Coverage-Driven Verification for Safety-by-Design in AI-Based Collision Avoidance Systems
Artificial Intelligence (AI) offers significant potential for future aviation systems; however, its integration into safety-critical applications requires compliance with the aviation sector's stringent safety standards. For AI and Machine Learning (ML)-based systems, the European Union Aviation Safety Agency (EASA) emphasizes the need to demonstrate the representativeness and completeness of the Operational Design Domain (ODD) and the associated data distributions used during development and verification. Despite this requirement, a structured engineering process for defining target distributions and evaluating representativeness within ODDs remains largely unexplored. This work presents...
| Authors | Thomas Stefani, Johann Maximilian Christensen, Elena Hoemann et al. |
|---|---|
| Primary category | cs.AI |
| Published | 21 August 2026 |
| Last updated at source | 21 August 2026 |
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