Haptic shared control systems that support drivers by means of added torques on the steering wheel are often tuned heuristically. To allow for more systematic design, this paper focuses on the Four Design Choices Architecture (FDCA) and systematically analyzes its tuning with an offline simulation model for the driver’s control behavior and neuromuscular system. These analyses indicated that within the FDCA architecture the Level of Haptic Support (LoHS), which is a feedforward channel supporting negotiation of upcoming curves, is a main contributor to joint system performance. In a driving simulator experiment, the adaptation to and acceptance of different LoHS levels was investigated. Driver acceptance was found to increase with increasing LoHS values up to 1. Objective metrics, including torque conflict (70% reduction), steering effort (81% reduction), steering wheel reversal rate, and lateral deviation all improved, indicating that with the FDCA a high LoHS is both acceptable and, in fact, preferred.

Haptic Shared Control (HSC) systems offer a means to support human drivers in the transition to fully-automated driving. Matching HSC systems settings with drivers’ time-varying neuromuscular system (NMS) dynamics requires real-time HSC adaptations. This paper presents an experimental validation of a previously proposed method for predicting drivers’ time-varying neuromuscular admittance using an ‘average’ grip force scheduled linear parameter varying (LPV) model. The quality of LPV model predictions is compared to that of Recursive Least Squares (RLS) fits of an admittance model on the same data. Ten participants performed steering wheel manipulation tasks with steering wheel perturbations that needed to be kept within a certain displacement boundary by adapting their grip force. Time-invariant (TI) and time-varying (TV) boundary levels were used to, respectively, construct and validate the LPV model. Results show that the average relation between admittance and grip force that underlies the current LPV method varies too much between TV and TI tasks, hampering accurate admittance predictions. Compared to the quality-of-fit of 80-90% obtained with RLS on the TV data, the LPV model’s predictions are insufficiently accurate and do not exceed 55% on average. An approach that enables individual instead of average LPV models to be constructed directly from TV experiment data needs to be pursued for HSC implementations.

A pursuit-tracking manual control model is introduced that includes an observer-like internal model to predict human detection of a change in controlled element dynamics. The internal model’s innovation signal, the difference between the observed and expected system response, is studied for its capacity to drive the detection of a change. The model’s performance is tested for different crossover frequencies, remnant power ratios, observer gains, and detection threshold settings, through Monte Carlo analysis of simulated pursuit-tracking tasks where the controlled element transitions from single to double integrator dynamics. The model shows highly accurate detection performance for a wide range in the observer gain, with a true positive rate of approximately 1 and a false positive rate of approximately 0.02. The high true and low false positive rates, combined with average detection times that match experimental human-in-the-loop data, show the observer model’s potential for accurately predicting human detection of a change in controlled element dynamics.

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.

The identification of time-varying, adaptive behavior of a human operator in basic manual control tasks is currently still a focus area, since most methodologies only account for time-invariant system dynamics. Previous authors have proven that estimation techniques based on ARX model structures can be used to identify time-varying HO model parameters. However, ARX methods do present several problems, such as a persistent bias in the obtained estimates of the HO model poles (neuromuscular parameters) that increases due to coupled noise and system models. Therefore, in this paper a novel identification technique based on Box-Jenkins (BJ) models is proposed, to achieve a better match between the BJ estimator's inherently uncoupled system and noise models and measured HO control dynamics. The identification process was tested offline (batch-fitting) using Ordinary Least Squares and the Prediction Error Method for both ARX and BJ models, respectively, or online when Recursive Least Squares and Recursive PEM are employed. The BJ estimator has excellent potential as an identification tool due to its bias reduction capabilities, as clearly shown in batch-fitting, although the non-linear optimization processes decrease its convergence speed by 500%. An RPEM algorithm with a forgetting factor of λ = 0.99609 and a first-order remnant model incorporated in the BJ structure was tested on Monte Carlo simulation and experimental data. While the recursive BJ estimator showed the same bias-diminishing advantages also seen in batch-fitting, the non-linear RPEM estimator's results showed much slower convergence after HO behavior adaptations and frequent instabilities of the obtained parameter estimates. Hence, further research is needed for implementing a practical bias-free HO model estimator based on the BJ model structure.

