[2604.02198] From High-Dimensional Spaces to Verifiable ODD Coverage for Safety-Critical AI-based Systems

Computer Science > Artificial Intelligence arXiv:2604.02198 (cs) [Submitted on 2 Apr 2026] Title:From High-Dimensional Spaces to Verifiable ODD Coverage for Safety-Critical AI-based Systems Authors:Thomas Stefani, Johann Maximilian Christensen, Elena Hoemann, Frank Köster, Sven Hallerbach View a PDF of the paper titled From High-Dimensional Spaces to Verifiable ODD Coverage for Safety-Critical AI-based Systems, by Thomas Stefani and Johann Maximilian Christensen and Elena Hoemann and Frank K\"oster and Sven Hallerbach View PDF Abstract:While Artificial Intelligence (AI) offers transformative potential for operational performance, its deployment in safety-critical domains such as aviation requires strict adherence to rigorous certification standards. Current EASA guidelines mandate demonstrating complete coverage of the AI/ML constituent's Operational Design Domain (ODD) -- a requirement that demands proof that no critical gaps exist within defined operational boundaries. However, as systems operate within high-dimensional parameter spaces, existing methods struggle to provide the scalability and formal grounding necessary to satisfy the completeness criterion. Currently, no standardized engineering method exists to bridge the gap between abstract ODD definitions and verifiable evidence. This paper addresses this void by proposing a method that integrates parameter discretization, constraint-based filtering, and criticality-based dimension reduction into a structured, multi...