D.M. Pool
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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.
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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.
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.
InceptionTime neural network models were trained to detect distractions in manual control tasks with pursuit and preview displays. Training and test data were collected in an experiment where ten participants were deliberately distracted from the primary control task using the Surrogate Reference Task. Overall, distractions are easier to detect in pursuit tasks, with test accuracies of around 80% and 60% for pursuit and preview data, respectively. With preview, human controllers see the future target trajectory, which enables them to mitigate distraction effects. Unexpectedly, data with longer distractions from ‘hard’ secondary tasks are more difficult to classify than ‘easy’ distractions; an effect attributed to differences in human behavior between the training and test data collection conditions. These results show clear opportunities for neural network models to detect distractions, in real-time, for increasing safety of human-operated vehicles.
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InceptionTime neural network models were trained to detect distractions in manual control tasks with pursuit and preview displays. Training and test data were collected in an experiment where ten participants were deliberately distracted from the primary control task using the Surrogate Reference Task. Overall, distractions are easier to detect in pursuit tasks, with test accuracies of around 80% and 60% for pursuit and preview data, respectively. With preview, human controllers see the future target trajectory, which enables them to mitigate distraction effects. Unexpectedly, data with longer distractions from ‘hard’ secondary tasks are more difficult to classify than ‘easy’ distractions; an effect attributed to differences in human behavior between the training and test data collection conditions. These results show clear opportunities for neural network models to detect distractions, in real-time, for increasing safety of human-operated vehicles.
Detecting Operator Distraction Through Multivariate Signal Processing
Statistical and Machine Learning Perspectives
This paper compares different multivariate signal processing methods - both based on classical statistical or machine learning models - for detecting human operator distraction in a preview tracking task. Binary Time Series Classification (TSC) methods proposed in prior work, which require both 'non-distracted' and 'distracted' training examples, were found to be out-performed by dedicated Anomaly Detection (AD) methods that aim to detect outliers from a learned 'non-distracted' prior distribution only. Even when applied in an ensemble learning framework, the TSC methods suffer from high false alarms and are highly sensitive to domain shift, due to their explicit reliance on representative 'distracted' training data. The considered statistical AD method, based on calculation of the Mahalanobis Distance (MD) of tested multivariate time-series samples, was found to be outperformed by a machine learning One-Class Support Vector Machine (OCSVM) method, as the latter more faithfully captured the (non-normal) distribution of the 'non-distracted' training data. Combining both AD methods in a meta detector only gave marginal improvement in the attained F1 score (0.5694) compared to the OCSVM (0.5610). The presented findings show that statistical and machine learning methods for AD can enable relevant detection of potentially impactful moments of distraction from human control data, which can be tested in real-time monitoring and adaptive support frameworks.
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This paper compares different multivariate signal processing methods - both based on classical statistical or machine learning models - for detecting human operator distraction in a preview tracking task. Binary Time Series Classification (TSC) methods proposed in prior work, which require both 'non-distracted' and 'distracted' training examples, were found to be out-performed by dedicated Anomaly Detection (AD) methods that aim to detect outliers from a learned 'non-distracted' prior distribution only. Even when applied in an ensemble learning framework, the TSC methods suffer from high false alarms and are highly sensitive to domain shift, due to their explicit reliance on representative 'distracted' training data. The considered statistical AD method, based on calculation of the Mahalanobis Distance (MD) of tested multivariate time-series samples, was found to be outperformed by a machine learning One-Class Support Vector Machine (OCSVM) method, as the latter more faithfully captured the (non-normal) distribution of the 'non-distracted' training data. Combining both AD methods in a meta detector only gave marginal improvement in the attained F1 score (0.5694) compared to the OCSVM (0.5610). The presented findings show that statistical and machine learning methods for AD can enable relevant detection of potentially impactful moments of distraction from human control data, which can be tested in real-time monitoring and adaptive support frameworks.
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.
Touchscreens are used increasingly in moving environments, where they can in fact be very difficult to use. A key issue is biodynamic feedthrough (BDFT): perturbing finger movements induced by feedthrough of motion accelerations (e.g., turbulence) through users' outstretched arms. Modeling the BDFT dynamics allows such erroneous components of touchscreen inputs to be computationally removed. Estimating accurate BDFT models using system identification methods traditionally requires experiments with prolonged measurement times. However, for touchscreen BDFT measurements, where participants generally keep their arms outstretched for the full measurement duration, long measurements lead to muscle fatigue, which impacts data quality. By means of a dedicated human-in-the-loop simulator experiment, this paper shows that reducing the measurement duration to around 30 s does not significantly affect participants BDFT dynamics, or our ability to model them. As a result, these experimental findings enable future investigations to use shorter measurement times to increase participant comfort without compromising BDFT model quality.
