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.
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.
To check that these indexes are really able to measure these factors in an operational environment (traffic control center), they will be tested in an ad-hoc validation experiment.
The context used will be the one of the Free Route Airspace. Tools will be developed able to support in a semi or totally automated way controllers’ tasks, including decision making (e.g. how to solve conflicts) and the implementation of decisions (e.g. giving orders to aircraft). The neurophysiological indexes will be used to evaluate the impact of these automations on controllers’ performance.
The radar environment in Anadolu and the En-route environment in ENAC have been chosen as operational environments to be simulated.