AISA · Project

AI Co-Pilot That Shares Situational Awareness With Air Traffic Controllers

transportPrototypeTRL 4

Imagine air traffic controllers juggling dozens of planes at once — now imagine giving them an AI co-pilot that sees exactly what they see, understands the same rules, and can explain its thinking. That's what this project built: a system combining pattern recognition with a reasoning engine so that AI and humans share the same mental picture of what's happening in the sky. When the AI spots a problem, it doesn't just flag it — it tells you why, using the same logic a trained controller would use. And if the AI makes a bad prediction, the system can catch that too.

By the numbers

EUR 990,125

EU contribution for developing AI situational awareness for ATM

consortium partners across 6 countries

project deliverables produced

industry partners involved in development

The business problem

What needed solving

Air traffic is growing but controller capacity isn't keeping up. Automating individual tasks like conflict detection helps, but current AI tools can't explain their reasoning and don't share the full situational picture with human controllers. This makes it hard to trust and safely integrate automation into live air traffic operations.

The solution

What was built

A proof-of-concept knowledge graph system that combines machine learning (for predictions and pattern detection) with a reasoning engine (for drawing conclusions and explaining them). The project produced 16 deliverables total, including the proof-of-concept KG system designed for en-route air traffic control operations.

Audience

Who needs this

Air Navigation Service Providers (ANSPs) like Eurocontrol, NATS, DFSAviation software vendors building next-gen ATM systemsAirport operators managing complex terminal airspaceDefense and military air operations centersDrone traffic management (UTM) platform developers

Business applications

Who can put this to work

Air Traffic Management

enterprise

Target: Air Navigation Service Provider

If you are an Air Navigation Service Provider struggling with growing traffic volumes and controller workload — this project developed a proof-of-concept AI system that shares situational awareness with controllers, enabling them to trust and verify automated decisions. Built with 7 partners across 6 countries under the SESAR programme, the system combines machine learning with knowledge graphs so the AI can explain its reasoning the same way a controller would.

Aviation Software

mid-size

Target: ATM system integrator or software vendor

If you are an aviation software company building the next generation of air traffic management tools — this project created a knowledge-graph-based reasoning engine that makes AI decisions transparent and explainable. Instead of black-box automation, controllers get AI that shows its work. The proof-of-concept system was developed under SESAR with input from 2 industry partners, giving it direct alignment with European ATM modernization goals.

Drone Traffic Management

any

Target: UTM platform developer or urban air mobility operator

If you are developing drone traffic management or urban air mobility platforms — the AI situational awareness approach from this project can help autonomous systems and human operators share a common operating picture. The knowledge graph and reasoning engine were designed to catch machine learning errors and explain decisions, which is critical when scaling unmanned operations where no single human can monitor everything.

Frequently asked

Quick answers

What would it cost to license or adopt this technology?

Based on available project data, the total EU contribution was EUR 990,125 for a research and innovation action. Licensing terms would need to be discussed directly with the coordinator (University of Zagreb). As a publicly funded RIA project, some results may be available under open or preferential licensing conditions.

Can this scale to handle real-world air traffic volumes?

The project produced a proof-of-concept knowledge graph system focused on en-route ATC operations. Scaling to full operational volumes would require further engineering and validation. The SESAR alignment (topic SESAR-ER4-01-2019) positions this on the European ATM modernization roadmap, which provides a pathway to operational deployment.

Who owns the intellectual property?

The consortium of 7 partners across 6 countries developed the technology. IP ownership typically follows Horizon 2020 rules where each partner owns results they generate. Contact the coordinator for specific licensing arrangements and IP access.

How does this compare to existing ATM automation tools?

Current automation tools handle isolated tasks like conflict detection without sharing the full situational picture with controllers. AISA's approach is different: it builds shared team situational awareness between AI and humans, and the AI can explain its reasoning. The system also includes a mechanism to detect when machine learning makes false estimates.

Is this certified or approved for operational use?

Based on available project data, this is at proof-of-concept stage — not yet certified for operational air traffic control. Aviation certification is a multi-year process. The SESAR programme alignment means the results feed into the European ATM master plan, which defines the pathway to operational validation and deployment.

What is the timeline to a deployable product?

The project ran from June 2020 to November 2022 and produced a proof-of-concept. Moving from proof-of-concept to an operationally deployable system in aviation typically requires several additional years of validation, testing, and certification. A commercial partner or follow-on SESAR project would be needed to advance the technology.

Consortium

Who built it

The AISA consortium brings together 7 partners from 6 countries (Austria, Switzerland, Germany, Spain, Croatia, Hungary), led by the University of Zagreb's Faculty of Transport and Traffic Sciences. The mix is research-heavy: 5 universities and 2 industry partners (29% industry ratio), with 1 SME. This academic-leaning composition reflects the exploratory nature of the work — building the scientific foundation for AI situational awareness. The 2 industry partners provide essential real-world ATM context, while the multi-country spread ensures alignment with European airspace integration goals under SESAR.

SVEUCILISTE U ZAGREBU FAKULTET PROMETNIH ZNANOSTICoordinator · HR
SLOT CONSULTING KERESKEDELMI, SZOLGALTATO, TANACSADO KORLATOLT FELELOSEGU TARSASAGparticipant · HU
SKYGUIDE, SA SUISSE POUR LES SERVICES DE LA NAVIGATION AERIENNE CIVILS ET MILITAIRESparticipant · CH
UNIVERSITAT LINZparticipant · AT
TECHNISCHE UNIVERSITAET BRAUNSCHWEIGparticipant · DE
ZURCHER HOCHSCHULE FUR ANGEWANDTE WISSENSCHAFTENparticipant · CH
UNIVERSIDAD POLITECNICA DE MADRIDparticipant · ES

How to reach the team

University of Zagreb, Faculty of Transport and Traffic Sciences (Croatia) — reach out via university channels or project website contact page

Order a report on this consortium See SVEUCILISTE U ZAGREBU FAKULTET PROMETNIH ZNANOSTI profile →

Next steps

Talk to the team behind this work.

Want to explore how AISA's AI situational awareness technology could fit your ATM operations or aviation software? SciTransfer can arrange a direct introduction to the research team and help evaluate the business case.

Order a report on this topic More in Transport & Mobility