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Touchscreens are used increasingly in moving environments, where they can in fact be very difficult to use. A key issue is biodynamic feedthrough (BDFT): perturbing finger movements induced by feedthrough of motion accelerations (e.g., turbulence) through users' outstretched arms. Modeling the BDFT dynamics allows such erroneous components of touchscreen inputs to be computationally removed. Estimating accurate BDFT models using system identification methods traditionally requires experiments with prolonged measurement times. However, for touchscreen BDFT measurements, where participants generally keep their arms outstretched for the full measurement duration, long measurements lead to muscle fatigue, which impacts data quality. By means of a dedicated human-in-the-loop simulator experiment, this paper shows that reducing the measurement duration to around 30 s does not significantly affect participants BDFT dynamics, or our ability to model them. As a result, these experimental findings enable future investigations to use shorter measurement times to increase participant comfort without compromising BDFT model quality.
This paper applies model-based biodynamic feedthrough (BDFT) cancellation to a touchscreen dragging task during realistic vertical (vertical) and lateral (horizontal) aircraft turbulence, to mitigate erroneous turbulence-induced inputs. One-size-fits-all (OSFA) BDFT models were used to model the influence of turbulence accelerations on finger position, achieving average quality-of-fits of 61% and 69% in the vertical and horizontal screen directions, respectively. On average, 27% of the touch input error variance was mitigated these OSFA models, with individualized models providing only a marginal improvement (+4%). The application of OSFA models identified from a condition with equally-scaled vertical and horizontal motion (adjusted intensity) to the realistic turbulence condition did not significantly affect cancellation performance, indicating that BDFT models may not need to be adaptive to varying motion intensity. However, consistent with earlier work, BDFT dynamics were found to vary between vertical and horizontal finger movements, with BDFT dynamics exhibiting lower stiffness and a higher static gain for vertical BDFT. On average, the linear BDFT-related component of touch input errors contributed 41% of the overall error variance, indicating that current linear BDFT model may need to be extended to include nonlinear effects, such as varying finger friction.
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This paper applies model-based biodynamic feedthrough (BDFT) cancellation to a touchscreen dragging task during realistic vertical (vertical) and lateral (horizontal) aircraft turbulence, to mitigate erroneous turbulence-induced inputs. One-size-fits-all (OSFA) BDFT models were used to model the influence of turbulence accelerations on finger position, achieving average quality-of-fits of 61% and 69% in the vertical and horizontal screen directions, respectively. On average, 27% of the touch input error variance was mitigated these OSFA models, with individualized models providing only a marginal improvement (+4%). The application of OSFA models identified from a condition with equally-scaled vertical and horizontal motion (adjusted intensity) to the realistic turbulence condition did not significantly affect cancellation performance, indicating that BDFT models may not need to be adaptive to varying motion intensity. However, consistent with earlier work, BDFT dynamics were found to vary between vertical and horizontal finger movements, with BDFT dynamics exhibiting lower stiffness and a higher static gain for vertical BDFT. On average, the linear BDFT-related component of touch input errors contributed 41% of the overall error variance, indicating that current linear BDFT model may need to be extended to include nonlinear effects, such as varying finger friction.
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.
