A Concept for Requirements-Driven Identification and Mitigation of Dataset Gaps for Perception Tasks in Automated Driving

Mohamed Sabry Moustafa, Maarten Bieshaar, Andreas Albrecht, Bernhard Sick

2025

Abstract

The development of reliable perception machine learning (ML) models is critical for the safe operation of automated vehicles. However, acquiring sufficient real-world data for testing and training these models is not only time-consuming and dependent on chance, but also presents significant risks in safety-critical situations. To address these challenges, we propose a novel requirements-driven, data-driven methodology leveraging state-of-the-art synthetic data generation techniques in combination with tailoring real-world datasets towards task-specific needs. Our approach involves creating synthetic scenarios that are challenging or impossible to capture in real-world environments. These synthetic datasets are designed to enhance existing real-world datasets by addressing coverage gaps and improving model performance in cases represented by such gaps in real world. Through a rigorous analysis based on predefined safety requirements, we systematically differentiate between gaps arising from insufficient knowledge about the system operational design domain (e.g., underrepresented scenarios) and those inherent to data. This iterative process enables identifying and mitigating underrepresented scenarios, particularly in safety-critical and underrepresented scenarios, leading to local improvement in model performance. By incorporating synthetic data into the training process, our approach effectively mitigates model limitations and contributes to increased system reliability, in alignment with safety standards such as ISO-21448 (SOTIF).

Paper Citation

in Harvard Style

Moustafa M., Bieshaar M., Albrecht A. and Sick B. (2025). A Concept for Requirements-Driven Identification and Mitigation of Dataset Gaps for Perception Tasks in Automated Driving. In Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-730-6, SciTePress, pages 385-392. DOI: 10.5220/0013309200003905

in Bibtex Style

@conference{icpram25,
author={Mohamed Moustafa and Maarten Bieshaar and Andreas Albrecht and Bernhard Sick},
title={A Concept for Requirements-Driven Identification and Mitigation of Dataset Gaps for Perception Tasks in Automated Driving},
booktitle={Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2025},
pages={385-392},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013309200003905},
isbn={978-989-758-730-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - A Concept for Requirements-Driven Identification and Mitigation of Dataset Gaps for Perception Tasks in Automated Driving
SN - 978-989-758-730-6
AU - Moustafa M.
AU - Bieshaar M.
AU - Albrecht A.
AU - Sick B.
PY - 2025
SP - 385
EP - 392
DO - 10.5220/0013309200003905
PB - SciTePress