• Project ID SafeOPS
  • Project duration 2021-01-01 > 2022-12-31
  • Cost
    • Total EUR 997.750,00 €
    • EU Contr. EUR 997.750,00 € €
  • Status Ongoing

SafeOPs* aims at developing a decision-support tool powered by AI to help air traffic controllers take complex decisions in the context of go-arounds.

AI technologies are critical enablers for the digital transformation of air traffic management. They are capable of delivering reliable, predictive analytics based on automated information processing to further enhance current operations: safer, more resilient and efficient.

SafeOPS investigates how predictive AI tools can be used in ATM as decision support technologies, to facilitate controllers making complex decisions. SafeOPS incorporates end users (ANSPs and airlines) from the earliest stages to guarantee the alignment of the technology with the operational requirements.

On the other hand, predictive analytics introduce new challenges for the controllers and their training, as well as for regulators and certification agencies. SafeOPS aims to address these challenges to push the safe introduction of big data analytics in aviation operations.

SafeOPS

Based on operational data (FDM, ADS-B, Metar, ...), SafeOPS will develop a tool chain for go-around predictions to support air traffic controllers in their decision making. A risk model will also be provided to adequately interpret the risks associated with a go-around in the given context.

In order to pave the way towards the implementation of AI-driven solutions, SafeOPS will evaluate its contribution to safety and resilience of ATM systems. A special focus will be placed on the interaction among humans (controllers) and technology within the socio-technical system.

Participants

TECHNISCHE UNIVERSITAET MUENCHEN (Coordinator)

FUNDACION INSTITUTO DE INVESTIGACION INNAXIS

DFS DEUTSCHE FLUGSICHERUNG GMBH

DEEP BLUE SRL

PEGASUS HAVA TASIMACILIGI ANONIM SIRKETI

IBERIA LINEAS AEREAS DE ESPANA SA OPERADORA

*From Prediction to Decision Support - Strengthening Safe and Scalable ATM Services through Automated Risk Analytics based on Operational Data from Aviation Stakeholders

This project has received funding from the SESAR Joint Undertaking under the European Union's Horizon 2020 research and innovation programme under grant agreement No 892919

European Union