The identification of time-varying human operator (HO) dynamics is critical for advancing adaptive support systems in manual control tasks. This study evaluates the performance of Adaptive Model Selection (AMS), a framework extending recursive ARX identification methods, for estimating time-varying HO parameters, particularly effective time delay (τ(t)). Two configurations of AMS were tested: AMS_q, employing the z-domain ARX representation, and AMS_δ, utilizing the delta-domain ARX representation.

A Monte Carlo simulation framework was used to simulate a compensatory manual control task under varying conditions, including remnant noise (P_n) and dynamic transitions in system parameters. Stability and convergence rates of delay estimates were analyzed for different window sizes (W_s). Results show that the correlation between W_s and convergence time was linear and remained unaffected by remnant noise, demonstrating that window size is the primary determinant of responsiveness. Larger Ws improved stability but introduced tracking delays, whereas smaller W_s allowed for faster adaptation to dynamic changes at the cost of increased sensitivity to noise.

The comparative analysis between the configurations revealed a strong dependence of delay estimation accuracy on the precision of the natural frequency estimate of the neuromuscular system (NMS). The natural frequency estimation directly influences HO dynamic response modeling, and inaccuracies in this parameter propagate through the recursive identification process, affecting the reliability of delay estimates.

These findings underscore the critical role of window size and natural frequency estimation in determining the accuracy and stability of effective time delay estimation through AMS. This study provides a foundation for refining AMS to better balance stability and responsiveness in estimating time-varying HO dynamics. Such advancements can facilitate more accurate modeling of time-varying HO behavior, deepen understanding of HO adaptation, and contribute to the advancement of adaptive support systems.

Recurrent training is crucial to helping pilots maintain or upgrade their flying skills. However, constantly monitoring and scrutinising pilots during training can lead to performance pressure. If pilots who are under pressure feel that their behaviour is affected it may ultimately reduce the effectiveness of recurrent training. To study the effects of performance pressure on pilot behaviour and learning, pilots (n=17) holding an airline transport pilot licence and type rating for large multi engine aircraft were invited to participate in a simulator experiment where they would learn a new autopilot system. The low-pressure group flew a series of training scenarios under no performance pressure whereas the high-pressure group flew the same training scenarios with prompts designed to induce performance pressure and raise their anxiety. It was hypothesised that the high-pressure group would be less incentivised to explore different functions of the autopilot due to the performance pressure, therefore leading to a lower quality of training which would be reflected in poorer knowledge of the autopilot system. Ultimately, whilst the high-pressure group did in fact undergo training with a significantly higher state anxiety. This was found to have no significant effect on their behaviour during training or their performance in a test scenario. The findings also suggest that the experiment would benefit from being repeated with more guidance given during training and more constrained scenarios to reduce the variation in the data.

The human operator's manual control behaviour under time-invariant conditions has been successfully modelled. However, significant gaps remain in understanding and modelling adaptive manual control behaviour under time-varying conditions. One such less-understood aspect is the pilot's ability to detect changes in time-varying controlled element dynamics. This study aims to develop a mathematical model that investigates open-loop stability as a criterion in compensatory and pursuit tracking tasks to model the pilot's detection of change in controlled element dynamics across different transition rates, particularly for transitions from stable to less-stable vehicle dynamics. The model operates under the assumption that trained human operators track statistical properties of tracking task signals within periods of compromised open-loop stability to trigger their detection of change in dynamics. The model identifies regions of reduced stability and simulates the tracking task signals through time-varying computer simulations. Subsequently, human-in-the-loop experiments are conducted to validate the model. The validated model demonstrates a combined accuracy of $88.54\%$ for the compensatory task and $80.62\%$ for the pursuit task. Notably, in the experiments, the error signal consistently outperforms the error rate signal across all transition rates, diverging from the results of the computer simulations. Overall, the proposed model's ability to successfully predict pilot detection across a spectrum of transition rates marks an advancement towards developing more human-like automation.

