most citedDefining Operational Conditions for Safety-Critical AI-Based Systems from Data

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cs.AI2026

Coverage-Driven Verification for Safety-by-Design in AI-Based Collision Avoidance Systems

Thomas Stefani, Johann Maximilian Christensen, Elena Hoemann +2

Artificial Intelligence (AI) offers significant potential for future aviation systems; however, its integration into safety-critical applications requires compliance with the aviat…

cs.AI2026

On the Applicability of Safety Nets: A Safety-By-Design Solution for Certifying Neural Networks

Johann Maximilian Christensen, Thomas Stefani, Elena Hoemann +2

The integration of Artificial Intelligence (AI) in safety-critical aviation systems presents significant challenges for certification and deployment. Aviation, often regarded as th…

cs.AI2026

From High-Dimensional Spaces to Verifiable ODD Coverage for Safety-Critical AI-based Systems

Thomas Stefani, Johann Maximilian Christensen, Elena Hoemann +2

While Artificial Intelligence (AI) offers transformative potential for operational performance, its deployment in safety-critical domains such as aviation requires strict adherence…

cs.AI20261 cited

Defining Operational Conditions for Safety-Critical AI-Based Systems from Data

Johann Maximilian Christensen, Elena Hoemann, Frank Köster +1

Artificial Intelligence (AI) has been on the rise in many domains, including numerous safety-critical applications. However, for complex systems in the real world, defining the und…

cs.AI2023

Towards solving ontological dissonance using network graphs

Maximilian Staebler, Frank Koester, Christoph Schlueter-Langdon

Data Spaces are an emerging concept for the trusted implementation of data-based applications and business models, offering a high degree of flexibility and sovereignty to all stak…