Background and challenges
Air Traffic Management (ATM) systems are becoming increasingly automated and AI-driven. While this improves efficiency, it also creates new vulnerabilities when facing High Impact Low Probability (HILP) events, such as extreme weather, cyberattacks or major system failures. These rare events are difficult to predict due to limited historical data and complex cascading effects across interconnected systems. Ensuring that automated ATM solutions remain safe, secure and resilient under extreme conditions is therefore a critical challenge for the aviation sector.
SINTEF’s contribution and implementation
SINTEF plays a central role in ATM‑RARE by strengthening the resilience, cybersecurity and trustworthiness of highly automated ATM systems. SINTEF’s work focuses on:
- Resilience and risk modelling: analysing how ATM systems behave under extreme disruptions and identifying critical vulnerabilities and cascading effects.
- Cybersecurity of automated systems: assessing risks in digital ATM infrastructures and developing mitigation strategies for emerging cyber threats.
- AI-based methods: applying machine learning and probabilistic models to detect early warning signals (precursors) of HILP events.
Methods and tools
The project relies on advanced software development, data analytics and simulation-based studies. SINTEF leverages its expertise in AI, cybersecurity testing and scenario-based simulations to deliver robust and scalable solutions for future ATM systems.
SINTEF leads the validation of ATM‑RARE solutions in realistic operational scenarios, translating research results into practical impact through key use cases such as airport power blackout and automated runway sequencing.
