M.M. van Paassen
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Journal article
(2026)
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Yoshinari Hashimoto, Ryosuke Masuda, Satoki Yasuhara, Max Mulder, Marinus M.(René) van Paassen
To achieve the efficient and stable operation of a blast furnace (BF) in the steel industry while retaining the critical skills of expert human operators, we developed a novel human-machine interface (HMI) grounded in ecological interface design (EID) principles. The BF is a complex, large-scale chemical reactor to produce hot metal where internal temperatures can reach up to 2000 °C. Due to the inherent difficulty of full process automation, the EID framework was adopted to support operators’ situation awareness and keep human expertise central to decision-making. The developed ecological interface (EI) enhances manual control using a transient model, allowing operators to explore the best control action by showing the predicted future states of controlled variables resulting from their hypothetical control actions. Furthermore, the developed EI provides operators with explicit information on the mass and energy balances linking control means and ends, which is intended to support situation awareness. To facilitate comprehensive training, an operation simulator, capable of running at 300 times real-time speed, was integrated with the developed EI. Human-in-the-loop experiments with 16 subjects showed that the EI significantly reduced the number of control actions in both scenarios and reduced the HMT control error and the duration of ΔP upper-bound violations in the complex scenario, whereas no statistically significant reductions were observed in coke rate or carbon intensity.
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To achieve the efficient and stable operation of a blast furnace (BF) in the steel industry while retaining the critical skills of expert human operators, we developed a novel human-machine interface (HMI) grounded in ecological interface design (EID) principles. The BF is a complex, large-scale chemical reactor to produce hot metal where internal temperatures can reach up to 2000 °C. Due to the inherent difficulty of full process automation, the EID framework was adopted to support operators’ situation awareness and keep human expertise central to decision-making. The developed ecological interface (EI) enhances manual control using a transient model, allowing operators to explore the best control action by showing the predicted future states of controlled variables resulting from their hypothetical control actions. Furthermore, the developed EI provides operators with explicit information on the mass and energy balances linking control means and ends, which is intended to support situation awareness. To facilitate comprehensive training, an operation simulator, capable of running at 300 times real-time speed, was integrated with the developed EI. Human-in-the-loop experiments with 16 subjects showed that the EI significantly reduced the number of control actions in both scenarios and reduced the HMT control error and the duration of ΔP upper-bound violations in the complex scenario, whereas no statistically significant reductions were observed in coke rate or carbon intensity.
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.
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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.
Journal article
(2026)
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Eline S. Bakker, Ferdinand Dijkstra, Clark Borst, Marinus M. van Paassen, Max Mulder
Air traffic growth leads to increasingly complex traffic situations near large airports. To support air traffic controllers in maintaining safety and improving efficiency, new support tools are needed. This paper presents the Inbound Traffic Support System (ITSS), a visual interface prototype developed in collaboration with the Netherlands’ Air Navigation Service Provider, LVNL. The interface visualizes constraints associated with the merging task and was tested for the South Sector of Amsterdam Airport Schiphol. By portraying potential merging solutions, it helps area controllers anticipate the impact of their decisions and supports their role in managing inbound traffic. Professional controllers provided positive feedback during the development phase while also identifying concerns such as display clutter that should be addressed. An experiment with semiprofessional controllers assessed the impact of the interface on operator performance and workload. Participants were instructed to rely on the interface rather than operational experience to ensure consistent results. Findings showed that the interface improved aircraft adherence to altitude restrictions and reduced the number of clearances needed in more complex scenarios. By revealing the solution space, the ITSS encourages earlier planning and reduces the need for late-stage interventions. Future work will include professional controller evaluations, increased simulation fidelity, and extended training.
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Air traffic growth leads to increasingly complex traffic situations near large airports. To support air traffic controllers in maintaining safety and improving efficiency, new support tools are needed. This paper presents the Inbound Traffic Support System (ITSS), a visual interface prototype developed in collaboration with the Netherlands’ Air Navigation Service Provider, LVNL. The interface visualizes constraints associated with the merging task and was tested for the South Sector of Amsterdam Airport Schiphol. By portraying potential merging solutions, it helps area controllers anticipate the impact of their decisions and supports their role in managing inbound traffic. Professional controllers provided positive feedback during the development phase while also identifying concerns such as display clutter that should be addressed. An experiment with semiprofessional controllers assessed the impact of the interface on operator performance and workload. Participants were instructed to rely on the interface rather than operational experience to ensure consistent results. Findings showed that the interface improved aircraft adherence to altitude restrictions and reduced the number of clearances needed in more complex scenarios. By revealing the solution space, the ITSS encourages earlier planning and reduces the need for late-stage interventions. Future work will include professional controller evaluations, increased simulation fidelity, and extended training.