Review
(2026)
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Robert Jacumet, Maurice Kolff, Joost Venrooij, Markus Schwienbacher, Sebastian Wagner, Dirk Wollherr, Marion Leibold, Daan M. Pool, Max Mulder
Driving simulators are essential tools to guide automotive research and development. Their motion system requires a motion cueing algorithm (MCA) to keep the simulator motion platform within its physical boundaries, while simultaneously aiming to recreate the sensation of real vehicle motion. While traditional, filter-based approaches are still predominant, optimization-based MCAs have been at the center of MCA research for over a decade due to their ability to systematically improve motion cueing quality through explicit cost function design and constraints handling. However, despite their demonstrated advantages, these optimization-based methods have not yet achieved widespread adoption in driving simulation. This paper therefore provides a comprehensive review of optimization-based MCAs for driving simulation, categorizing and comparing algorithms, describing their key developments and core characteristics. The current limited real-time capability, lack of accurate evaluation methods, challenges in cost function design and its tuning, and the current lack of accurate future reference predictions are identified as key barriers to the practical deployment and widespread use of optimization-based MCAs. These theoretical and practical challenges are further reviewed, providing guidelines to advance the theory and application of optimization-based MCAs. Central in these advancements are a better understanding of which motions constitute a realistic motion experience, a framework allowing to compare the achieved motion fidelity of MCAs across papers, the design of the cost function focusing on human motion perception, and techniques for easing up the tuning process to swiftly reach high quality tunings for different simulators, scenarios, and use cases. We identify the need for improving the real-time capability, and providing high quality motion reference predictions using learning-based approaches on diverse datasets, along with techniques to handle existing uncertainties. Following these guidelines, new foundations for optimization-based algorithms in driving simulation can be achieved, which will significantly impact the research and development of automotive systems.
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Driving simulators are essential tools to guide automotive research and development. Their motion system requires a motion cueing algorithm (MCA) to keep the simulator motion platform within its physical boundaries, while simultaneously aiming to recreate the sensation of real vehicle motion. While traditional, filter-based approaches are still predominant, optimization-based MCAs have been at the center of MCA research for over a decade due to their ability to systematically improve motion cueing quality through explicit cost function design and constraints handling. However, despite their demonstrated advantages, these optimization-based methods have not yet achieved widespread adoption in driving simulation. This paper therefore provides a comprehensive review of optimization-based MCAs for driving simulation, categorizing and comparing algorithms, describing their key developments and core characteristics. The current limited real-time capability, lack of accurate evaluation methods, challenges in cost function design and its tuning, and the current lack of accurate future reference predictions are identified as key barriers to the practical deployment and widespread use of optimization-based MCAs. These theoretical and practical challenges are further reviewed, providing guidelines to advance the theory and application of optimization-based MCAs. Central in these advancements are a better understanding of which motions constitute a realistic motion experience, a framework allowing to compare the achieved motion fidelity of MCAs across papers, the design of the cost function focusing on human motion perception, and techniques for easing up the tuning process to swiftly reach high quality tunings for different simulators, scenarios, and use cases. We identify the need for improving the real-time capability, and providing high quality motion reference predictions using learning-based approaches on diverse datasets, along with techniques to handle existing uncertainties. Following these guidelines, new foundations for optimization-based algorithms in driving simulation can be achieved, which will significantly impact the research and development of automotive systems.
Electrovibration technology enables tactile texture rendering on capacitive touchscreens by modulating friction between the finger and the screen through electrostatic attraction forces, generated by applying an alternating voltage signal to the screen. Accurate signal calibration is essential for robust texture rendering but remains challenging due to variations in sliding speed, applied force, and individual skin mechanics, all of which unpredictably affect frictional behavior. Here, we investigate how exploration conditions affect electrovibration-induced finger friction on touchscreens and the role of skin mechanics in this process. Ten participants slid their index fingers across an electrovibration-enabled touchscreen at five sliding speeds (20∼100 mm/s) and applied force levels (0.2∼0.6 N). Contact forces and skin accelerations were measured while amplitude modulated voltage signals spanning the tactile frequency range were applied to the screen. We modeled the finger-touchscreen friction response as a first-order system and the skin mechanics as a mass–spring-damper system. Results showed that sliding speed influenced the friction response's cutoff frequency, along with the estimated finger moving mass and stiffness. For every 1 mm/s increase in speed, the cutoff frequency, the finger moving mass, and stiffness increased by 13.8 Hz, 3.23×10−5 kg, and 4.04 N/m, respectively. Correlation analysis revealed that finger stiffness had a greater impact on the cutoff frequency than moving mass. Notably, we observed a substantial inter-participant variability in both finger-display interaction and skin mechanics parameters. Finally, we developed a speed-dependent friction model to support consistent and perceptually stable electrovibration-based haptic feedback across varying user conditions.
