HAAWAII - Highly Automated Air Traffic Controller Workstations with Artificial Intelligence Integration

Artificial Intelligence , Machine Learning, Air-Traffic Control, Natural Language Processing, Automatic Speech Recognition, 

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 datadriven
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.