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.
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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.
Journal article
(2026)
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Jiayu Chen, Annemarie Landman, Alexis Derumigny, Olaf Stroosma, M. M. van Paassen, Max Mulder
Objective: We aim to investigate how pilots’ startle and surprise responses affect information-processing performance during simulated in-flight events. Background: Startle and surprise are distinct constructs, each with their own potential effects on pilot’s performance during unexpected in-flight events. Prior research suggests that startle may impair performance through stress-induced cognitive interference, whereas surprise may do so via cognitive demands associated with sensemaking. Thus, we hypothesized that both startle and surprise would negatively affect information-processing performance on a secondary auditory cognitive task. Method: Using a motion-based hexapod simulator and a twin-propeller aircraft model, 26 pilots each performed eight single-pilot flight scenarios, which were designed to elicit varying levels of startle and surprise responses. Linear mixed-effects models were employed to analyse the relationships between self-report startle and surprise with secondary task performance, while controlling for individual differences and differences between the scenarios. Results: The results revealed that higher startle was significantly associated with reduced information-processing speed. For surprise, no significant association was found. Conclusion: The findings suggest that, within the context of the tested scenarios, startle appeared to impose a more pronounced disruptive effect on pilots’ information-processing performance than surprise. Application: The study underscores the need for tailored interventions to enhance pilots’ resilience to startle and calls for further research on ecologically valid methods to induce surprise for research and training purposes.
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Objective: We aim to investigate how pilots’ startle and surprise responses affect information-processing performance during simulated in-flight events. Background: Startle and surprise are distinct constructs, each with their own potential effects on pilot’s performance during unexpected in-flight events. Prior research suggests that startle may impair performance through stress-induced cognitive interference, whereas surprise may do so via cognitive demands associated with sensemaking. Thus, we hypothesized that both startle and surprise would negatively affect information-processing performance on a secondary auditory cognitive task. Method: Using a motion-based hexapod simulator and a twin-propeller aircraft model, 26 pilots each performed eight single-pilot flight scenarios, which were designed to elicit varying levels of startle and surprise responses. Linear mixed-effects models were employed to analyse the relationships between self-report startle and surprise with secondary task performance, while controlling for individual differences and differences between the scenarios. Results: The results revealed that higher startle was significantly associated with reduced information-processing speed. For surprise, no significant association was found. Conclusion: The findings suggest that, within the context of the tested scenarios, startle appeared to impose a more pronounced disruptive effect on pilots’ information-processing performance than surprise. Application: The study underscores the need for tailored interventions to enhance pilots’ resilience to startle and calls for further research on ecologically valid methods to induce surprise for research and training purposes.
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.
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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.
Cabin crew startle and surprise
Occurrence and impact
Journal article
(2026)
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D.M.L. Vlaskamp, Alex Pollitt, J. Blundell, A. Landman, Eric L. Groen, M.M. van Paassen, O. Stroosma, Max Mulder
Startle and surprise are known to impair pilot performance during non-normal flight conditions. In the past years research has focused on developing strategies to help pilots mitigate startle and surprise responses to unexpected situations. However, no equivalent research exists for cabin crew. This study investigates the prevalence, impact on crew performance, and emotional consequences of startle and surprise among cabin crew, as well as the way in which it is currently trained. A survey was conducted among 348 European-based cabin crew to gather data on in-flight events that provoked startle, surprise, or both. Objective measures included stress ratings, perceived impact on performance, perceived emotional control, training exposure, and lasting anxiety. Qualitative data were also collected to assess crew responses and coping mechanisms. Out of 348 respondents, 79.3% reported having experienced startle, surprise or both. High stress levels were significantly correlated with perceived performance impairments and lasting anxiety. Current startle and surprise training improved perceived preparedness but did not reduce perceived stress or anxiety. Emotional control was deemed a key protective factor. Startle and surprise can cause operationally significant impact on cabin crew performance. Training that includes simple, effective emotional regulation strategies, such as those developed for pilots, is currently lacking and may help improve both immediate performance and long-term psychological resilience in the cabin environment, as may increased peer-support access.