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Electrovibration technology enables tactile texture rendering on capacitive touchscreens by modulating friction between the finger and the screen through electrostatic attraction forces, generated by applying an alternating voltage signal to the screen. Accurate signal calibration is essential for robust texture rendering but remains challenging due to variations in sliding speed, applied force, and individual skin mechanics, all of which unpredictably affect frictional behavior. Here, we investigate how exploration conditions affect electrovibration-induced finger friction on touchscreens and the role of skin mechanics in this process. Ten participants slid their index fingers across an electrovibration-enabled touchscreen at five sliding speeds (20∼100 mm/s) and applied force levels (0.2∼0.6 N). Contact forces and skin accelerations were measured while amplitude modulated voltage signals spanning the tactile frequency range were applied to the screen. We modeled the finger-touchscreen friction response as a first-order system and the skin mechanics as a mass–spring-damper system. Results showed that sliding speed influenced the friction response's cutoff frequency, along with the estimated finger moving mass and stiffness. For every 1 mm/s increase in speed, the cutoff frequency, the finger moving mass, and stiffness increased by 13.8 Hz, 3.23×10−5 kg, and 4.04 N/m, respectively. Correlation analysis revealed that finger stiffness had a greater impact on the cutoff frequency than moving mass. Notably, we observed a substantial inter-participant variability in both finger-display interaction and skin mechanics parameters. Finally, we developed a speed-dependent friction model to support consistent and perceptually stable electrovibration-based haptic feedback across varying user conditions.
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.
Journal article
(2025)
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Maurice Kolff, Chantal Himmels, Joost Venrooij, Arben Parduzi, Daan M. Pool, Andreas Riener, Max Mulder
This paper investigates the effects of motion mismatches on simulator sickness and subjective ratings of the motion. In an open-loop driving simulator experiment, participants were driven through a recorded urban drive twelve times, in which mismatches were induced by manipulating the following three aspects in motion cueing: (i) mismatches in specific vehicle axes, (ii) mismatch types (scaling, missing, and false cues), and (iii) inconsistent scaling between different motion axes. Subjects (N=52) reported simulator sickness post-hoc (after each drive), as well as continuously during each drive, a first in simulator sickness research. Furthermore, subjective post-hoc motion incongruence ratings on the quality of the motion were extracted. Results show that longitudinal motion mismatches lead to the most simulator sickness and the highest ratings, followed by mismatches in lateral motion, then yaw rate. False cues induce the most sickness, followed by missing and then scaled motion. Inconsistent scaling between the axes has no significant effect. The continuous sickness ratings support that the occurrence and severity of simulator sickness are indeed related to mismatches in simulator motion of specific maneuvers. This paper contributes to an improved understanding of the relationship between simulator motion and sickness, allowing for more targeted motion cueing strategies to prevent and reduce sickness in driving simulators. These strategies may include the appropriate selection of the simulator, the motion cueing, and the sample of participants, following the presented results.
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This paper investigates the effects of motion mismatches on simulator sickness and subjective ratings of the motion. In an open-loop driving simulator experiment, participants were driven through a recorded urban drive twelve times, in which mismatches were induced by manipulating the following three aspects in motion cueing: (i) mismatches in specific vehicle axes, (ii) mismatch types (scaling, missing, and false cues), and (iii) inconsistent scaling between different motion axes. Subjects (N=52) reported simulator sickness post-hoc (after each drive), as well as continuously during each drive, a first in simulator sickness research. Furthermore, subjective post-hoc motion incongruence ratings on the quality of the motion were extracted. Results show that longitudinal motion mismatches lead to the most simulator sickness and the highest ratings, followed by mismatches in lateral motion, then yaw rate. False cues induce the most sickness, followed by missing and then scaled motion. Inconsistent scaling between the axes has no significant effect. The continuous sickness ratings support that the occurrence and severity of simulator sickness are indeed related to mismatches in simulator motion of specific maneuvers. This paper contributes to an improved understanding of the relationship between simulator motion and sickness, allowing for more targeted motion cueing strategies to prevent and reduce sickness in driving simulators. These strategies may include the appropriate selection of the simulator, the motion cueing, and the sample of participants, following the presented results.
