The introduction of more advanced automation in air traffic control seems inevitable. Air traffic controllers will then take the role of automation supervisors, a role which is generally unsuitable for humans. Gamification, the use of game elements in non-gaming contexts, shows promising results in mitigating the effects of boredom in highly automated domains requiring human supervision. An example is luggage screening, where dangerous items are rarely found, through projecting fictional threats on top of x-ray scans. This paper presents and experimentally tests a proposed implementation of gamification within highly automated en-route air traffic control. Fictional flights were superimposed among automatically controlled real traffic, thus creating fictional conflicts that needed resolving. System supervisors were tasked to supervise the behaviour of a fully automated conflict detection and resolution system, while manually routing fictional flights safely and efficiently through the sector, avoiding conflicts with both real and fictional flights. Automation anomalies were simulated, as well as an automation failure event, after which the system supervisor needed to assume manual control over all traffic. The presence of fictional flights increased self-reported concentration levels and reduced boredom. However, some participants reported that fictional flights were distracting. Thus, while the use of fictional flights increases engagement, it might negatively affect other cognitive functions, and with that, compromise safety. Thus, while the implementation of such a tool might provide benefits in terms of skill retention and engagement, further research is recommended involving professional air traffic controllers, improved measurement tools and a longitudinal study that better excites boredom, complacency, and skill erosion in order to understand and mitigate its negative effects.

Computer algorithms facilitate increased automation in various human-centered work areas to improve operational safety and efficiency. Algorithmic transparency is considered essential for human operators, policy makers and system developers, as it allows them to understand the capabilities and limitations of an algorithm. In this research, we focus on path-planning algorithms and propose a purely visual approach to achieve their transparency. This approach extracts and portrays information directly from the algorithms, aiming to visually reveal their inner workings. Benchmark tests indicate that extracting information from path-planning algorithms may significantly slow them down. For time-constrained operations, it is recommended to store only the necessary data during the pathfinding process and perform information extraction afterwards. Based on theories from cognitive engineering, six transparency levels were designed to chunk meaningful information pertaining path-planning algorithms. A user study among non-experts (N=40) was then conducted to evaluate the impact of visual algorithmic transparency on human understanding. The results suggest that increased transparency levels allow non-experts to more correctly and confidently understand the details of a path-planning algorithm. However, it is also found that certain transparency levels can lead to confusion, especially when the algorithm behaves in a way contrary to human expectations. This study further reveals that, given the same level of transparency, sampling-based algorithms may be easier to comprehend than graph-based algorithms. This research can serve as a reference for how to achieve transparency in path-planning-related applications and how to hierarchically portray and organize transparency information.

Energy management is essential in low-energy flight conditions. Changes in fixed-wing aircraft configuration affect performance and energy boundaries, and improved insight therein should allow pilots to better predict potentially dangerous situations, maintain suitable safety margins, and more effectively react to unforeseen events. This paper presents the design and experimental evaluation of a vertical situation display with enhancements portraying changes in the flight performance envelope. Sixteen pilots were tasked to fly approach and go-around scenarios with both a baseline and an enhanced display, with some of the scenarios including unexpected failures in configuration changes. Results show that the new display makes pilots maintain larger margins in velocity, thus spending less time below the advised minimum speed limit in final approach. However, these larger velocity margins also led to larger errors with respect to target velocities. Failures in configuration changes were more quickly discovered with the new display, although these results could not be substantiated due to a lack of statistical significance. However, pilots did report feeling better able to predict dangerous situations. Overall, pilots preferred the novel display; no significant differences in workload were found.

Air Traffic Controllers (ATCOs) ensure safe and efficient operations by scanning radar displays to identify flights needing clearances. They then compare flight parameters to assess the impact of potential actions on sector safety. With global air traffic expected to rise, comparing flight labels will become more time-consuming, increasing workload and response delays. To ease this cognitive burden, a flight filtering mechanism is introduced, focusing on flights with spatio-temporal proximities to a selected flight of interest. Based on data from a previous study involving five professional controllers, filter parameters and their thresholds have been selected and tuned. Results indicate that filtering by consolidated state- and intent-based interaction parameters yield the best match to controllers’ judgements about relevant flights relative to a flight of interest. It is anticipated that the filter, outputting a list of relevant flights, can serve as an operational support tool by fading non-relevant flights, reducing cognitive effort in visual searches, and could aid Flight-Centric Air Traffic Control (ATC) allocation models that are based on predicting flight-centric complexity.

