026-02-05
location: Paderborn
name: Mobilitätssymposium 2026
start_date: 2026-02-05
date_created: 2026-02-19T13:51:16Z
date_updated: 2026-02-19T14:58:00Z
department:
- _id: DEP5023
language:
- iso: ger
publication_status: published
status: public
title: 'Autonomes Fahren auf Straße und Schiene: Wo stehen wir und wie geht es weiter?'
type: conference_speech
user_id: '83781'
year: '2026'
...
---
_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. Electronics. 2025;14(16):3177. doi:10.3390/electronics14163177
apa: Danso, A. A., & Büker, U. (2025). Automated Generation of Test Scenarios
for Autonomous Driving Using LLMs. Electronics, 14(16), 3177. https://doi.org/10.3390/electronics14163177
bjps: Danso AA and Büker U (2025) Automated Generation of Test Scenarios
for Autonomous Driving Using LLMs. Electronics 14, 3177.
chicago: 'Danso, Aaron Agyapong, and Ulrich Büker. “Automated Generation of Test
Scenarios for Autonomous Driving Using LLMs.” Electronics 14, no. 16 (2025):
3177. https://doi.org/10.3390/electronics14163177.'
chicago-de: 'Danso, Aaron Agyapong und Ulrich Büker. 2025. Automated Generation
of Test Scenarios for Autonomous Driving Using LLMs. Electronics 14, Nr.
16: 3177. doi:10.3390/electronics14163177,
.'
din1505-2-1: 'Danso, Aaron Agyapong
; Büker, Ulrich: Automated Generation
of Test Scenarios for Autonomous Driving Using LLMs. In: Electronics 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,” Electronics, vol. 14, no. 16, p. 3177, 2025, doi:
10.3390/electronics14163177.'
mla: Danso, Aaron Agyapong, and Ulrich Büker. “Automated Generation of Test Scenarios
for Autonomous Driving Using LLMs.” Electronics, vol. 14, no. 16, 2025,
p. 3177, https://doi.org/10.3390/electronics14163177.
short: A.A. Danso, U. Büker, Electronics 14 (2025) 3177.
ufg: 'Danso, Aaron Agyapong/Büker, Ulrich: Automated Generation of Test Scenarios
for Autonomous Driving Using LLMs, in: Electronics 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: '11850'
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.'
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. Engineering. doi:10.20944/preprints202407.2591.v1
apa: Subramanian, R., & Büker, U. (n.d.). Study of Contactless Computer Vision-based
Road Condition Estimation Methods within the Framework of an Operational Design
Domain Monitoring System. In Engineering. MDPI AG. https://doi.org/10.20944/preprints202407.2591.v1
bjps: Subramanian R and Büker U (n.d.) Study of Contactless Computer Vision-Based
Road Condition Estimation Methods within the Framework of an Operational Design
Domain Monitoring System. Engineering.
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.” Engineering. MDPI AG, n.d. https://doi.org/10.20944/preprints202407.2591.v1.
chicago-de: Subramanian, Ramakrishnan und Ulrich Büker. Study of Contactless Computer
Vision-based Road Condition Estimation Methods within the Framework of an Operational
Design Domain Monitoring System. Engineering. MDPI AG. doi:10.20944/preprints202407.2591.v1,
.
din1505-2-1: 'Subramanian, Ramakrishnan
; Büker, Ulrich: Study of Contactless
Computer Vision-based Road Condition Estimation Methods within the Framework of
an Operational Design Domain Monitoring System. In: Engineering, MDPI AG'
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, Engineering. (n.d.).
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,” Engineering. MDPI AG. doi: 10.20944/preprints202407.2591.v1.'
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.” Engineering, MDPI AG, https://doi.org/10.20944/preprints202407.2591.v1.
short: R. Subramanian, U. Büker, Engineering (n.d.).
ufg: 'Subramanian, Ramakrishnan/Büker, Ulrich: Study of Contactless Computer
Vision-based Road Condition Estimation Methods within the Framework of an Operational
Design Domain Monitoring System, in: Engineeringo. O. u. J. .'
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. Engineering. MDPI AG;
date_created: 2024-08-28T14:46:39Z
date_updated: 2024-09-09T14:05:12Z
department:
- _id: DEP5023
- _id: DEP5000
doi: 10.20944/preprints202407.2591.v1
language:
- iso: eng
publication: Engineering
publication_status: submitted
publisher: MDPI AG
status: public
title: Study of Contactless Computer Vision-based Road Condition Estimation Methods
within the Framework of an Operational Design Domain Monitoring System
type: preprint
user_id: '83781'
year: '2024'
...