The introduction of Trajectory Based Operations (TBO) is set to change the operation on all levels of Air Traffic Control (ATC), including aerodrome control. Here, adherence to a planned four-dimensional trajectory is important to achieve a stable operation. At the same time, this concept provides possibilities for support tools to be used in ATC, by making use of new data and information. This research presents the Take-off Timing Support Tool (TTST) for aerodrome control, and more specifically runway control, at Amsterdam Airport Schiphol. It is incorporated into the already existing Electronic Flight Strip System and features a time axis on which flight strips can be placed. By dragging departures along the time axis a planning and sequence can be constructed, while taking into account the provided solution space to prevent conflicts. An experiment conducted with the TTST showed that professional air traffic controllers used the information from the tool in their decision-making, leading to a decrease in conflict count, and it was demonstrated that a stable planning could be established. Therefore, the TTST is a suitable platform to support TBO in aerodrome control and assists air traffic controllers with time-based control. No significant difference in departure interval was present. The workload did increase moderately.

Haptic shared controllers (HSCs) are a promising solution to prevent human over-reliance on automation during tasks such as car driving. However, research has shown that if the HSC is tuned incorrectly, then there is a risk of haptic conflicts between the human and HSC. To address this challenge, this paper presents the design and implementation of a novel adaptive HSC that continuously adjusts its look-ahead time. By estimating the time shift between the reference state of the HSC and the actual state, the HSC adapts to the look-ahead time of the human it is interacting with. Results from a human-in- the-loop experiment show that the novel HSC achieves similar subjective ratings as a fixed preview HSC, as well as a significant improvement over a fixed pursuit HSC. Going from pursuit to preview, objective experiment data shows that as the adaptive HSC adjusts its look-ahead time, haptic conflicts are reduced and tracking performance is increased. The presented findings are a step forward in designing haptic support systems with high chances of user acceptance. The proposed adaptive look-ahead algorithm provides a new method for online estimation of human look-ahead time, with or without a HSC in-the-loop.

Roll reversal errors, where the pilot tries to steer the aircraft back to wings-level but unintentionally increases the bank angle instead, have contributed to several accidents. Previous studies have shown that these errors can be caused by misinterpreting the attitude indicator (AI), with the figure-ground relations cited as contributing to this misinterpretation. A modified AI was developed, which uses several static monocular depth cues (color gradient, linear perspective lines, and shadow-light relationship) to strengthen the figure-ground relationship.
The modified version of the AI was compared to a baseline AI in a two-part flight simulator experiment where pilot reaction time and error rate, severity, and duration were measured. The first part induced the leans illusion making use of physiological adaptation to roll angle, distraction, and surprise. The second part simulated the leans illusion by simply rolling the simulator to the left or right. A group of 25 experienced commercial airline pilots performed a roll-to-level task in a moving-base simulator, which also provided spatially disorienting motion cues, using both the baseline and modified versions of the AI. While the modified
display had a lower error rate in the motion-opposite scenario when using the novel method (4.91% compared to 6.07%), no significant difference was found between the error rate of the two displays. The only significant difference was found in the reaction time, where the modified AI caused an increase in reaction time. The error rates and reaction times of the first part of the experiment did not match previous research. The novel disorientation method seemed to work best in a surprise scenario. While no significant differences were found between the modified AI and the baseline AI, it is still recommended to continue testing the modified AI with a new experiment setup, especially analyzing its effect in more extreme attitudes.

An important bottleneck in managing inbound traffic capacity is the limited size of the Terminal Manoeuvring Area, which should not become crowded by aircraft arriving from the Control Area. This is done by arrival metering, a process entailing sending aircraft through the Initial Approach Fix (IAF) at pre-established Expected Approach Times (EAT). Currently, area controllers are required to deliver aircraft at the IAF at their EAT ± 120 seconds. Reducing this window in the near future would allow for less buffer times between aircraft to increase capacity, potentially allowing implementation of time-based separation at the IAF as an initial step towards trajectory based operations. This research aims to produce a visual decision support tool to aid the aircraft sequencing process and achieving EAT adherence at the IAF, that is compatible with the currently in use or upcoming systems used in the Amsterdam Area Control Center. To avoid significant changes to the modus operandi of controllers, the aim is to show the user the operational constraints and focus on expediting traffic. An experiment was conducted with eight professional controllers to compare the newly developed tool with a rendition of the current interface. The results are positive, indicating less deviation from the EATs in all participants and overall lower participant workload as a result of using the proposed decision support tool.