As concepts for incorporating uncrewed aerial vehicles (UAS) into controlled airspace are being developed, the need for automated UAS traffic management (UTM) systems to guide UAS and maintain safety is becoming more apparent. A major point of concern for the implementation of UTM is how such systems could coexist alongside the human-centric air traffic management system that is already in place. The European Union’s U-space concept proposes the use of dynamic segregation of airspace reserved for UAS within the control zone. We conducted a simulation experiment with ten air traffic control officer (ATCO) volunteers to gather insights into the feasibility of tower controllers performing the dynamic segregation task. An interface prototype that supports dynamic geofencing and low-level UAS control was developed for this purpose. We found that our proposed interface design helped ATCOs detect potential conflicts between UAS and crewed aircraft. However, they were not always able to adequately resolve them, which resulted in several loss of separation events. It appears that the limitations of the dynamic segregation concept do not fit well with typical air traffic control strategies used by ATCOs. To substantiate our findings, we propose future research to investigate how to overcome the limitations of dynamic segregation to resolve tactical conflicts by revising ATCO control strategies, reevaluating their role in dynamic segregation, as well as considering the definition of flight rules and separation minima for UAS.

It is widely recognized that airspace capacity must increase over the coming years. It is also commonly accepted that meeting this challenge while balancing concerns around safety, efficiency, and workforce issues will drive greater reliance on automation. However, if automation is not properly developed and deployed, it represents something of a double-edged sword, and has been linked to several human–machine system performance issues. In this article, we argue that human–automation function and task allocation may not be the way forward, as it invokes serialized interactions that ultimately push the human into a problematic supervisory role. In contrast, we propose a flight-based allocation strategy in which a human controller and digital colleague each have full control authority over different flights in the airspace, thereby creating a parallel system. In an exploratory human-in-the-loop simulation exercise involving six operational en route controllers, it was found that the proposed system was considered acceptable after the users gained experience with it during simulation trials. However, almost all controllers did not follow the initial flight allocations, suggesting that allocation schemes need to remain flexible and/or be based on criteria capturing interactions between flights. In addition, the limited capability of and feedback from the automation contributed to this result. To advance this concept, future work should focus on substantiating flight-centric complexity in driving flight allocation schemes, increasing automation capabilities, and facilitating common ground between humans and automation.

Automation errors may result in human performance issues that are often difficult to grasp. Skraaning and Jamieson (2023) proposed a taxonomy for classifying automation errors into categories based on the visible symptoms of design problems, so as to benefit the design of training scenarios. In this paper, we propose a complementary classification that is based on the mechanisms of human-automation interaction guided by Rasmussen’s Skill, Rule and Knowledge (SRK) taxonomy. We identified four main failure classes and expect that this classification can support automation designers.

Any-angle path planning is an extension of traditional path-planning algorithms that aims to generate smoother and shorter paths in graphs by allowing any-angle moves between vertices, rather than being restricted by edges. Many any-angle path-planning algorithms have been proposed, such as Theta*, Block A* and Anya, but most of them are designed only for static environments, which is not applicable when dynamic obstacles are present. Time-Optimal Any-Angle Safe-Interval Path Planning (TO-AA-SIPP) was developed to fill this gap, which can find an optimal collision-free any-angle path that minimizes the traversal time. However, as indicated by its authors, TO-AA-SIPP may not be efficient enough to be used in multi-agent pathfinding (MAPF). Therefore, this paper presents a new algorithm Zeta*-SIPP to improve TO-AA-SIPP by means of 1) reducing useless search nodes that have no contribution to finding optimal solutions, and 2) introducing Field of View (FoV) instead of Line of Sight (LoS) to speed up visibility checks with static obstacles. Benchmark experiments showed that Zeta*-SIPP reduced the computation time of TO-AA-SIPP by around 70%-90% on average.

To facilitate a smooth integration of drones into the current Air Traffic Management (ATM) system, Unmanned Air Traffic Management (UTM) systems, services and protocols are currently under development. Unlike current ATM, UTM will rely on high levels of automation. This is potentially problematic, because 100% safe and reliable automation under all circumstances cannot be guaranteed. UTM therefore warrants human supervision and interaction, especially at small airports near urban areas where drone traffic may cross the arrival and departure routes of manned air traffic. Supervision, however, requires some form of transparency for humans to understand the limitations and the behavior of an automated system (e.g., what is it currently doing, what it is planning to do next, and why?). Previous research underlined the importance of UTM transparency, but also indicated that it remains unclear what type of human operator will eventually supervise the UTM system. The background, training and expertise of a human operator may impact the transparency needs and what information needs to be communicated and when. In this paper, the results of a questionnaire-based user study are presented in which information needs were collected from twelve operational Air Traffic Controllers and twelve drone operators and engineers. Results indicate that transparency is deemed imperative for UTM and that information elements categorized as ‘operational transparency’ are typically preferable over ‘engineering transparency’ elements, regardless of operator group. Surprisingly, we found no significant difference in transparency needs between controllers and drone operators, suggesting that a ‘one-size-fits-all’ transparency solution for UTM would be possible.

