Search

International audience; 97% of Non-Stabilized Approach (NSA) are continued until landing going against Standard Opera- tional Procedures (SOP). For some of these approaches, the reason is a lack of situation awareness for others it is because of operational constraints that standard SOP do not take into account like ATC, remaining fuel on board, weather... Most of the time everything goes well but pilots often admit afterwards that they should have go-around and that safety margins were greatly reduced. In the frame of Clean Sky 2 JU, the HARVIS (Human Aircraft Roadmap for Virtual Intelligent System) project aims to identify how cognitive computing algorithms implemented in a digital assistant could support the decision making of a single pilot in complex situation at horizon 2035+. Such a digital assistant would have to mimic human behavior like being adaptive, interactive and contextual. To answer the NSA problem, this assistant could be fed with aircraft parameters and contextual information (ATC, weather, pilot physiological state...) and provide alerting in case of parameters deviations, suggestions of corrective action or even go-around advisories like a Pilot Monitoring is doing today in multi-crew operations. Our innovative approach is to use expertise from a large number of pilots on many relevant sce- narios to classify human judgement on real flight trajectories instead of relying on rules like SOP. Relevant segments of NSA based on Flight Data Recordings (FDR) and operational constraints will be presented on a web interface displaying parts of the flight deck for the pilots to give their judge- ment about the necessity to go-around or not. Thanks to this labelling, an assistant will be trained to recognize these situations and will provide real time support to decision-making. Unlike existing alerting and decisions-aided systems that are based on trajectory prediction and not human expertise, our assistant will be adaptive to the situation, being discreet if the approach goes well and more intrusive/directive when things go wrong. Instead of relying on the sole Pilot Monitoring point of view, the Pilot Flying will benefit from the expertise of hundreds of pilots. The acceptance of the assistant should be strengthened as a result of this original approach. This assistant will contribute to make flight more safe, economic and ecologic. As it will only advise and not make corrective actions, pilots will have the possibility to keep flying manually voluntarily contributing to maintaining their skill level. As a consequence, they will be more efficient in case of autopilot failure, thus contributing to safety. The argument is also economic and ecologic as the assistant may also decrease the number of go around by reducing the number of NSA. The implementation of HARVIS NSA assistant will be conducted in 2020 and user tests in 2021.

International audience; The aim of this work was to test a neuro-physiological methodology able to discriminate the Skill (S), Rule (R) and Knowledge (K) based cognitive control levels of Air-Traffic-Controllers’ performing realistic traffic management tasks . The three categories of human behaviours have been associated to specific cognitive functions (e.g. attention, memory, decision making) already investigated with Electroencephalography (EEG) measurements. A link between S-R-K behaviours and expected frequency bands configurations has been hypothesized. Eventually, specific events have been designed to trigger S, R and K like behaviours and then integrated into realistic Air Traffic Management (ATM) simulations. A machine-learning algorithm has been used to differentiate the three different levels of cognitive control by using brain features extracted from the EEG rhythms of different brain areas, that is, the frontal theta and the parietal alpha activities. Twelve professional Air-Traffic-Controllers (ATCOs) from the École Nationale de l’Aviation Civile (ENAC) of Toulouse (France) have been involved in the study. The results showed that the algorithm was able to differentiate with high discrimination accuracy (AUC > 0.7) the three S-R-K cognitive behaviours during simulated air-traffic scenarios in an ecological ATM environment

International audience; This paper shows how the CATS (Complete Air Traffic Simulator) can model the controller¡¯s conflict detection task using a sliding time window. Because of uncertainties, detection is by far the most time consuming and cognitively challenging aspect of air traffic control. The processes governing detection are distinct from resolution actions. Few conflicts detected lead to a resolution maneuver. Because future aircraft positions are uncertain, controllers detect many more conflicts or "potential conflicts" than actually occur. In this paper, we solve conflicts in a real time context and compare different uncertainty scenarios. We show how uncertainties impact the number of theoretical potential conflicts detected and the increase the number of unnecessary maneuver actions required to keep the traffic safe. When we deal with realistic uncertainty values, the majority of calculation time in CATS is used for calculating maneuvers that will usually not be transmitted to pilots. This observation reveals the true nature of the controller¡¯s workload today.

International audience; The RTA (Required Time Arrival) capabilities of aircraft FMS (Flight Management Systems) offer new opportunities to solve mid-term horizon conflicts (20 minutes in advance) with small speed adjustments. The ERASMUS project has shown promising results of up to $80\%$ conflict resolution using small speed adjustments in the $[-6\%;+3\%]$ range with 20 minutes advance notice. The hypotheses were based on very accurate trajectory predictions (TPs). In this article we show how the quality of these results decreases as the uncertainties on the trajectory prediction increase. Therefore we used the CATS (Complete Air Traffic Simulator) developed in the late 90s at CENA (Centre d'Etudes de la Navigation A\'erienne) and constrained the solver to use only speed maneuvers for leveled or descending aircraft with different hypotheses on speed ranges and speed uncertainties. Results show that Traffic Control using Speed Adjustments (TCSA) can solve most of the conflicts even when we consider uncertainty on the TP. However, the number of maneuvers that need to be given to aircraft is highly influenced by the uncertainties used in the TP.

The conflict resolution problem is quite simple as long as the number of aircraft involved is small. Many automation projects disregarded the problem of clusters and failed on real traffic tests because they were unable to deal with complex conflicts. The first definition of cluster appeared in the middle of the nineties when theoretical research started on conflict resolution. The n-aircraft conflict resolution problem is highly combinational and cannot be optimally solved using classical mathematical optimisation techniques. The set of admissible solutions is made of many unconnected subsets enclosing different local optima, but the subset enclosing the optimum cannot be found a priori. Using a priority order to solve a n-aircraft conflict is much easier but the solution is not optimal. However it is difficult to determine the best order or even a good order that ensures that a solution exists. In this paper, a theoretical study of the possible structures of clusters is presented. A simulation using French real traffic data compares the structure of clusters with direct and standard routes. The sensitivity of cluster sizes to uncertainties on trajectories forecast is studied.

International audience; This paper introduces several algorithms which build optimal configurations of Air Traffic Control sectors, taking into account traffic prediction, sector capacities and the number of available control positions.

International audience; Conflict probes will be important components of air traffic control tools in the next years. They appear in almost every project (CINCAT, ERATO, HIPS, PHARE, and so on). To be useful, they have to fulfill two goals : reliability (they have to detect all conflicts) and efficiency (they must minimize the number of false alarms). Conflict probes rely on trajectory prediction, and their reliability and efficiency highly depend on the accuracy of trajectory prediction. In this paper, we present a quick mathematical insight of the influence of ground speed errors on trajectory prediction, results of arithmetic simulations on real traffic both on ground and vertical speed errors, and a statistical analysis of these results to model the influence of vertical and ground speed errors on conflict probe.

International audience; For the last 10 years, airlines have widely supported research on the development of airspaces where aircraft would be free to decide their trajectory: these areas where called Free-flight airspaces. However, as soon as two aircraft are in the same area, their separation must be guaranteed. FACES1 is an autonomous and coordinated embarked (on board) conflict solver for Free Flight airspace. It solves conflict by computing simple manoeuvres that guarantees conflict free trajectories for the next minutes (min). Coordination is ensured by giving sequential manoeuvres to aircraft with a token allocation strategy. FACES can be implemented with the current positioning, broadcasting and flight management technology. Moreover, it is robust to communication or system failure for time up to one or two minutes. FACES was tested with a traffic simulator on busy traffic days over France. Airspace over flight level2 320 was considered as Free Flight.