Aerodynamic stall has been a critical factor in recent aircraft crashes, leading to revised regulations for simulator-based stall prevention and recovery training. However, the updated regulations still lack an objectively defined level of accuracy for simulators' stall models that ensures effective pilot training. To help determine this required accuracy, this paper investigates how the Just Noticeable Difference (JND) thresholds for deviations in a stall model's ‘stall abruptness’ parameter translate from a passive observer setting (typical JND experiments) to an active flying scenario (realistic training task). An experiment was performed in the SIMONA Research Simulator with 16 active pilots, whose sensitivity to stall abruptness variations was measured in both these scenarios. In the passive scenario, pilots' JND thresholds were measured using a staircase procedure in a symmetric stall maneuver flown by a stall autopilot. In the active scenario, the method of constant stimuli was used to determine the JND thresholds when the pilots themselves actively flew the same stall maneuver. The average JND thresholds for the passive and active scenarios were estimated by fitting a psychometric curve to the combined responses of all participants. Overall, the passive JND thresholds for the stall abruptness parameter, with an average Weber fraction of 0.11±0.094, were lower than those measured in an earlier experiment (0.16±0.14), indicating a higher sensitivity. Furthermore, the psychometric curve of the active experiment was found to lie entirely to the right of the passive psychometric function: the active JND threshold was found to be five times higher than the passive JND threshold. Overall, this indicates a decreased sensitivity to changes in stall abruptness -- and hence a reduced demand on its modeling accuracy -- when pilots are flying a stall themselves.
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Aerodynamic stall has been a critical factor in recent aircraft crashes, leading to revised regulations for simulator-based stall prevention and recovery training. However, the updated regulations still lack an objectively defined level of accuracy for simulators' stall models that ensures effective pilot training. To help determine this required accuracy, this paper investigates how the Just Noticeable Difference (JND) thresholds for deviations in a stall model's ‘stall abruptness’ parameter translate from a passive observer setting (typical JND experiments) to an active flying scenario (realistic training task). An experiment was performed in the SIMONA Research Simulator with 16 active pilots, whose sensitivity to stall abruptness variations was measured in both these scenarios. In the passive scenario, pilots' JND thresholds were measured using a staircase procedure in a symmetric stall maneuver flown by a stall autopilot. In the active scenario, the method of constant stimuli was used to determine the JND thresholds when the pilots themselves actively flew the same stall maneuver. The average JND thresholds for the passive and active scenarios were estimated by fitting a psychometric curve to the combined responses of all participants. Overall, the passive JND thresholds for the stall abruptness parameter, with an average Weber fraction of 0.11±0.094, were lower than those measured in an earlier experiment (0.16±0.14), indicating a higher sensitivity. Furthermore, the psychometric curve of the active experiment was found to lie entirely to the right of the passive psychometric function: the active JND threshold was found to be five times higher than the passive JND threshold. Overall, this indicates a decreased sensitivity to changes in stall abruptness -- and hence a reduced demand on its modeling accuracy -- when pilots are flying a stall themselves.
To improve the safety of commercial air transport, pilots are required to train on simulators to recognize the characteristics of an impending stall and subsequently correctly recover from it. To prevent negative training, it is important that the accuracy of the used simulation models is sufficiently high. A key approach for modeling the nonlinear, unsteady aerodynamic effects during the stall is by using Kirchhoff's theory of flow separation. However, widespread difficulties exist in correctly estimating the stall-related parameters of nonlinear flow separation models from flight test data. Therefore, the research in this paper aims to increase the obtained model accuracy by making optimal use of already existing flight data via introduction of a slice-based modeling method. This is done by analyzing the change in the parameter estimate values when applying the system identification procedure to sliced partitions of simulated flight data, for both the pre-stall and post-stall phases. These partitions incrementally increase in size with time from the stall initiation. The simulation data is generated to be representative of the available flight test data, but with known ‘truth’ values for all estimated model parameters. The estimated value for each partition was compared to the true parameter setting in the simulation model used to create the data. It was also investigated whether this coincided with points of increased Fisher information in the data. Manually, an optimal window was found for each parameter for which the estimated value and truth value were equal and sufficient Fisher information was present. For the stall-related parameters the optimal window is often not more than 10 s wider than the stall. For the linear stability and control derivatives it is found that using more data generally results in a better estimate. Finally, the optimal window sizes were used for parameter estimation on the real flight test data. Even though this method represents a prototype, in more than half of the validation cases a decrease in MSE of 10% to 35% was achieved. This shows that the new slice-based modeling method is able to improve the accuracy of nonlinear stall models without the need to gather more flight data and may have applications that reach beyond the realm of stall modeling.