Startle and surprise can impair pilot performance and affect flight safety. This study investigates the prevalence of different startle and surprise events among helicopter pilots, its impact on pilot stress and mental effort and the influence of training background. It also looks at currently used startle mitigation strategies and evaluates the usability of a previously proposed “Aviate, Breathe, Check (ABC)” startle management method (Piras et al. 2023). A survey among 234 helicopter pilots revealed that 96% had experienced impactful startle or surprise events during operations. Scenarios such as disorientation, tail rotor incidents, and flight into instrument meteorological conditions (IMC) were considered particularly stressful. Reported levels of stress and mental effort during startle and surprise events did not differ between pilots with higher and lower experience levels or between pilots with a different training background (military or civilian). Only 38% of pilots indicated they were specifically trained to deal with startle and surprise and only 1% were trained to use a breathing technique. Most pilots (90%) expressed openness to implementing the ABC method and expected benefits from using it. Concerns regarding time constraints in critical situations emerged as the primary objection to adopting this technique. Overall, the findings indicate that the introduction of a startle management method tailored for helicopter operations could significantly enhance safety, especially given the higher accident rates compared to fixed-wing operations. Future research should focus on developing effective training protocols that account for the unique challenges of helicopter flying.

On final approach, an approach controller is responsible for separating aircraft lining up on the instrument landing system. In an attempt to increase traffic throughput, especially in strong headwind conditions, European regulation advises all European airports to move from distance-based to time-based separation. This effectively changes the controller’s task from a distance-based to a time-based problem. Further complications arise because of the European recategorization of aircraft types initiative, and experts fear that the gains foreseen with time-based separation will not be realized. This paper presents a visual tool integrated into the radar screen to assist controllers in performing time-based separation, the ideal turn-in point (ITIP) display. To assist controllers in selecting optimal approach strategies, starting from the moment aircraft enter the terminal control area, the display shows the possibilities and restrictions in the system rather than giving (restricting) advisories. A proof-of-concept experiment was performed with people knowledgeable in air traffic control (N = 8) and compared the ITIP to a current industry state-of-the-art display designed by U.K.’s National Air Traffic Services in scenarios of varying difficulty. Results show that with the ITIP tool, efficiency improved with similar or higher levels of safety and similar or lower workload. These promising results justify testing the interface with professional air traffic controllers. Future work aims at reducing clutter, increasing simulation fidelity, and increasing the level of support in complex traffic situations.

This manual provides guidance for human factors researchers and applied psychologists, on the standardized and scientifically rigorous use of the instruments. It is structured to include an overview of the instruments, administration guidelines, and a summary of their psychometric properties to support accurate application and interpretation in research and operational contexts.

While human control behavior is well-understood in continuous control tasks, little is still known about how human operators detect sudden changes in the controlled element dynamics. This paper focuses on modeling this detection phase for pursuit tracking tasks. Potential triggers for the human operator to detect changes in the controlled element dynamics were investigated via a time-varying computer simulation. Based on the results, hypotheses were generated and later tested in a single-axis pursuit tracking experiment with fifteen participants. Transitions from approximate single to approximate double integrator dynamics and vice versa were investigated, for which participants indicated if they detected the transition by pressing a button. Using the button push data, a model for each transition was developed and validated. The models work under the assumption that human operators use a threshold, a multiple of the steady-state standard deviation, on certain signals to detect transitions. The models developed for the transition from single to double integrator dynamics and vice versa are proposed to trigger on the tracking error and system output acceleration, respectively. They have an accuracy of 88.9% and 99.4%, respectively. However, a consistent underestimation of the detection lag remains a limitation of both models. Nonetheless, this research helped confirm the tracking error can be used in a model for the transition from single to double integrator dynamics, proposed a model for the opposite transition, and identified that the relationship between control inputs and the system's response as a crucial factor for the detection.

This paper outlines the three-phase construction of the Startle and Surprise Inventories (Startle-I; Surprise-I) and Visual Analogue Scales for Startle and Surprise (Startle-VAS; Surprise-VAS). In Phase 1, seven experts in the field assessed the content validity of 14 items for surprise, 7 items for startle derived from fundamental and applied literature. Elimination of items was based on a 50% agreement of relevance. In Phase 2, 81 participants completed the retained 19 items nine times, each time immediately after watching a video clip. A multilevel exploratory factor analysis was applied to assess the construct validity of items. In Phase 3, concurrent validity of the Startle-VAS and Surprise-VAS was tested by comparing with the Startle-I and Surprise-I scores, respectively. The first two phases yielded a 11-item two-factor solution, corresponding to the constructs of startle and surprise. These results supported Startle-I and Surprise-I as measures of self-report startle and surprise, with Startle-VAS and Surprise-VAS as efficient alternatives.