Research into analysing the feasibility of using VR headsets to train helicopter pilots is in its infancy. While enough is known about what effects VR headsets have on pilots, a research gap exists in the field of explaining what exactly causes these effects to occur. This thesis aims to develop a visual model that can be used to analytically predict how pilot behaviour will be affected by a changing Field-of-View and explain why these changes occur. The scope of this model is analysing where in the pilot’s vision, the point of most feedback lies, and what happens when this point gets clipped out of view while the pilot controls a two degree-of-freedom helicopter model. Subsequently, human-in-the-loop experiments were carried out where the participants had to do velocity disturbance rejection tasks and pitch disturbance rejection tasks for various fields-of-view, to try and validate the model. It was predicted that the performance for the pitch disturbance rejection task would not be affected by the field-of-view but the performance for the velocity disturbance rejection task would degrade as field-of-view was lowered. Overall, there was no significant trend that emerged from the experiments for either task, but significant insights were gained into the scope of the visual model. Based on these, and other findings, the model did a good job in identifying why performance changes and in setting up future researchers for success in this field.

The aviation industry's reliance on automation raises concerns about pilot complacency, necessitating continuous pilot proficiency measures. To that end, real-time pilot skill feedback is vital—through alerts on declining skill levels or scalable levels of autonomy. Current cybernetic methods are limited as they assume linearity and time-invariance of human behavior and lack real-time capability. Neural Networks (NNs) offer a solution but face challenges such as high computational costs and limited generalization capability. To overcome these issues, this paper introduces a new and compact Residual Network for eXplainable Convolutional MTS Classification (RN-XCM) designed explicitly to classify pilot skill levels. Results demonstrate RN-XCM's ability to accurately classify skill levels based on 1.2 seconds of dual-axis control data, achieving a test accuracy of up to 93.50%, while requiring 50% less training time than competing NN models. It also achieves a test accuracy of 80.16% for previously unseen subjects, signifying its competence as a one-size-fits-all classifier. Notably, RN-XCM performs 17.88% better when classifying dual-axis tracking tasks over single-axis tracking tasks. Overall, the possibility of real-time feedback provided by the RN-XCM can enable quantitative evaluation of pilot control behavior, therefore enhancing safety and facilitating smoother interactions between pilots and aircraft.

Autonomous vehicles are becoming increasingly prevalent in society, but the transition from active driver to passive passenger is known to increase the risk and severity of motion sickness. Motion anticipation is a critical factor in this difference, and visual information is known to be a major contributor to motion anticipation. However, the underlying mechanisms of how visual information influences motion sickness remain largely unknown. This study aims to investigate the effect of visual translation on motion sickness development. To accomplish this, a proposal was made to extend an existing motion sickness prediction model with visual translational components. A human-out-of-the-loop experiment was conducted in a moving-base simulator to test the proposed model's reliability and determine whether visual translation influences motion sickness. The experiment simulated an urban ride that featured repetitive accelerating and breaking maneuvers on a straight road. Eighteen participants were subjected to three different visual conditions that varied in the amount of global optical flow, while the simulator's motion was identical. The results suggest that congruent visual translation may lead to a minor, non-significant reduction of motion sickness, while the amount of optical flow does not seem to affect it. The proposed motion sickness model was able to predict a slight decrease in motion sickness for conditions with visual optical flow, as demonstrated by the experiment. Nonetheless, it is still debatable whether congruent visual translation may influence the onset and severity of motion sickness, as the results of this study could not confirm this.

The joint management of airspace by human controllers and automated agents is gaining prevalence in nextgeneration Air Traffic Control (ATC). In such settings, human controllers are challenged with maintaining situation awareness while dealing with intricate and often opaque automated technologies. This study probed the potential of a concept interface, which augments traditional radar displays, to facilitate human controller-automation teamwork. With visual explanations and interactive elements, it intended to make the control behavior of automated agents transparent and comprehensible to human operators. The primary objective was to explore whether features from a non-conventional design could offer insights for designing interfaces for highly automated ATC operations. The concept was tested in a prototype-based user experience evaluation with static traffic scenarios, involving 15 active air traffic controllers from the Maastricht Upper Area Control Center. Results indicated that the visual explanations of the interface enabled controllers to comprehend the plans and reasoning underlying automation’s behavior. A significant correlation between controllers’ evaluations and their attitudes toward novel technologies underlines the influence of pre-existing beliefs about automation on the acceptance of new interface tools. Future research is warranted to streamline parts of the design and further investigate the interface’s capacity for explaining automation’s actions in dynamic, real-time scenarios.

