STRESS started from the analysis of the current expectations of ATM stakeholders towards automation. European research agenda is working to introduce higher levels of automation in air traffic control. In the future, a new generation of highly automated supporting technologies will be developed. They are expected to autonomously (or partially autonomously) manage tasks that are currently carried out by human operators and/or to support humans in making decisions that the operators will hardly be in a position to question.
This new scenario will inherently change the role of the technology, its interactions with the operators and the capability of the operators to judge the quality of the information provided. This, in turn, will imply the need to consider the new systemic risks and mitigations that can be associated with technological and/or organisational failures. It will also imply the need for a radical revision of the competences required to perform tasks – as well as of how tasks, roles and responsibilities are allocated among the operators (both in the front-end and in the back-end) and between operators and machines.
To address all these implications the project selected as a theoretical framework for automation definition and classification the research carried out by different authors such as Sheridan, Parasuraman and Bainbridge.
Workshop on stress and attention in future ATM scenarios, with the collaboration of Padova ACC controllers
Human Performance Indexes
These have been recognized to be the most impacted Human Factors issues .
For this reason, they will be investigated through neurophysiological measurement tools.
In particular, the project designed 4 indexes able to assess these factors using electroencephalography (EEG), eye-tracker and skin-conductance-response measurement tools.
These indexes have been tested in laboratory, with ad-hoc designed simple tasks simulating different levels of attention, stress and cognitive control. The tests took place at the Sapienza University in Rome.
Indexes test and refinement at the Sapienza University laboratories
A total of 16 freshly graduated air traffic controllers took part in the validation activities, and were asked to manage a realistic operational scenario developed to induce different levels of worklaod, attention and stress.
Their neurophysiological signals were recorded continuously, and the data collected then compared with behavioural and performance data (looking at how controllers handled the traffic) and subjective data (feedback from experimental subjects and external experts) to verify that the indexes are capable of assessing attention and stress.
The video shows the experimental subjects managing the simulated air traffic, and the recording of the data.
The validation activity held at Anadolu Univrsity
The impact of automation on human performance
To do that, ENAC developed a platform able to simulate highly automated tools for en-route controllers. In February, this platform has been used to study what happens to controllers attention, stress, workload and type of cognitive control when interacting with advanced solutions, and how they react in case of transitions to lower levels of automation (when failures or degradation happen).
The validation activity held at ENAC
Communication and dissemination
During the project, several activities have been carried on to advertise the project and share its results: brochure, website, social campains, participation to conferences and meetings.
U. Turhan, B. Açıkel, T.Güneş, Discussion on automation Effects for Human Operator’s Performance in the Aviation
1st International Symposium on Multidisciplinary Studies and Innovative Technologies, 2-4 November 2017 Tokat, Türkiye.
G. Borghini, M. Ragosta, P. Aricò, S. Bonelli, G. Di Flumeri, N. Sciaraffa, P. Tomasello, D. Mancini, A. Colosimo, F. Babiloni, Development of Neurometrics for Selective Attention Evaluation in ATM
7th SESAR Innovation Days, 28-30 November 2017, Belgrade, Serbia.
G. Borghini, P. Aricò, G. Di Flumeri, N. Sciaraffa, A. Colosimo, M.T. Herrero, A. Bezerianos, N. Thakor, F. Babiloni, A New Perspective for the Training Assessment: Machine Learning-Based Neurometric for Augmented User’s Evaluation, Frontiers in Neuroscience, 11, 2017
P. Aricò, G. Borghini, G. Di Flumeri, A. Colosimo, S. Bonelli, A. Golfetti, S. Pozzi, J.P. Imbert, G. Granger, R. Benhacene, F. Babiloni, Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment Frontiers in Neuroscience, 10, 2018