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Startle and surprise are known to impair pilot performance during non-normal flight conditions. In the past years research has focused on developing strategies to help pilots mitigate startle and surprise responses to unexpected situations. However, no equivalent research exists for cabin crew. This study investigates the prevalence, impact on crew performance, and emotional consequences of startle and surprise among cabin crew, as well as the way in which it is currently trained. A survey was conducted among 348 European-based cabin crew to gather data on in-flight events that provoked startle, surprise, or both. Objective measures included stress ratings, perceived impact on performance, perceived emotional control, training exposure, and lasting anxiety. Qualitative data were also collected to assess crew responses and coping mechanisms. Out of 348 respondents, 79.3% reported having experienced startle, surprise or both. High stress levels were significantly correlated with perceived performance impairments and lasting anxiety. Current startle and surprise training improved perceived preparedness but did not reduce perceived stress or anxiety. Emotional control was deemed a key protective factor. Startle and surprise can cause operationally significant impact on cabin crew performance. Training that includes simple, effective emotional regulation strategies, such as those developed for pilots, is currently lacking and may help improve both immediate performance and long-term psychological resilience in the cabin environment, as may increased peer-support access.
Journal article
(2026)
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Daan Vlaskamp, James Blundell, Annemarie Landman, Eric L. Groen, M. M. van Paassen, Olaf Stroosma, Max Mulder
Startle and surprise are known to potentially incapacitate professionals who respond to emergency situations. In the aviation domain, self-management methods to prevent such incapacitation have been introduced for pilots. This study aims to explore the relevance of similar self-management methods tailored for cabin crew, using subjective evaluation by cabin crew and cabin crew instructors. First, a method was designed and refined using interviews and a focus group with nine subject-matter experts, consisting of four steps: protect, reset, check and act. Second, relevance of the method was quasi-experimentally tested by letting 15 cabin crew members apply it in a simulated firefighting scenario. After performing the scenario, they rated whether the method had positive or negative effects, was difficult or easy to use, and they retrospectively rated their perceived stress before and after applying the method. Participants positively rated the method’s effectiveness and usability, and reported the method to significantly reduce perceived stress. Participants provided several suggestions for improving the method, and for approaches to integrate the method more effectively into existing protocols. The outcomes of this study provide direction on the design and use of self-management methods that can help manage startle and surprise in teams.
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Startle and surprise are known to potentially incapacitate professionals who respond to emergency situations. In the aviation domain, self-management methods to prevent such incapacitation have been introduced for pilots. This study aims to explore the relevance of similar self-management methods tailored for cabin crew, using subjective evaluation by cabin crew and cabin crew instructors. First, a method was designed and refined using interviews and a focus group with nine subject-matter experts, consisting of four steps: protect, reset, check and act. Second, relevance of the method was quasi-experimentally tested by letting 15 cabin crew members apply it in a simulated firefighting scenario. After performing the scenario, they rated whether the method had positive or negative effects, was difficult or easy to use, and they retrospectively rated their perceived stress before and after applying the method. Participants positively rated the method’s effectiveness and usability, and reported the method to significantly reduce perceived stress. Participants provided several suggestions for improving the method, and for approaches to integrate the method more effectively into existing protocols. The outcomes of this study provide direction on the design and use of self-management methods that can help manage startle and surprise in teams.
Haptic Shared Control during Automated Take-Off
Effects on Trust and Workload
Automation reduces pilot workload but introduces risks such as mode confusion and automation surprise, which can impair situation awareness and delay responses. Haptic shared control, delivered via active sidesticks, may mitigate these risks by providing continuous force feedback to convey automation intent and warnings. This study examined how the haptic information in shared control systems affects trust, workload, and awareness during automated take-off scenarios involving normal and abnormal autopilot behaviors. Twenty participants monitored automation performance while completing a secondary 2-back task under two shared control conditions: haptic shared versus input-mixing shared control. Results show that abnormal autopilot behaviors significantly increased workload and reduced trust, with haptic shared control having context-dependent effects - supporting trust and reducing workload during over-rotation but lowering trust in normal conditions. Haptic shared control had no significant effect upon participant situation awareness or secondary task performance. These findings suggest that the provision of haptic information within shared control can support pilots during automation anomalies but require human-centered implementation and training to avoid unintended effects under normal conditions.
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Automation reduces pilot workload but introduces risks such as mode confusion and automation surprise, which can impair situation awareness and delay responses. Haptic shared control, delivered via active sidesticks, may mitigate these risks by providing continuous force feedback to convey automation intent and warnings. This study examined how the haptic information in shared control systems affects trust, workload, and awareness during automated take-off scenarios involving normal and abnormal autopilot behaviors. Twenty participants monitored automation performance while completing a secondary 2-back task under two shared control conditions: haptic shared versus input-mixing shared control. Results show that abnormal autopilot behaviors significantly increased workload and reduced trust, with haptic shared control having context-dependent effects - supporting trust and reducing workload during over-rotation but lowering trust in normal conditions. Haptic shared control had no significant effect upon participant situation awareness or secondary task performance. These findings suggest that the provision of haptic information within shared control can support pilots during automation anomalies but require human-centered implementation and training to avoid unintended effects under normal conditions.
