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README.md

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 ---
 license: cc-by-4.0
+license_link: LICENSE
+task_categories:
+- image-classification
+language:
+- en
+tags:
+- traffic sign recognition
+- synthetic
+- synset
+- OCTAS
+- computer-vision
+- robustness
+- sensitivity-analysis
+- automated-driving
+pretty_name: Synset Signset Sensitivity Analysis Dataset
 ---
+
+# Synset Signset Sensitivity Analysis Dataset
+
+<!-- Provide a quick summary of the dataset. -->
+The <em>Synset Signset Sensitivity Analysis Dataset</em> is a large-scale, physically based synthetic test 
+dataset for evaluating the sensitivity and robustness of machine-learning traffic sign recognition models to 
+sensing parameters. It contains 9,442,800 images from 43 
+[GTSRB](https://ieeexplore.ieee.org/abstract/document/6033395) traffic sign classes, systematically generated
+across 2,196 combinations of motion blur and additive image noise. The dataset also includes traffic sign 
+masks, segmentation images, and image-level JSON metadata.
+
+**Website**: [synset.de/datasets/synset-signset-ger/sensitivity-analysis/](https://synset.de/datasets/synset-signset-ger/sensitivity-analysis/) <br>
+**Paper:** Sielemann, A., Bitzer, N., Roschani, M., Ziehn, J., and Beyerer, J. (2026). Sensitivity Analysis of Deep-Learning Perception Models to Sensing Parameters. In 2026 IEEE 29th International Conference on Intelligent Transportation Systems (ITSC).  <br>
+**Authors:** [Anne Sielemann](https://www.linkedin.com/in/anne-sielemann-23011026a/), [Niklas Bitzer](https://de.linkedin.com/in/nbitzer), Masoud Roschani,
+[Jens Ziehn](https://www.linkedin.com/in/jrziehn/), and Juergen Beyerer. [Fraunhofer IOSB](https://www.iosb.fraunhofer.de/), Germany.  <br>
+**Funded by:** In part by the [German Federal Ministry for Economic Affairs and Energy](https://www.bundeswirtschaftsministerium.de/Navigation/EN/Home/home.html) (BMWE) within the program “New Vehicle and System Technologies” as part of the [AVEAS research project](https://aveas.org/en/ueberblick-en-translation/), within the scope of the [KAMO: Karlsruhe Mobility High Performance Center](https://www.kamo.one/en/welcome/). <br>
+**License:** CC-BY 4.0 <br>
+
+## Description
+
+The <em>Synset Signset Sensitivity Analysis Dataset</em> 
+
+## Citation and Reference
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+To cite this dataset in your scientific work, please use the following bibliography entry:
+
+**BibTeX:**
+
+    @inproceedings{sensitivity_analysis_sielemann_2026,
+      title={{Sensitivity Analysis of Deep-Learning Perception Models to Sensing Parameters}},
+      author={Sielemann, Anne and Bitzer, Niklas and Roschani, Masoud and Ziehn, Jens and Beyerer, Juergen},
+      booktitle={2026 IEEE 29th International Conference on Intelligent Transportation Systems (ITSC)},
+      year={2026}
+    }
+
+**APA:**
+
+    Sielemann, A., Bitzer, N., Roschani, M., Ziehn, J., and Beyerer, J. (2026). 
+    Sensitivity Analysis of Deep-Learning Perception Models to Sensing Parameters. 
+    In 2026 IEEE 29th International Conference on Intelligent Transportation Systems (ITSC).
+
+In case of copying and redistributing, or publishing an adapted version of our dataset, please provide the name of our dataset, the creator names, a copyright notice, 
+a link to this website, a license notice with a link to the license, and, if changes were made, a disclaimer notice, and a short description of the applied changes. 
+For example, as follows:
+
+    This work is based on Sensitivity Analysis of Deep-Learning Perception Models to Sensing Parameters
+    by Anne Sielemann, Niklas Bitzer, Masoud Roschani, Jens Ziehn, and Juergen Beyerer,
+    © 2026 Fraunhofer IOSB, All rights reserved.
+    Link: https://synset.de/datasets/synset-signset-ger/sensitivity-analysis/
+    Licence: CC BY 4.0 
+    Disclaimer: The original authors are neither affiliated nor responsible for any applied changes.
