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.

CONCERTO aims to provide the means to maximize the opportunities for CO2 reduction and to introduce non-CO2 impact management in daily operations. The goal is to reach TRL6 processes and tools that allow the ecosystem to achieve more eco-friendly trajectories and thus higher climate impact reduction without requiring major changes on legacy ATM and airlines systems. To do so, two specific objectives are declined for CONCERTO. The first one focuses on CO2 emissions reduction when the second one introduces non-CO2 climate impact reduction mitigation means. Finally, as CONCERTO is a data driven project, generating data on CO2 and non-CO2 effects and opportunities at TMA, ATSU and possibly network level, the project presents the opportunity to contribute to the improvement of european environmental performances dashboards. Ambition #1- Keep the pace on CO2 emissions reduction, integrate Green ATC capacity in ATM processes, with the appropriate level of automation, and support ATM actors in balancing permanently regularity and environmental performance at local and network level. Ambition #2- By leveraging state-of-the-art climate science, allow ATM actors to take their “eco-responsibility” to the next level Ambition #3- Strengthen cooperation, anticipation and exchange of information between NM, Air Traffic Control, Airlines and Flight Planning Service providers. Ambition #4- Demonstrate that mitigation measures such as the eco-friendly orchestrator and ECHO area avoidance concepts can be deployed progressively at network level, in close connection with scientific progress