Update README.md

README.md

@@ -36,7 +36,30 @@ masks, segmentation images, and image-level JSON metadata.
 
 ## Description
 
-The <em>Synset Signset Sensitivity Analysis Dataset</em> 
+The <em>Synset Signset Sensitivity Analysis Dataset</em> is a large-scale, physically based synthetic test dataset designed to 
+systematically analyze the sensitivity of deep-learning perception models to sensing parameters. The dataset focuses on the task 
+of traffic sign recognition and was generated using the [OCTAS®](https://octas.org/) simulation platform, based on the 
+[Synset Signset Germany](https://synset.de/datasets/synset-signset-ger/) (SSG) data-generation pipeline. Rather than applying purely 
+artificial image corruptions trough image augmentations, the dataset represents degradations through physically meaningful parameters.
+
+The dataset investigates the trade-off between exposure time and camera gain, or ISO, under low-light conditions. Increasing the exposure 
+time can compensate for reduced irradiance but introduces motion blur, whereas increasing the camera gain enables shorter exposure times 
+at the cost of increased additive image noise. This trade-off is represented by systematically varying motion blur and noise across 
+combinations of sensing parameters. The parameter space comprises 36 motion-blur levels, ranging from 0 to 35 pixels, and 61 additive-noise 
+levels, ranging from 0.00 to 0.60, resulting in 2,196 subdatasets. The parameter combinations enable the analysis of model sensitivity 
+not only to individual sensing parameters but also to their interactions under comparable effective illumination conditions.
+
+Each subdataset contains 43 traffic sign classes from the 
+[German Traffic Sign Recognition Benchmark](https://ieeexplore.ieee.org/abstract/document/6033395) (GTSRB), with 100 images per class. 
+In total, the dataset comprises 9,442,800 test images rendered at a resolution of 48 × 48 pixels (approximately mean image size of GTSRB). 
+The images include stochastic variations in viewing angle, environmental illumination, and traffic sign surface appearance. 
+In addition to the RGB images, traffic sign masks, segmentation images, and image-level JSON metadata describing the selected 
+environmental and imaging parameters are provided.
+
+The dataset is intended to support reproducible sensitivity and robustness analyses of deep-learning models under challenging sensing 
+conditions. It can be used to examine performance changes across the blur–noise parameter space, compare model architectures and training 
+datasets, and characterize class-specific robustness. As a synthetic test dataset with a defined parameter scope, it does not represent 
+the full variability of real-world driving environments and should be complemented by evaluations on suitable real-world data.
 
 ## Citation and Reference
 
@@ -72,6 +95,9 @@ For example, as follows:
 
 ## Uses
 
+The dataset was intended to be used as an evaluation benchmark for machine-learning applications for the task of 
+German traffic sign recognition, with the aim of assessing their robustness regarding noise and motion blur.
+
 ### Direct Use 
 
 <!-- This section describes suitable use cases for the dataset. -->
@@ -85,6 +111,14 @@ one trade-off is more favorable for specific traffic sign classes or model archi
 
 <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
 
+The dataset should not be used for critical applications, particularly high-risk applications as named by the European AI Act 
+under Annex III (which includes "AI systems intended to be used for the ‘real-time’ and ‘post’ remote biometric identification 
+of natural persons" and "AI systems intended to be used as safety components in the management and operation of road traffic"), 
+without exhaustive research into the fitness of the dataset, to evaluate whether it is "relevant, sufficiently representative, 
+and to the best extent possible free of errors and complete in view of the intended purpose of the system." 
+No such claim is not made with the publication of this dataset.
+
+
 ## 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. -->
@@ -107,11 +141,36 @@ one trade-off is more favorable for specific traffic sign classes or model archi
 ### Curation Rationale
 
 <!-- Motivation for the creation of this dataset. -->
+The prediction of model performance during operation and the early identification of system limits are pivotal challenges in 
+the development of machine-learning systems for safety-critical applications such as automated driving. In particular, 
+the robustness of perception models to variations within the operational design domain (ODD) is difficult to quantify, 
+as the influence of environmental conditions and sensing parameters can not readily be derived from the system design.
+
+Existing approaches often augment real-world datasets with artificially introduced degradations. However, these 
+degradations cannot be directly related to physically meaningful properties of the target operational design domain (ODD). 
+This dataset was therefore created to demonstrate how physically based synthetic data can be leveraged to systematically 
+evaluate not only individual corruptions but also trade-offs and interactions between multiple sensing parameters. 
+Specifically, this work focuses on the trade-off between longer exposure times, which introduce motion blur, 
+and increased camera gain or ISO, which amplifies image noise.
+
+The dataset was designed to be comparable to the [GTSRB](https://ieeexplore.ieee.org/abstract/document/6033395) dataset, 
+one of the best known available traffic sign recognition benchmark datasets.
 
 ### Source Data
 
 <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
 
+- The dataset was generated in the [OCTAS®](https://octas.org/) simulation framework,
+  by using rasterization trough the [OGRE](https://ogre3d.org) engine.
+
+- The traffic sign template images, which are used as input to the GAN-based texture synthesis,
+  stem from the
+  [Wikipedia overview of German traffic signs](https://de.wikipedia.org/wiki/Bildtafel_der_Verkehrszeichen_in_der_Bundesrepublik_Deutschland_seit_2017).
+
+- Image-based lighting (IBL) uses environment maps from [PolyHaven](https://polyhaven.com/).
+
+- The 3D geometry of the tree that serves as an occlusion object originates from [PolyHaven](https://polyhaven.com/).
+
 #### 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. -->
@@ -132,7 +191,7 @@ one trade-off is more favorable for specific traffic sign classes or model archi
 
 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/), 
+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