We investigated the effect of personality traits and flight experience on pilot cognitive and affective responses across seven startling and surprising scenarios performed in motion-based simulators. A dataset of 89 airline pilots from four studies was used. The personality traits measured were trait anxiety, decision-related action orientation (AOD), and failure-related action orientation (AOF). Pilot self-reported responses in scenarios were standardized by obtaining z scores of startle, surprise, stress, and mental workload. Only trait anxiety was found to be significantly positively correlated with stress. No significant effects of AOD, AOF, or flight hours were found on pilots’ responses. The results indicate trait anxiety may affect pilots’ responses to stressful scenarios, even though pilots are selected based on low trait anxiety.

Providing adequate simulator motion cues for simulated upset and stall scenarios remains challenging. This paper evaluates the potential of novel optimization-based motion cueing algorithms for upset and stall simulation. An offline analysis is performed to compare three Model Predictive Control (MPC) algorithms with varying prediction horizon lengths and prediction strategies (i.e., "Oracle", "Perfect", and "Constant") against two baseline classical washout algorithm implementations from literature. The analysis is performed for a symmetric stall scenario flown with TU Delft's Cessna Citation II laboratory aircraft. Overall, the analysis shows that the objective motion cueing quality expressed in terms of the Root Mean Square Error (RMSE) improves by 29.8% (for specific forces) and 18.7% (for rotational velocities) with the "Oracle" and "Perfect" MPC implementations compared to the reference classical washout results. For the "Constant" MPC algorithm, which in fact does not include any explicit prediction across the MPC algorithm's future prediction window, only a marginal improvement in motion quality was found. Overall, these results imply that, assuming a sufficient future reference motion prediction can be achieved, optimization-based motion cueing algorithms have the potential to provide significantly better motion cueing quality compared to classical motion cueing algorithms.

An online pilot manual control behavior identification method, based on recursive low-order time-series model estimation, is presented and validated using experimental data. Eight participants performed compensatory tracking tasks with time-varying vehicle dynamics, where, at an unpredictable moment during a run, a sudden degradation in dynamics could occur. They were instructed to push a button when they detected a change in dynamics. Two methods to automatically detect the moment when pilot adaptation occurs from online estimated parameter traces are discussed. Results show that pilots are more accurate in detecting changes than either algorithm. But when the algorithms are correct, they are often quicker to detect pilot adaptation than pilots themselves. The presented techniques have potential but need improvements.

Understanding human perception of haptic feedback is critical when designing and regulating these interfaces. In recent years, experiments have been conducted to determine the just-noticeable difference (JND) in mass-spring-damper dynamics, using a hydraulic admittance display in the form of a side-stick. These experiments have resulted in a model of JNDs when interacting with linear second-order dynamics. In real-world applications, however, control force dynamics also commonly include nonlinearities, such as friction. This research extends the current understanding of JNDs in linear systems by including the nonlinear case, where friction is also present. Experiments were conducted to determine JNDs in friction when combined with second-order system dynamics. Results indicate that friction JND can be independent of linear system dynamics as long as its value compared to the linear system's impedance is sufficiently large. As a consequence, friction JND follows Weber's law, also when it is combined with mass-spring-damper dynamics, unless the level of friction approaches the detection threshold, which in turn can be influenced by the linear system dynamics. Based on the findings presented, it is possible to conduct targeted experiments to confirm and add to these initial results.

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.

Objective
We aimed to find objective measures of the impact of spatially disorienting (SD) stimuli on pilot cognition in an ecologically valid environment.
Background
SD frequently occurs in military rotary-wing operations and often contributes to mishaps. Effects of SD stimuli on pilots are usually quantified using control errors, but effects on cognition have not yet been successfully quantified.
Method
Military helicopter pilots (n = 14) performed scenarios with six SD stimuli (SD condition) and six corresponding control stimuli (NoSD condition) in a motion-base simulator with integrated virtual reality headset. SD stimuli were: false horizon, featureless terrain, leans, brownout, a somatogyral yaw illusion, and loss of horizon due to night vision goggles (NVGs). Mental workload was measured using auditory arithmetic task performance and attentional focus was measured using eye-tracking.
Results
Average arithmetic task performance was significantly impaired, and proportional gaze dwell time on the attitude indicator was significantly increased in the SD compared to the NoSD condition. Of the six SD stimuli, the featureless terrain, the leans, and the brownout induced significant effects on performance, whereas the featureless terrain, brownout, and false horizon significantly affected gaze behavior. The NVGs and somatogyral yaw stimuli did not induce significant effects. Pilots’ self-reports indicated awareness of all SD stimuli, except for the featureless terrain.
Conclusion
The results indicate that SD impacts pilot mental workload and attentional focus.
Application
Modern military aircraft present a large volume of mission-related information to pilots. This study shows that SD stimuli may negatively impact the processing of such information.