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TRIMIS

Highly Automated Air Traffic Controller Workstations with Artificial Intelligence Integration

PROJECTS
Funding
European
European Union
Duration
-
Status
Ongoing
Geo-spatial type
Other
Total project cost
€1 825 000
EU Contribution
€1 825 000
Project Acronym
HAAWAII
STRIA Roadmaps
Network and traffic management systems (NTM)
Transport mode
Airborne icon
Transport policies
Societal/Economic issues,
Safety/Security
Transport sectors
Passenger transport,
Freight transport

Overview

Call for proposal
H2020-SESAR-2019-2
Link to CORDIS
Objectives

Advanced automation support developed in Wave 1 of SESAR IR includes using of automatic speech recognition (ASR) to reduce the amount of manual data inputs by air-traffic controllers. Evaluation of controllers’ feedback has been subdued due to the limited recognition performance of the commercial of the shell ASR engines that were used, even in laboratory conditions. The reasons for the unsatisfactory conclusions include e.g. inability to distinguish controllers’ accents, deviations from standard phraseology and limited real-time recognition performance. Past exploratory research funded project MALORCA, however, has shown (on restricted use-cases) that satisfactory performance can be reached with novel data-driven machine learning approaches.

Based on the results of MALORCA HAAWAII project aims to research and develop a reliable, error resilient and adaptable solution to automatically transcribe voice commands issued by both air-traffic controllers and pilots. The project will build on very large collection of data, organized with a minimum expert effort to develop a new set of models for complex environments of Icelandic en-route and London TMA.

HAAWAII aims to perform proof-of-concept trials in challenging environments, i.e. to be directly connected with real-life data from ops room. As pilot read-back error detection is the main application, HAAWAII aims to significantly enhance the validity of the speech recognition models.

The proposed work goes far beyond the work planned for the Wave 2 IR programme and will improve both safety and reduce controllers’ workload. The digitization of controller and pilot voice utterances can be used for a wide variety of safety and performance related benefits including, but not limiting to pre-fill entries into electronic flight strips and CPDLC messages. Another application demonstrated during proof-of-concept will be to objectively estimate controllers’ workload utilising digitized voice recordings of the complex London TMA.

Funding

Parent Programmes
Institution Type
Public institution
Institution Name
European Commission
Type of funding
Public (EU)
Specific funding programme
H2020-EU.3.4.7. - SESAR JU
Other Programme
SESAR-ER4-18-2019 Automation and CWP

Partners

Lead Organisation
Organisation
Deutsches Zentrum Fr Luft Und Raumfahrt E.v
Address
Linder Hoehe, 51147 KOELN, Germany
Organisation website
EU Contribution
€520 000
Partner Organisations
Organisation
Austro Control Osterreichische Gesellschaft Fur Zivilluftfahrt Mbh
Address
WAGRAMER STRASSE 19, 1220 WIEN, Austria
EU Contribution
€32 129 388
Organisation
Austro Control Osterreichische Gesellschaft Fur Zivilluftfahrt Mbh
Address
WAGRAMER STRASSE 19, 1220 WIEN, Austria
EU Contribution
€100 000
Organisation
Isavia Ans Ehf
Address
REYKJAVIKURFLUGVELLI 102, 102 REYKJAVIK, Iceland
EU Contribution
€260 000
Organisation
Croatia Control, Croatian Air Navigation Services Ltd
Address
RUDOLFA FIZIRA 2, 10410 VELIKA GORICA, Croatia
EU Contribution
€25 000
Organisation
Vysoke Uceni Technicke V Brne
Address
Antoninska 548/1, 60190 Brno, Czechia
Organisation website
EU Contribution
€300 000
Organisation
Nats
Address
4000 Parkway, whiteley 4000, Fareham, Hampshire, PO157FL, United Kingdom
Organisation website
EU Contribution
€260 000
Organisation
Fondation De L'institut De Recherche Idiap
Address
RUE MARCONI 19, 1920 MARTIGNY, Switzerland
EU Contribution
€5 485 750
Organisation
Fondation De L'institut De Recherche Idiap
Address
RUE MARCONI 19, 1920 MARTIGNY, Switzerland
EU Contribution
€360 000

Technologies

Technology Theme
Information systems
Technology
Machine learning for air traffic management
Development phase
Validation

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