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To improve the safety of commercial air transport, pilots are required to train on simulators to recognize the characteristics of an impending stall and subsequently correctly recover from it. To prevent negative training, it is important that the accuracy of the used simulation models is sufficiently high. A key approach for modeling the nonlinear, unsteady aerodynamic effects during the stall is by using Kirchhoff's theory of flow separation. However, widespread difficulties exist in correctly estimating the stall-related parameters of nonlinear flow separation models from flight test data. Therefore, the research in this paper aims to increase the obtained model accuracy by making optimal use of already existing flight data via introduction of a slice-based modeling method. This is done by analyzing the change in the parameter estimate values when applying the system identification procedure to sliced partitions of simulated flight data, for both the pre-stall and post-stall phases. These partitions incrementally increase in size with time from the stall initiation. The simulation data is generated to be representative of the available flight test data, but with known ‘truth’ values for all estimated model parameters. The estimated value for each partition was compared to the true parameter setting in the simulation model used to create the data. It was also investigated whether this coincided with points of increased Fisher information in the data. Manually, an optimal window was found for each parameter for which the estimated value and truth value were equal and sufficient Fisher information was present. For the stall-related parameters the optimal window is often not more than 10 s wider than the stall. For the linear stability and control derivatives it is found that using more data generally results in a better estimate. Finally, the optimal window sizes were used for parameter estimation on the real flight test data. Even though this method represents a prototype, in more than half of the validation cases a decrease in MSE of 10% to 35% was achieved. This shows that the new slice-based modeling method is able to improve the accuracy of nonlinear stall models without the need to gather more flight data and may have applications that reach beyond the realm of stall modeling.
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.
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.
Recent aircraft have seen the implementation of touchscreens (TSCs) on the flight deck, as they enable more intuitive and direct human-machine interactions. However, biodynamic feedthrough (BDFT), i.e., the direct transmission of the aircraft's accelerations through the pilot's body to the control inputs, is a cause for concern, preventing safe and reliable use of TSCs in turbulence. This paper describes a simulator experiment evaluating the performance of model-based mitigation of BDFT in a TSC dragging task performed in turbulence. In the experiment, a total of nine different vertical (heave) motion perturbations were tested: multisine signals resembling turbulence, stationary (Gaussian) and variable (patchy) simulated turbulence, each at three intensity levels (RMS acceleration of 0.75, 0.5, and 0.25 m/s2). For the multisine turbulence signals, on average over 87% accuracy of the identified personalized BDFT models was achieved for the high and medium turbulence levels, reducing to 74% for the low-intensity turbulence due to degraded BDFT consistency. Furthermore, BDFT models fitted to the Gaussian turbulence data were found to achieve an accuracy comparable to that observed for the multisine motion disturbances, with only 3.5% lower performance on average. As expected, for the more time-varying patchy turbulence cases, model-based BDFT cancellation was found to be 4.7% lower than for the Gaussian turbulence data. Finally, models generalizing BDFT dynamics across participants or experimental runs were found to always be outperformed by individual participant and individual trial models, giving up to 10% higher identification performance. Overall, these findings show that a model-based approach to canceling the effects of BDFT mitigation for TSCs in turbulence is promising, but that real-time identification and time-varying BDFT models will be needed to achieve consistently high mitigation performance in realistic variable turbulence.
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Recent aircraft have seen the implementation of touchscreens (TSCs) on the flight deck, as they enable more intuitive and direct human-machine interactions. However, biodynamic feedthrough (BDFT), i.e., the direct transmission of the aircraft's accelerations through the pilot's body to the control inputs, is a cause for concern, preventing safe and reliable use of TSCs in turbulence. This paper describes a simulator experiment evaluating the performance of model-based mitigation of BDFT in a TSC dragging task performed in turbulence. In the experiment, a total of nine different vertical (heave) motion perturbations were tested: multisine signals resembling turbulence, stationary (Gaussian) and variable (patchy) simulated turbulence, each at three intensity levels (RMS acceleration of 0.75, 0.5, and 0.25 m/s2). For the multisine turbulence signals, on average over 87% accuracy of the identified personalized BDFT models was achieved for the high and medium turbulence levels, reducing to 74% for the low-intensity turbulence due to degraded BDFT consistency. Furthermore, BDFT models fitted to the Gaussian turbulence data were found to achieve an accuracy comparable to that observed for the multisine motion disturbances, with only 3.5% lower performance on average. As expected, for the more time-varying patchy turbulence cases, model-based BDFT cancellation was found to be 4.7% lower than for the Gaussian turbulence data. Finally, models generalizing BDFT dynamics across participants or experimental runs were found to always be outperformed by individual participant and individual trial models, giving up to 10% higher identification performance. Overall, these findings show that a model-based approach to canceling the effects of BDFT mitigation for TSCs in turbulence is promising, but that real-time identification and time-varying BDFT models will be needed to achieve consistently high mitigation performance in realistic variable turbulence.
