What question did this study set out to answer?

This research aims to align dataset construction with safety requirements for AI in automated driving systems.

May 10, 2026Open Access

What data do we need? – a model-based approach for scenario-driven dataset construction

MNMarcus NolteKhyber Teaching Hospital NSNayel Fabian SalemTechnische Universität Braunschweig MMMarkus MaurerTechnische Universität Braunschweig

Key Points

This research aims to align dataset construction with safety requirements for AI in automated driving systems.
Introduced a model-based approach for dataset requirements in AI safety lifecycle.
Investigated commonalities between Safety Engineering and dataset-related activities.
Demonstrated approach through a safety analysis example for an L4 hub-to-hub scenario.
Identified critical dataset requirements for safety analyses meeting ISO/PAS 8800 standards.
Established traceability from operational design domains to dataset specifications.
Improved understanding of AI behavioral impacts in safety-critical environments.

Abstract

Abstract The increased use of Artificial Intelligence (AI) in safety-critical systems such as Automated Driving Systems (ADS) requires a reevaluation of established Systems and Safety Engineering processes to accommodate AI characteristics. Datasets determine an AI system’s (potentially hazardous) behavior significantly. Hence, ISO/PAS 8800 demands dataset requirements, safety analyses, and validation activities in an AI safety lifecycle. This article investigates commonalities between Safety Engineering Activities specified in ISO 21448 and dataset-related activities specified in ISO/PAS 8800. We introduce a model-based approach to support traceability from knowledge about an Operational Design Domain to required dataset requirements and data labels for training and testing AI systems in the scope of ISO/PAS 8800. The approach is demonstrated in an example safety analysis for an L4 hub-to-hub scenario.

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