This study investigated the effect of manual and autopilot control on hazard and failure detection in aviation, in cases where the autopilot can masks or diminishes cues of aircraft movement. This mechanism could result in loss of situation awareness, upsets and ultimately accidents. Twenty airline pilots participated in an experiment in which two scenarios, an engine failure and icing accumulation hazard, were flown in both manual and autopilot control conditions. Results show no significant difference in detection time for the engine failure scenario between the two modes, but a marginally significant difference in the icing accumulation scenario and a clear difference in the way pilots detected the failure/hazard. Manual control may provide clearer cues, but automation may lower workload and allow for better monitoring. Monitoring of the flight controls was found to be an important factor in hazard detection, with pilots who had their hands resting on the controls having significantly lower detection times. The study provides insights on the importance of active monitoring of flight controls and on the impact of non-backdriven controls. The findings suggest that understanding the advantages and disadvantages of autopilot use is crucial for training pilots effectively.

This study explored several anticipatory interfaces as motion sickness mitigation tool in autonomously driving vehicles. The interfaces were designed to lower the mismatch between the expected motion and the sensory input in a moving body, by providing anticipatory information on upcoming vehicle motion. The two proposed interfaces were a Light only interface and a Combined interface featuring both light and sound. The interfaces present information on the direction and magnitude proportional to the upcoming vehicle accelerations, 0.9s before the actual event. The interfaces were tested in separate trials and compared to a Baseline trial. The comparison was done by a mixed-order, within-participants experiment (n=18), performed in the SIMONA Research Simulator, with identical 30-minute driving conditions. To test the interfaces’ effectiveness in the most motion sickness-triggering setting, subjects got a reading task on a handheld tablet. The results were quantified on an 11-point motion sickness scale and subjective survey questions. Improvements in motion sickness and subjective experience were found with both interfaces. However, only the Combined interface trial showed significant improvements compare to the Baseline. Neither of the interfaces was considered intrusive, while only the Combined interface was considered comfortable. The survey also clearly favored the Combined interface in scores on timing cor- rectness, intuitiveness, and subjective cue correctness. Although the Combined interface had the best results, strong individual preferences were reported for both interfaces, a hypothetical sound interface, or no interface. Further research is advised to focus on the Combined interface while considering optional light and/or sound disengagement in line with personal preference.

The Dutch Air Traffic Control has implemented fixed approach trajectories within approach control during night operations when traffic density is low. During the day, when traffic density is higher, vector-based operations are used. The Dutch Air Traffic Control aims to implement fixed approach trajectories when traffic density is high. This shift from the current vector-based operations will allow aircraft to perform a continuous descent and fly around inhabited areas, reducing the emissions and noise. To improve the capacity of the landing runway in strong headwind conditions, time-based separation is envisioned. Separating aircraft on time will result in a constant runway capacity in all wind conditions. However, to make these changes possible, additional decision support tools are required to assist controllers and reduce their workload. This paper discusses the use of a new toolset, centered around a time-space diagram which shows the expected arrival time and distance-to-go to a selected reference waypoint. Results of an experiment with eight professional air traffic controllers show that the added tools allowed future conflicts to be solved sooner and with fewer instructions. Aircraft followed the fixed approach trajectories better in the latter stages of the approach when compared to current vector-based operations. Although a slight increase in runway capacity was observed with the new toolset, it was not statistically significant. The workload of the controllers did not show a difference when using the new toolset. In conclusion, the added tools enabled the controllers to combine fixed approach trajectories with time-based separation. However, to reduce perceived workload, future designs should aim to integrate the time-space diagram on the main radar screen such that controllers do not need to switch attention between screens.