---
_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. Eng : advances in engineering. 2024;5(4):2778-2804. doi:10.3390/eng5040145'
apa: 'Subramanian, R., & Büker, U. (2024). 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(4), 2778–2804.
https://doi.org/10.3390/eng5040145'
bjps: 'Subramanian R and Büker U (2024) 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, 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.” Eng : Advances in Engineering 5, no.
4 (2024): 2778–2804. https://doi.org/10.3390/eng5040145.'
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. Eng : advances in engineering
5, Nr. 4: 2778–2804. doi:10.3390/eng5040145,
.'
din1505-2-1: 'Subramanian, Ramakrishnan
; Büker, Ulrich: Study of Contactless
Computer Vision-Based Road Condition Estimation Methods Within the Framework of
an Operational Design Domain Monitoring System. In: Eng : advances in engineering
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,” Eng : advances in engineering, vol. 5, no. 4,
pp. 2778–2804, 2024, doi: 10.3390/eng5040145.'
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.” Eng : Advances in Engineering, vol. 5,
no. 4, 2024, pp. 2778–804, https://doi.org/10.3390/eng5040145.'
short: 'R. Subramanian, U. Büker, Eng : Advances in Engineering 5 (2024) 2778–2804.'
ufg: 'Subramanian, Ramakrishnan/Büker, Ulrich: Study of Contactless Computer
Vision-Based Road Condition Estimation Methods Within the Framework of an Operational
Design Domain Monitoring System, in: Eng : advances in engineering 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'
...
---
_id: '12904'
abstract:
- lang: eng
text: 'It is crucial to identify defective machine components in production to ensure
quality. Some components generate heat when defective, so automating the inspection
process with a thermal imaging camera can provide qualitative measurements. This
work aims to use computer vision methods to locate these components in thermal
images. Since there is currently no comparison of object detection and semantic
segmentation algorithms for this use case, this study compares different architectures
with the goal of localising these components for further defect inspection. Moreover,
as there are currently no datasets for this use case, this study contributes a
novel annotated dataset of thermal images of combine harvester components. The
different algorithms are evaluated based on the quality of their predictions and
their suitability for further defect inspection. As semantic segmentation and
object detection cannot be directly compared with each other, custom weighted
metrics are used. The architectures evaluated include RetinaNet, YOLOV8 Detector,
DeepLabV3+, and SegFormer. Based on the experimental results, semantic segmentation
outperforms object detection regarding the use case, and the SegFormer architecture
achieves the best results with a weighted MeanIOU of 0.853. '
author:
- first_name: Hanna
full_name: Senke, Hanna
id: '79810'
last_name: Senke
- first_name: Dennis
full_name: Sprute, Dennis
last_name: Sprute
- first_name: Ulrich
full_name: Büker, Ulrich
id: '81453'
last_name: Büker
orcid: 0000-0002-4403-3889
- first_name: Holger
full_name: Flatt, Holger
id: '58494'
last_name: Flatt
citation:
ama: Senke H, Sprute D, Büker U, Flatt H. Deep Learning-Based Localisation of
Combine Harvester Components in Thermal Images. (Längle T, Heizmann M, Karlsruher
Institut für Technologie. Institut für Industrielle Informationstechnik , Fraunhofer-Institut
für Optronik, Systemtechnik und Bildauswertung , eds.). KIT Scientific Publishing;
2024:71-82. doi:10.58895/ksp/1000174496-7
apa: Senke, H., Sprute, D., Büker, U., & Flatt, H. (2024). Deep learning-based
localisation of combine harvester components in thermal images. In T. Längle,
M. Heizmann, Karlsruher Institut für Technologie. Institut für Industrielle Informationstechnik
, & Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung (Eds.),
Forum Bildverarbeitung 2024 = Image Pocessing Forum 2024 (pp. 71–82). KIT
Scientific Publishing. https://doi.org/10.58895/ksp/1000174496-7
bjps: 'Senke H et al. (2024) Deep Learning-Based Localisation of
Combine Harvester Components in Thermal Images, Längle T et al. (eds). Karlsruhe:
KIT Scientific Publishing.'
chicago: 'Senke, Hanna, Dennis Sprute, Ulrich Büker, and Holger Flatt. Deep Learning-Based
Localisation of Combine Harvester Components in Thermal Images. Edited by
Thomas Längle, Michael Heizmann, Karlsruher Institut für Technologie. Institut
für Industrielle Informationstechnik , and Fraunhofer-Institut für Optronik, Systemtechnik
und Bildauswertung . Forum Bildverarbeitung 2024 = Image Pocessing Forum 2024.
