Project description
Deep learning to help air traffic controllers
The future of air traffic management studies is grounded in machine learning processes aimed at improving the performance and accuracy of techniques. Simulation scenarios can provide deep learning opportunities for air traffic controllers. The collection, storage, processing and sharing of voice communications from real world air-traffic control data will be made possible on a new platform created by the EU-funded ATCO2 project. It will target spoken commands issued by the air traffic controllers and readback confirmations provided by pilots. The project will also access voice recordings from air navigation service providers such as Austro Control, which is Europe’s leading air traffic control organisation.
Fields of science
- natural sciencescomputer and information sciencescomputer securitydata protection
- engineering and technologymechanical engineeringvehicle engineeringaerospace engineeringaircraft
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- social scienceseducational sciencespedagogyactive learning
Programme(s)
Call for proposal
H2020-CS2-CFP09-2018-02
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Funding Scheme
CS2-IA - Innovation action
Coordinator
1920 Martigny
Switzerland
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Participants (6)
601 90 Brno Stred
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66123 Saarbrucken
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3400 Burgdorf
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61600 Brno Kralovo Pole
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75013 Paris
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47121 Forli
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