The identification of time-varying, adaptive behaviour of a human operator in basic manual control tasks is undoubtedly under development since most methodologies only account for time-invariant systems. Previous authors have proved that estimation techniques based on ARX structures can generally identify the HO model parameters. Nonetheless, ARX methods present several problems, such as the persistent bias in estimates that may increase due to coupled noise and system models. Therefore, a novel identification technique based on Box Jenkins models is proposed to achieve a more adequate match between the estimator structure and the HO model. The identification process can be conducted offline by the Ordinary Least Squares and Prediction Error Method, or online, when Recursive Least Squares and Recursive PEM are employed, respectively, in ARX and BJ models. The BJ estimator has excellent potential as an identification tool due to its bias reduction capabilities, as clearly shown in batch-fitting, although non-linear optimization processes decrease its convergence speed by 500%. An RPEM algorithm with forgetting factor 𝝀 = 0.99609 and first-order remnant BJ structure is implemented and tested under Monte Carlo simulation and experimental data. Recursive BJ algorithms could help to achieve the ideal identification method by diminishing the Neuro-Muscular parameter bias in ARX.

Spatial disorientation (SD) is one of the main causes of incidents and accidents in aviation. While most studies have investigated the effect of SD on the control task, we tested the effect of SD on cognitive performance in an operationally representative environment.
Thirteen Dutch military helicopter pilots flew scenarios with six different SD events using an AH-64 Apache flight model in virtual reality in a 6-DoF motion simulator. The SD events used were: “False Horizon”, “Featureless Terrain”, “the Leans”, “Brownout”, “Somatogyral Illusion” and “Night Vision Goggles (NVGs)”. Corresponding scenarios without the SD events were performed to obtain baseline measures of cognitive performance. When performing the scenarios, participants had either the role of pilot flying or pilot monitoring.
To test the cognitive performance, participants performed a mathematical processing task. The corrected reaction time and error rate were significantly higher during the SD events than during the baseline events.
These effects were most prominent in the “Featureless Terrain” and “the Leans” scenarios. The results indicate that SD has a negative impact on the cognitive performance of military helicopter pilots. These findings underline the importance of SD awareness training for pilots, as well as the use of workload management procedures when experiencing SD.

Technological devices are ubiquitous, think of for example smartphones and in-vehicle information systems. Both can contribute towards distracted driving where the visual field of the human controller is shifted away from the primary control task. In this paper a neural network model is trained using the InceptionTime architecture and used to detect distractions in pursuit and preview tracking tasks. For this purpose an experiment has been designed to collect data in which participants are distracted using a visual distraction called the Surrogate Reference Task (SuRT). It was found that distractions are easier to detect in tracking tasks with pursuit displays instead of preview displays. This is because in preview displays the future target trajectory is shown to the human controller, resulting in a lower tracking error compared to pursuit displays. Apart from the tracking error, the InceptionTime neural network was also trained using the time-series data of the control input and system output. Important characteristic of distracted data found were a reduced control input and higher tracking errors, which may have helped in detecting distractions. The classification models were able to predict data samples correctly with an accuracy of 80.78% and 61.66% in pursuit and preview tracking tasks with distractions, respectively. Lastly, individualised models showed better performance when compared to 'one-size-fits-all models'. Results show clear opportunities for applying neural network models in real-time to detect distractions for increasing safety in human operated machines.

Today, most fatal accidents in commercial aviation are caused by loss of control in flight. Due to the increased reliance on automation and pilots being moved out of the loop, startling and surprising events can be contributing factors. To more effectively train pilots, startle and surprise are to be included in training scenario’s. A subjective scale indicating the level of startle and surprise is being developed to help build these training programs, but an objective baseline of whether pilots are startled or surprised is needed in order to validate and further develop this scale. In this way, the scale can be used in flight simulators to asses the pilot’s perceived level of startle and surprise during novel training scenario’s. This work aims to test a startle and surprise detection method using physiological data. Electromyography, electrocardiography and electroencephalography data were collected. A validation of the occurrence of physiological effects related to both startle and surprise was performed, after which a detection algorithm was constructed. Validation of effects was performed on data of 22 participants using a three-stimulus oddball task with additional auditory startling stimuli. It was found that all considered physiological effects related to startle can be reliably observed. Contrary, not all physiological effects related to surprise were observed. The detection algorithm was tuned on data of twelve participants and showed to generalise well to the other ten data points. Startle detection could be performed with high accuracy, although surprise detection was poor.