Karlsruhe: KIT Scientific Publishing, 2024. https://doi.org/10.58895/ksp/1000174496-7.'
chicago-de: 'Senke, Hanna, Dennis Sprute, Ulrich Büker und Holger Flatt. 2024. Deep
learning-based localisation of combine harvester components in thermal images.
Hg. von Thomas Längle, Michael Heizmann, Karlsruher Institut für Technologie.
Institut für Industrielle Informationstechnik , und Fraunhofer-Institut für Optronik,
Systemtechnik und Bildauswertung . Forum Bildverarbeitung 2024 = Image Pocessing
Forum 2024. Karlsruhe: KIT Scientific Publishing. doi:10.58895/ksp/1000174496-7,
.'
din1505-2-1: 'Senke, Hanna ; Sprute, Dennis ; Büker,
Ulrich ; Flatt, Holger ;
Längle, T. ; Heizmann,
M. ; Karlsruher Institut für Technologie.
Institut für Industrielle Informationstechnik ; Fraunhofer-Institut
für Optronik, Systemtechnik und Bildauswertung (Hrsg.): Deep learning-based
localisation of combine harvester components in thermal images. Karlsruhe :
KIT Scientific Publishing, 2024'
havard: H. Senke, D. Sprute, U. Büker, H. Flatt, Deep learning-based localisation
of combine harvester components in thermal images, KIT Scientific Publishing,
Karlsruhe, 2024.
ieee: 'H. Senke, D. Sprute, U. Büker, and H. Flatt, Deep learning-based localisation
of combine harvester components in thermal images. Karlsruhe: KIT Scientific
Publishing, 2024, pp. 71–82. doi: 10.58895/ksp/1000174496-7.'
mla: Senke, Hanna, et al. “Deep Learning-Based Localisation of Combine Harvester
Components in Thermal Images.” Forum Bildverarbeitung 2024 = Image Pocessing
Forum 2024, edited by Thomas Längle et al., KIT Scientific Publishing, 2024,
pp. 71–82, https://doi.org/10.58895/ksp/1000174496-7.
short: H. Senke, D. Sprute, U. Büker, H. Flatt, Deep Learning-Based Localisation
of Combine Harvester Components in Thermal Images, KIT Scientific Publishing,
Karlsruhe, 2024.
ufg: 'Senke, Hanna u. a.: Deep learning-based localisation of combine harvester
components in thermal images, hg. von Längle, Thomas u. a., Karlsruhe 2024.'
van: 'Senke H, Sprute D, Büker U, Flatt H. Deep learning-based localisation of combine
harvester components in thermal images. Längle T, Heizmann M, Karlsruher Institut
für Technologie. Institut für Industrielle Informationstechnik , Fraunhofer-Institut
für Optronik, Systemtechnik und Bildauswertung , editors. Forum Bildverarbeitung
2024 = Image Pocessing Forum 2024. Karlsruhe: KIT Scientific Publishing; 2024.'
conference:
end_date: 2024-11-22
location: Karlsruhe
name: Forum Bildverarbeitung 2024
start_date: 2024-11-21
corporate_editor:
- 'Karlsruher Institut für Technologie. Institut für Industrielle Informationstechnik '
- 'Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung '
date_created: 2025-05-08T14:01:20Z
date_updated: 2025-05-12T07:33:48Z
department:
- _id: DEP5023
doi: 10.58895/ksp/1000174496-7
editor:
- first_name: Thomas
full_name: Längle, Thomas
last_name: Längle
- first_name: Michael
full_name: Heizmann, Michael
last_name: Heizmann
keyword:
- industrial quality assurance
- deep learning architectures
- object localisation
- Thermal images
language:
- iso: eng
page: 71-82
place: Karlsruhe
publication: Forum Bildverarbeitung 2024 = Image Pocessing Forum 2024
publication_identifier:
isbn:
- 978-3-7315-1386-5
publication_status: published
publisher: KIT Scientific Publishing
quality_controlled: '1'
status: public
title: Deep learning-based localisation of combine harvester components in thermal
images
type: conference_editor_article
user_id: '83781'
year: '2024'
...
---
_id: '12905'
author:
- first_name: Lennart
full_name: Schünemann, Lennart
last_name: Schünemann
- first_name: Ulrich
full_name: Büker, Ulrich
id: '81453'
last_name: Büker
orcid: 0000-0002-4403-3889
citation:
ama: Schünemann L, Büker U. Berechnung der Koplanarität und der stabilen Auflageflächen
elektronischer, oberflächenmontierbarer Bauelemente.; 2024.
apa: Schünemann, L., & Büker, U. (2024). Berechnung der Koplanarität und
der stabilen Auflageflächen elektronischer, oberflächenmontierbarer Bauelemente.