+
+## Uses
+
+### Direct Use 
+
+<!-- This section describes suitable use cases for the dataset. -->
+The presented dataset can be used to investigate the trade-off between motion blur and additive image noise. Increasing the exposure time 
+can improve image brightness but may introduce stronger motion blur, while increasing camera gain or ISO can reduce the required exposure 
+time at the cost of increased image noise. By evaluating different combinations of both parameters 
+at comparable illumination levels, researchers can analyze how perception models respond to these competing sensing strategies and whether 
+one trade-off is more favorable for specific traffic sign classes or model architectures.
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
+
+## Dataset Structure
+
+<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+
+| Property | Description |
+|---|---|
+| Number of subdatasets | 36 × 61 = 2,196 |
+| Motion blur | 0 to 35 pixels |
+| Number of motion-blur levels | 36 |
+| Additive noise factor | 0.00 to 0.60 |
+| Noise step size | 0.01 |
+| Number of noise levels | 61 |
+| Traffic sign classes | 43 GTSRB classes |
+| Images per class and subdataset | 100 |
+| Total number of images | 9,442,800 |
+| Image resolution | 48 × 48 pixels |
+
+## Dataset Creation
+
+### Curation Rationale
+
+<!-- Motivation for the creation of this dataset. -->
+
+### Source Data
+
+<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+
+#### Who are the source data producers?
+
+<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
+
+- [PolyHaven](https://polyhaven.com/), as the provider of the environment maps for image-based lighting (IBL) and the 3D tree object,
+  is an online library for open (CC0) 3D assets provided by different authors.
+
+- [Wikipedia](https://de.wikipedia.org/), one of the largest free multilingual open-content encyclopedias, includes the complete list
+  of existing German traffic signs and their template images.
+
+### Annotations
+
+<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+
+#### Annotation process
+
+<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
+
+The major part of the annotations, including masks, segmentation images, camera parameters and artifacts, and environmental conditions, 
+is based on ground truth data created as part of the scene generation / rendering process. Semantic segmentation images were rendered 
+using the [Ogre](https://www.ogre3d.org/) rendering engine plugin for [OCTAS®](https://octas.org/), 
+which provides rasterization / shading-based image generation.
+
+The only manual annotation performed in the creation of the particular dataset is the labeling of permissible upper and lower signs taking 
+the German traffic code / regulation [StVO](https://www.stvo2go.de/verkehrszeichen-wissensnetz/) (Straßenverkehrs-Ordnung) and real-world 
+examples into account.
+
+
+#### Who are the annotators?
+
+<!-- This section describes the people or systems who created the annotations. -->
+
+The annotation of the permissible upper and lower signs was performed by the authors.
+
+#### Personal and Sensitive Information
+
+<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
+
+The dataset contains no data that might be considered personal, sensitive, or private.
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+- **Limited Scope** The analysis focuses mainly on the trade-off between motion blur and additive image noise.
+  For a comprehensive robustness analysis of a traffic sign recognition application, additional types of corruptions should consequently
+  be examined.
+
+- **Traffic Signs:** The wear and tear generation is limited to artifacts such as color fading, scratches, screw holes, and sticker residues.
+  Complex stickers, graffiti, or dirt are not included. Retroreflector patterns are excluded, and retroreflection is not simulated.
+  The traffic signs are solely mounted on metallic traffic sign poles.
+
+- **Geographic Bias** The dataset comprises a subset of exclusively German traffic signs.
+
+- **Environment:** Environmental variation includes no adverse weather conditions (snow, raindrops, fog, ...).
+
+- **Occlusions:** All included occlusions or shadows stem from a single 3D tree geometry.
+
+- **Camera:** Only one set of intrinsic camera parameters is used, and only a single camera lens type
+  (based on a Tamron M112FM35 35 mm lens) is simulated. 
+  It can be assumed that the set of simulated imaging artifacts is not complete.
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+<!-- Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. -->
+
+It is recommended to use the dataset primarily for scientific research. Application to practical real-world use cases should include
+human oversight and exhaustive evaluation of the fitness for the respective purpose, including the impact of domain shifts.
+
+## Dataset Card Contact
+
+Anne Sielemann\
+Fraunhofer IOSB\
+Group »Automotive and Simulation«\
+Fraunhoferstr.  |  76131 Karlsruhe  |  Germany\
+anne.sielemann@iosb.fraunhofer.de\
+[www.iosb.fraunhofer.de](https://www.iosb.fraunhofer.de)
+
+Jens Ziehn\
+Fraunhofer IOSB\
+Group leader »Automotive and Simulation«\
+Fraunhoferstr.  |  76131 Karlsruhe  |  Germany\
+Phone +49 721 6091 – 633\
+jens.ziehn@iosb.fraunhofer.de\
+[www.iosb.fraunhofer.de](https://www.iosb.fraunhofer.de)