Search

One of the most interesting challenges of the next few years will be airspace system automation. This process will involve different aspects such as air traffic management, aircraft and airport operations and guidance and navigation systems. The use of unmanned aerial systems for civil missions will be one of the most important steps in this automation process. In this article, an air traffic management oriented conflict detection & resolution algorithm that models air traffic management operative technique as avoidance manoeuvres in order to self-separate the unmanned aerial vehicle from piloted air traffic is presented. As a first step, a geometric analysis identifies all possible unmanned aerial vehicle routes among the mission targets and related potential conflicts with piloted air traffic. For each potential conflict, air traffic management operative techniques are used to model different options of conflict resolution: vertical and horizontal avoidance, speed regulation, holding patterns and rerouting. The performances of a reference unmanned aerial vehicle are used to estimate the cost of each possible sub-route and, in case of conflict, the cost of each possible avoidance manoeuvre. In this way, the unmanned aerial vehicle mission is modelled as a combinatorial optimization problem that concerns the sequencing of both targets and conflict resolution options. As output, a conflict free route that minimizes the air traffic impact over the mission is provided. Simulation results over real air traffic data show how this approach could be useful for future common management of piloted and non-piloted air traffic. [ABSTRACT FROM PUBLISHER]

Increasing the level of automation in air traffic management is seen as a measure to increase the performance of the service to satisfy the predicted future demand. This is expected to result in new roles for the human operator: he will mainly monitor highly automated systems and seldom intervene. Therefore, air traffic controllers (ATCos) would often work in a supervisory or control mode rather than in a direct operating mode. However, it has been demonstrated how human operators in such a role are affected by human performance issues, known as Out-Of-The-Loop (OOTL) phenomenon, consisting in lack of attention, loss of situational awareness and de-skilling. A countermeasure to this phenomenon has been identified in the adaptive automation (AA), i.e., a system able to allocate the operative tasks to the machine or to the operator depending on their needs. In this context, psychophysiological measures have been highlighted as powerful tool to provide a reliable, unobtrusive and real-time assessment of the ATCo's mental state to be used as control logic for AA-based systems. In this paper, it is presented the so-called "Vigilance and Attention Controller", a system based on electroencephalography (EEG) and eye-tracking (ET) techniques, aimed to assess in real time the vigilance level of an ATCo dealing with a highly automated human-machine interface and to use this measure to adapt the level of automation of the interface itself. The system has been tested on 14 professional ATCos performing two highly realistic scenarios, one with the system disabled and one with the system enabled. The results confirmed that (i) long high automated tasks induce vigilance decreasing and OOTL-related phenomena; (ii) EEG measures are sensitive to these kinds of mental impairments; and (iii) AA was able to counteract this negative effect by keeping the ATCo more involved within the operative task. The results were confirmed by EEG and ET measures as well as by performance and subjective ones, providing a clear example of potential applications and related benefits of AA.