Bildverarbeitung in der Automation 2024, Lemgo.
bjps: Schünemann L and Büker U (2024) Berechnung der Koplanarität und
der stabilen Auflageflächen elektronischer, oberflächenmontierbarer Bauelemente.
.
chicago: Schünemann, Lennart, and Ulrich Büker. Berechnung der Koplanarität und
der stabilen Auflageflächen elektronischer, oberflächenmontierbarer Bauelemente,
2024.
chicago-de: Schünemann, Lennart und Ulrich Büker. 2024. Berechnung der Koplanarität
und der stabilen Auflageflächen elektronischer, oberflächenmontierbarer Bauelemente.
din1505-2-1: 'Schünemann, Lennart
; Büker, Ulrich: Berechnung der
Koplanarität und der stabilen Auflageflächen elektronischer, oberflächenmontierbarer
Bauelemente, 2024'
havard: L. Schünemann, U. Büker, Berechnung der Koplanarität und der stabilen Auflageflächen
elektronischer, oberflächenmontierbarer Bauelemente, 2024.
ieee: L. Schünemann and U. Büker, Berechnung der Koplanarität und der stabilen
Auflageflächen elektronischer, oberflächenmontierbarer Bauelemente. 2024.
mla: Schünemann, Lennart, and Ulrich Büker. Berechnung der Koplanarität und der
stabilen Auflageflächen elektronischer, oberflächenmontierbarer Bauelemente.
2024.
short: L. Schünemann, U. Büker, Berechnung der Koplanarität und der stabilen Auflageflächen
elektronischer, oberflächenmontierbarer Bauelemente, 2024.
ufg: 'Schünemann, Lennart/Büker, Ulrich: Berechnung der Koplanarität und
der stabilen Auflageflächen elektronischer, oberflächenmontierbarer Bauelemente,
o. O. 2024.'
van: Schünemann L, Büker U. Berechnung der Koplanarität und der stabilen Auflageflächen
elektronischer, oberflächenmontierbarer Bauelemente. 2024.
conference:
end_date: 2024-11-05
location: Lemgo
name: Bildverarbeitung in der Automation 2024
start_date: 2024-11-05
date_created: 2025-05-08T14:20:14Z
date_updated: 2025-05-12T07:53:57Z
department:
- _id: DEP5023
language:
- iso: ger
publication_status: published
status: public
title: Berechnung der Koplanarität und der stabilen Auflageflächen elektronischer,
oberflächenmontierbarer Bauelemente
type: conference_speech
user_id: '83781'
year: '2024'
...
---
_id: '12906'
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. ODD Monitoring in Autonomous Vehicles.; 2024.
apa: Subramanian, R., & Büker, U. (2024). ODD monitoring in Autonomous Vehicles.
20. Dortmunder Autotag, Dortmund.
bjps: Subramanian R and Büker U (2024) ODD Monitoring in Autonomous Vehicles.
.
chicago: Subramanian, Ramakrishnan, and Ulrich Büker. ODD Monitoring in Autonomous
Vehicles, 2024.
chicago-de: Subramanian, Ramakrishnan und Ulrich Büker. 2024. ODD monitoring
in Autonomous Vehicles.
din1505-2-1: 'Subramanian, Ramakrishnan
; Büker, Ulrich: ODD monitoring
in Autonomous Vehicles, 2024'
havard: R. Subramanian, U. Büker, ODD monitoring in Autonomous Vehicles, 2024.
ieee: R. Subramanian and U. Büker, ODD monitoring in Autonomous Vehicles.
2024.
mla: Subramanian, Ramakrishnan, and Ulrich Büker. ODD Monitoring in Autonomous
Vehicles. 2024.
short: R. Subramanian, U. Büker, ODD Monitoring in Autonomous Vehicles, 2024.
ufg: 'Subramanian, Ramakrishnan/Büker, Ulrich: ODD monitoring in Autonomous
Vehicles, o. O. 2024.'
van: Subramanian R, Büker U. ODD monitoring in Autonomous Vehicles. 2024.
conference:
end_date: 2024-09-05
location: Dortmund
name: 20. Dortmunder Autotag
start_date: 2024-09-05
date_created: 2025-05-08T14:24:06Z
date_updated: 2025-05-12T07:46:38Z
department:
- _id: DEP5023
language:
- iso: eng
publication_status: published
status: public
title: ODD monitoring in Autonomous Vehicles
type: conference_poster
user_id: '83781'
year: '2024'
...