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Fundamental Science and Outreach for Capacity-on-demand and Dynamic Airspace

The SESAR 3 JU has identified the following innovative research elements that could be used to achieve the expected outcomes. The list is not intended to be prescriptive; proposals for work on areas other than those listed below are welcome, provided they include adequate background and justification to ensure clear traceability with the R&I needs set out in the SRIA for the capacity on demand and dynamic airspace flagship.

  • Human performance challenges in an ATM environment with higher levels of automation. The ATM system can be seen as a joint human–machine cognitive system deriving from an integrated design that optimises the collaboration of actors with a view to improving system performance (R&I need: on-demand ATS). The research will address:
    • human performance aspects related to higher levels of automation (e.g. stress, lack of attention, deskilling, complacency), and resilience by, for example, developing new approaches to determining suitable task allocation strategies enabling cooperation between ATCOs and/or other ATM actors and automation;
    • the development of methods to enable automation to adapt to changes in the environment, such as changes in the behaviour of actors (e.g. modification of operational procedures), the entrance of new actors, and unforeseen traffic or weather situations/disruptions;
    • the development and assessment of suitable ML methods for ATM automation that are able to predict which information needs to be provided to enable the human operator to cooperatively work together with the automation;
    • new training requirements and programmes for ATCOs and other ATM personnel that take into account the implications of the expected future role of the human and the introduction of new support tools;
    • the use of psychophysiological measurements (e.g. neurometrics or the detection of facial expressions) for applications such as stress management systems, fatigue declaration, new training techniques and adaptive automation;
    • the privacy and acceptability aspects of the proposed solutions.
  • Legal and regulatory challenges in an ATM environment with higher levels of automation. This element covers the challenges related to the means of approval/certification of novel ATM-related airborne and ground systems that enable higher levels of automation (in particular those systems based on ML techniques) by considering both legal and regulatory aspects (including privacy), along with technical aspects (e.g. architecture, system performance, reliability). In addition, potential changes to ATCOs’ licences and training will be assessed, including with regard to the use of conflict detection and resolution support tools by ATCOs in order to ensure capacity growth, in contrast with the trend towards creating smaller sectors where capacity benefits reach a finite limit (R&I need: on-demand ATS).
  • Increasing the use of middle airspace. This element addresses the potential business case for increasing the use of middle airspace (approximately between 15 000 ft and 25 000 ft) by jet engine aircraft and the trade-offs between increased capacity (and reduced delays) through the provision of ATFM slots for flights in middle airspace and increased fuel consumption and environmental impact (R&I need: on-demand ATSs).
  • Integration of air vehicles and personal air vehicles. In the future, new UAVs and personal air vehicles will fly long range and at higher altitudes to feed airports. This research will investigate the necessary seamless integration of those personal air vehicles into a more automated ATM (R&I need: on-demand ATS).
  • Models and theories of behaviour change. An active role for the human factor in system design will be vital to support the transition from the tactical involvement of controllers to management of traffic “by exception”. Research is needed to understand and manage the impact of system changes on human performance and workload in the long term. Readiness to change, barriers to change and likelihood of relapse should be addressed in system design, monitoring and improvement over the long term. Both models of behaviours and behavioural change theories should be investigated as diagnostic tools to explain and predict specific behaviours. Furthermore, resilience in handling abnormal situations should be addressed, in order to understand how this resilience can be maintained with reduced human involvement (R&I need: on-demand ATS).