How can neuroscience support the development of ATM in the future?

How can neuroscience support the development of ATM in the future?

How can neuroscience support the development of ATM in the future?

As the role of air traffic controllers shifts to a more observatory role, does neuroscience hold the key to ensuring this change doesn’t affect air traffic flows?

Air traffic is growing as is its complexity. Due to the progressive increase of automation levels, the adoption of innovative concepts such as 4D trajectories, and the introduction of drones into the airspace, experts expect the European air traffic management (ATM) system to face drastic challenges in the nearest future.

Soon, the roles and tasks of controllers will change, and it is vital to enhance the comprehension of human response to such changes. It is also vital to develop tools to investigate aspects like the ability to monitor complex situations and face unexpected disruptions, and to monitor in real time controllers’ fitness to the task, in order to anticipate risks and problems.

The Human Performance Envelope

Several aspects such as stress, workload, attentional resources available, attention focus and so on, impact controllers’ performance. In recent years, the concept of “Human Performance Envelope” (HPE) emerged as a new paradigm in human factors (HFs) to account for this complexity. Rather than focusing on one or two individual factors in isolation (e.g. workload, attention), it considers a range of common factors in accidents and maps how they work in combination to lead to a performance decrement that could affect safety.

It is reasonable to expect that the future air traffic control officer’s (ATCO) HPE will be different from the one we would use today. It will have different underlying HF concepts, or at least a different weight among them. For instance, ATCOs are expected to move to a monitoring position of highly-automated systems, with very few tactical interventions, strategic planning by exception when automation cannot find a solution and the need to intervene rapidly to recover disruptions or unexpected events. As compared to pilots, workload may be even less primary, but with sudden bursts when recovery actions are needed. Indeed, stress will be a major factor both in normal conditions, when ATCOs will need to rely on automation without actively controlling it, and in disruptions. Such a monitoring role will probably require even more attention than pilots exert today. In fact, ATCOs will need to deal with very complex systems, with many interacting elements of different typologies (e.g. RPAS) moving in 4D trajectories across space.

Currently, the main research challenge for complex systems is to explore the HPE in highly-automated environments in an innovative and reliable way. This investigation can provide new knowledge and guidelines needed for designing and implementing higher levels of automation and the related procedures and humans’ roles.

To address this issue Deep Blue, a SME based in Rome and specialist in human factors and safety, coordinated the NINA and STRESS research projects. NINA and STRESS were part of a wider research initiative aimed at investigating the application of neuroscience to the development of new technologies for ATM. In fact, these projects investigated the use of neurophysiological indicators to assess air traffic controllers’ mental state during the execution of operational tasks in highly-automated scenarios. The investigation aimed at deriving guidelines and principles for the design of future ATM systems. The European Commission co-financed both projects in the framework of the SESAR Exploratory Research programme.

Neurometrics at work

Neurophysiological indicators are quite advanced today, offering a unique opportunity to objectively monitor the factors composing the HPE. However, a research gap remains in place concerning the customisation of these indicators to (future) ATM tasks. Both STRESS and NINA aimed to fill this gap. While neurophysiology knows what to monitor to detect stress, what we call stress in ATM may correspond to different patterns of neurological activity as compared to everyday stress. This consideration also applies to other complex HFs concepts like attention and vigilance, which have an everyday meaning and are being studied in contexts different from ATM. In addition, aviation research on neurophysiological indicators has mostly focused on cognitive concepts, traditionally disregarding the stress-related aspects. This oversimplification is hard to justify at the light of current neurophysiological knowledge, where the stress-response has been shown to play a key role in “cognitive” processes like decision-making or attentional focus. A good example is the “startle effect”, defined as an automatic reflex elicited by exposure to a sudden, intense event that violates a pilot’s expectations, and is currently one of the hot topics for pilot performance.

The relevance of stress is also recognised by the EASA, that in the Notice of Proposed Amendment (NPA) addresses the issue of licensing and medical certification of air traffic controllers (EASA, 2012), considering stress and fatigue management as an essential topic for training (AMC1 ATCO.D.045(c)(4) human factors training). In particular, stress demands for a systematic approach. In fact, its importance is likely to grow as systems rely more on automation, and humans move to monitoring positions. A typical case is the automation disruption, when humans have to react quickly in highly-stressful conditions. In these situations, stress is known to influence performance and impair attention, memory and decision-making (Angeli et al., 2004).

In order to capture this level of complexity, STRESS and NINA proposed a multidisciplinary approach. They implemented the high time resolution neurophysiological measurement of air traffic controllers’ stress, workload, attention, cognitive control and vigilance during the execution of operational tasks, within a simulated air traffic control environment reproducing the complexity of future airspace scenarios and associated highly-automated technologies. To achieve this, they carried out data fusion of the following measures: Neural patterns of brain activation (EEG), physiologic indicators (heart activity, galvanic skin response), kinematics (body posture data like joint angles, segment kinematics, segment global positions, body centre of mass) and eye tracking.

The composition of the projects Consortia engaged in these projects reflected such multidisciplinarity, bringing together partners with different expertise. Their competence profiles include a strong understanding of human factors (Deep Blue), a solid experience in the use of neurophysiologic measurements (Sapienza University), a deep knowledge of air traffic management domain (ENAC and Anadolu University), and an overall view on what is the strategic agenda for the development of this domain in the upcoming years (EUROCONTROL).

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