To alleviate the workload of air traffic controllers, part of the air traffic may be handled by a future automated system. When deciding which flights to delegate, a distinction can be made between basic and non-basic flights, with the former being prime candidates for delegation. The human controller can then focus on the non-basic flights, where human competencies are most valuable and more difficult to automate. The classification of flights is preferably based on objective measures relating to the traffic situation. Existing complexity models are, however, often used for capacity predictions or airspace restructuring and primarily to assess the complexity of a sector as a whole. In this paper we use empirically collected flight complexity ratings from 15 professional en-route air traffic controllers. They indicated which other flights contributed to their complexity assessment of a single flight of interest. This exploratory study was able to build a machine-learning model which adequately classifies these flights, based on a qualified majority of controllers. By analyzing the interactions between the included flights, we discuss whether a classification model can differentiate between basic and non-basic flights, and which traffic features play the largest role. Once this can be done reliably and an appropriate complexity threshold has been chosen, a model can be developed as a starting point for an automatic allocation algorithm that distributes flights between a human controller and the computer.

In academic air traffic control research, traffic scenarios are often repeated to increase the sample size and enable paired-sample comparisons, e.g., between different display variants. This comes with the risk that participants recognize scenarios and consequently recall the desired response. In this paper we provide an overview of mitigation techniques found in literature and conclude that rotating scenario geometries is most frequently used. The potential impact of these transformations on participant behavior, as described in this paper, is however not sufficiently addressed in most studies. As an example we, therefore, analyze previously collected eye tracking data from ten professional air traffic controllers, each presented with three repetitions in various rotations of several distinct scenarios. Results imply that researchers wishing to repeat scenarios should more carefully consider whether mitigation techniques might have an impact on their results.

Expert participants may not always be available for evaluation of new displays or support systems, and in some cases, it might be better to use novice participants, particularly when the display or support significantly changes existing work practices. To provide tools and arguments for selecting the expertise level of participants, we propose the use of Rasmussen’s decision ladder to analyze where and how a new visualization or a support tool changes the task, and identify steps where a novice participant may learn to perform the task to an acceptable level. A comparison to the support with the current operational interfaces then shows where an expert might have difficulty in stepping away from learned practice. This analysis is applied to the domain of air traffic control, and a selected set of relevant past research with both expert and novice participants is reviewed, revisiting the decision for a participant level in the study.

The SESAR-funded Modern ATM via Human / Automation Learning Optimisation (MAHALO) project recently completed two years of technical work exploring the human performance impacts of AI and Machine Learning (ML), as applied to enroute ATC conflict detection and resolution (CD&R). It first developed a hybrid ML CD&R capability, along with a realtime simulation platform and experimental User Interface. After a series of development trials, the project culminated in a pair of field studies (i.e., human-in-the-loop trials) across two EU countries, with a total of 35 operational air traffic controllers. In each of these two field studies, controller behaviour was first captured in a pre-test phase, and used to train the ML system. Subsequent main experiment trials then experimentally manipulated within controllers both Conformance (as either a personalised-, group average-, or optimized model) and Transparency (as ether a baseline vector depiction, an enhanced graphical diagram, or a diagram-plus-text presentation). The proposed paper presents guidelines on the design and implementation of ML systems in Air Traffic Control, derived from the results and lesson learned from the Simulations, as well as the qualitative feedback received from the controllers themselves.

The Continuous Descent Approach (CDA) offers reduced aircraft noise emissions and fuel consumption, but the obstacle limiting it to reduced traffic density conditions, is the low predictability of the trajectory and Estimated Time of Arrival (ETA). The solution proposed by this research is to develop a pilot support interface to facilitate the execution of a fixed flight path angle CDA, where thrust is not limited to idle and a velocity profile can be tracked, which will lead to a selected ETA. Initially, the CDA trajectory was investigated through an aerodynamic model, while the solution space of the ETA and the calculation of the stepwise velocity profile were defined. Following the analysis of the pilot’s role, a two-fold support interface was designed based on Ecological Interface Design (EID), with a Vertical Situation Display (VSD) playing a central role for planning and execution. The interface was tested in a MATLAB®/Simulink® setup and five pilots were recruited to execute simulations over different wind conditions. Their on-time performance was satisfactory, while they worked with the provided cues and suggested some interface changes to offer more flexibility. A more robust application of the proposed approach can lead to a wider adoption of the CDA, as a validated procedure.