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.
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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.
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.
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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.
On the road to comfort
Evaluating the influence of motion predictability on motion sickness in automated vehicles
Journal article
(2025)
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Rowenna Wijlens, Boris J.V. Englebert, Atsushi Takamatsu, Mitsuhiro Makita, Hikaru Sato, Takahiro Wada, Joost C.F. de Winter, Marinus M. van Paassen, Max Mulder
Automated vehicles could increase the risk of motion sickness because occupants are not involved in driving and do not watch the road. This paper aimed to investigate the influence of motion predictability on motion sickness in automated vehicles, as better motion anticipation is believed to mitigate motion sickness. In a simulator-based study, twenty participants experienced two driving conditions differing only in turn directions. The repetitive condition featured a repeating turn direction pattern. The non-repetitive condition contained pseudo-randomly ordered turn directions. To mimic an ‘eyes-off-the-road’ setting and prevent visual motion anticipation, road visuals were omitted. No significant differences in sickness or head motion, a metric for motion anticipation, were found between the conditions. No participant recognised the repeating turn pattern. This suggests no increased motion anticipation in the repetitive condition, possibly due to a reduced ability to recognise a repeating motion pattern in one degree of freedom within more complex motion.
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Automated vehicles could increase the risk of motion sickness because occupants are not involved in driving and do not watch the road. This paper aimed to investigate the influence of motion predictability on motion sickness in automated vehicles, as better motion anticipation is believed to mitigate motion sickness. In a simulator-based study, twenty participants experienced two driving conditions differing only in turn directions. The repetitive condition featured a repeating turn direction pattern. The non-repetitive condition contained pseudo-randomly ordered turn directions. To mimic an ‘eyes-off-the-road’ setting and prevent visual motion anticipation, road visuals were omitted. No significant differences in sickness or head motion, a metric for motion anticipation, were found between the conditions. No participant recognised the repeating turn pattern. This suggests no increased motion anticipation in the repetitive condition, possibly due to a reduced ability to recognise a repeating motion pattern in one degree of freedom within more complex motion.
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.
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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.
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.
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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.
Air traffic control is advancing digitalization by developing advanced decision-support systems, where the way information is presented to operators plays a central role in shaping performance. However, the effects of different visual representations within these systems on human decision-making remain not fully understood. In this study, we compared two Conflict Detection and Resolution (CD&R) tools: the Highly Interactive Problem Solver (HIPS) and the Solution Space Diagram (SSD). Although both systems are grounded in the same control problem, they differ in how they represent the control constraints that define conflict conditions and feasible responses. Through a human-in-the-loop experiment under low-and high-traffic conditions, we analyzed how these differences influence decision-making. Results showed that, particularly in low-density traffic, HIPS enabled quicker responses, fewer commands, and smaller safety margins, whereas SSD, despite receiving more favorable subjective ratings, led to greater variability in actions. These findings suggest that visualization significantly impacts decision-making consistency and efficiency. However, in highly complex environments, overall effectiveness may depend more on operators' ability to shift and adapt decision-making patterns facilitated by the interface than on specific visual elements.
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Air traffic control is advancing digitalization by developing advanced decision-support systems, where the way information is presented to operators plays a central role in shaping performance. However, the effects of different visual representations within these systems on human decision-making remain not fully understood. In this study, we compared two Conflict Detection and Resolution (CD&R) tools: the Highly Interactive Problem Solver (HIPS) and the Solution Space Diagram (SSD). Although both systems are grounded in the same control problem, they differ in how they represent the control constraints that define conflict conditions and feasible responses. Through a human-in-the-loop experiment under low-and high-traffic conditions, we analyzed how these differences influence decision-making. Results showed that, particularly in low-density traffic, HIPS enabled quicker responses, fewer commands, and smaller safety margins, whereas SSD, despite receiving more favorable subjective ratings, led to greater variability in actions. These findings suggest that visualization significantly impacts decision-making consistency and efficiency. However, in highly complex environments, overall effectiveness may depend more on operators' ability to shift and adapt decision-making patterns facilitated by the interface than on specific visual elements.
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.
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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.
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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.