Haptic cues on the side stick are a promising method to reduce loss of control in-flight incidents. They can be intuitively interpreted and provide immediate support, leading to a shared control system. However, haptic interfaces are limited in providing information, and the reason for cues may not always be clear to pilots. This study presents the results of the conceptual development of visual display symbology that supports haptic feedback on the side stick in communicating flight envelope boundaries to pilots. Novel indications for the limits of airspeed, load factor, angle of attack, and angle of bank, which for the first time simultaneously indicate magnitude and direction of the haptic cues, were integrated in an Airbus primary flight display. The symbology was tested in a pilot-in-the-loop experiment with professional Airbus pilots (N=16) flying several approaches in alternate law with haptic feedback. Objective results do not show clear improvements, although the time spent outside the flight envelope is slightly reduced. Subjective results indicate a preference, however, for the new display and an increased understanding of the haptic feedback. Further research is recommended to improve the interface design, remove unused indications, and test a bank scenario using current operational bank limits.

To meet increasing safety and performance demands in air traffic control (ATC), more advanced automated systems will be introduced to assist human air traffic controllers. Some even foresee complete automation, with the human as a supervisor only to step-in when automation fails. Literature and empirical evidence suggest that supervising highly-automated systems can cause severe vigilance and complacency problems, out-of-the-loop situation awareness and transient workload peaks. These impair the ability for humans to successfully take over control. In this study, situation awareness prompts were used as a way to keep controllers cognitively engaged during their supervision of a fully automated ATC system. Results from an exploratory human-in-the-loop experiment, in which eight participants were instructed to monitor a fully automated ATC system in a simplified ATC context, show a significant decrease in workload peaks following an automation failure after being exposed to high-level SA questions. Although the selected method did not necessarily yield improved safety and manual control efficiency, results suggest that using situation awareness feedback in line with controllers' attention could be an avenue worth exploring further as a training tool.

In the quest for more efficient air traffic management, a common approach is to allocate an increasing amount of functionality to higher levels of automation, with a supervisory role for humans. This potentially leads to forthcoming issues such as skill degradation and out-of-the-loop phenomenon. If the traffic in an airspace is instead shared between a human operator and an automated system, with specific flights fully delegated to automation, operators can maintain their skills and stay actively involved in controlling the rest of the traffic. This does, however, lead to new forms of mixed conflicts, where two flights are controlled by different agents. A smart flight allocation strategy, starting with the delegation of basic flights requiring little monitoring or cognitive effort, is expected to improve combined human-automation performance. In this paper, we present flowcharts to model en-route air traffic controller cognitive think and action processes in two core tasks: conflict detection and resolution. We qualitatively describe the impact of delegating flights to automation and the associated introduction of mixed conflicts. Once empirically validated and quantified in follow-up research, these models can be used to design flight allocation strategies for future human-automation teams.

This paper investigates the effects of different automation design philosophies for a helicopter navigation task. A baseline navigation display is compared with two more advanced systems: an advisory display, which provides a discrete trajectory suggestion; and a constraint-based display, which provides information about the set of possible trajectory solutions. The results of a human-in-the-loop experiment with eight pilot participants show a significant negative impact of the advisory display on pilot trajectory decision-making: out of the 16 encountered off-nominal situations across the experiment, only 6 were solved optimally. The baseline and constraint-based display both lead to better decisions, with 14 out of 16 being optimal. However, pilots still preferred the advisory display, in particular in off-nominal situations. These results highlight that even when a support system is preferred by pilots, it can have strong inadvertent negative effects on their decision-making.

Air traffic controller workload is a limiting factor in the current air traffic management system. Adaptive support systems have the potential to balance controller workload and gain acceptance as they provide support during times of need. Challenges in the design of adaptive support systems are to decide when and how to trigger support. The goal of this study is to gain empirical insights into these challenges through a human-in-the-loop experiment, featuring a simplified air traffic control environment in which a novel triggering mechanism uses the quality of the controller's decisions to determine when support is needed. The designed system seeks to prevent high workload conditions by providing resolution advisories when the controller exceeds a threshold of 'self-complicating' decisions. Results indicate that the new system is indeed capable of increasing the efficiency and safety compared to full manual control without intervention. More adaptive support, however, increased the frustration of participants, decreased acceptance, and did not result in improved workload ratings. These findings suggest that, unless we can better infer human intent in complex work environments, adaptive support at the level of decision-making is problematic. A potentially more fruitful direction is to provide support at the level of information integration, with full decision-making authority with the human.