Erratum
Effects of Target Trajectory Bandwidth on Manual Control Behavior in Pursuit and Preview Tracking (IEEE Trans. Hum.-Mach. Syst. (2020) 50:1 (68–78) DOI: 10.1109/THMS.2019.2947577)
This erratum applies to the following published paper [1]. In Fig. 10(e) and (f) of the published version of the paper, the measured values for the t
f and T
l,f (both having values around 1 s for the considered dataset) were interchanged. This erratum includes both the published and corrected versions of Fig. 10 and the related paragraph in the paper's Results section. PUBLISHED VERSION In preview tasks, bandwidth changes yield only minor adaptations in the model parameters, see Fig. 10. Only the average look-ahead time t
f decreases slightly with bandwidth from around 1.05 to 0.9 s, Fig. 10(e) but with substantial between-participant variability, as indicated by the overlapping confidence intervals. The lower t
f may not reflect a systematic adaptation to the bandwidth, but a more subtle adaptation to minimize the errors due to the additional high-amplitude sinusoids at 2.5 and 4 rad/s, see also Fig. 8c. The general way in which participants use the available preview for control is, however, not affected by the target signal bandwidth: the target response gain (K
f ≈ 0.95, Fig. 10(d)) and lag time-constant (T
l, f ≈ 1.15 s, Fig. 10(f)) are approximately invariant. The estimated control dynamics in Fig. 12 show that the target trajectory is tracked almost perfectly at all frequencies below 4 rad/s, mostly because the phase lead due to τ
f allows for synchronizing the CE output with the target signal (as opposed to pursuit tasks, see Fig. 11, bottom right). Therefore, different-bandwidth target signals provide no incentive for HCs to strongly adapt their control behavior in preview tasks. (Figure Presneted) CORRECTED VERSION In preview tasks, bandwidth changes yield only minor adaptations in the model parameters, see Fig. 10. Only the average target smoothing time-constant T
l, f decreases slightly with bandwidth [from around 1.05 to 0.9 s, Fig. 10(f)], but with substantial between-participant variability, as indicated by the overlapping confidence intervals. The lower T
l, f indicates that slightly more smoothing is applied to reduce tracking of the more high-amplitude high-frequency sinusoids in the 2.5 and 4 rad/s bandwidth signals through the feedforward response, see also Fig. 8(c). The general way in which participants use the available preview for control is, however, not affected by the target signal bandwidth: the target response gain [K
f ≈ 0.95, Fig. 10(d)] and look-ahead time [τ
f ≈ 1.15 s, Fig. 10(e)] are approximately invariant. The estimated control dynamics in Fig. 12 show that the target trajectory is tracked almost perfectly at all frequencies below 4 rad/s, mostly because the phase lead due to τ
f allows for synchronizing the CE output with the target signal (as opposed to pursuit tasks, see Fig. 11, bottom right). Therefore, different-bandwidth target signals provide no incentive for HCs to strongly adapt their control behavior in preview tasks. (Figure presented).
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This erratum applies to the following published paper [1]. In Fig. 10(e) and (f) of the published version of the paper, the measured values for the t f and T l,f (both having values around 1 s for the considered dataset) were interchanged. This erratum includes both the published and corrected versions of Fig. 10 and the related paragraph in the paper's Results section. PUBLISHED VERSION In preview tasks, bandwidth changes yield only minor adaptations in the model parameters, see Fig. 10. Only the average look-ahead time t f decreases slightly with bandwidth from around 1.05 to 0.9 s, Fig. 10(e) but with substantial between-participant variability, as indicated by the overlapping confidence intervals. The lower t f may not reflect a systematic adaptation to the bandwidth, but a more subtle adaptation to minimize the errors due to the additional high-amplitude sinusoids at 2.5 and 4 rad/s, see also Fig. 8c. The general way in which participants use the available preview for control is, however, not affected by the target signal bandwidth: the target response gain (K f ≈ 0.95, Fig. 10(d)) and lag time-constant (T l, f ≈ 1.15 s, Fig. 10(f)) are approximately invariant. The estimated control dynamics in Fig. 12 show that the target trajectory is tracked almost perfectly at all frequencies below 4 rad/s, mostly because the phase lead due to τ f allows for synchronizing the CE output with the target signal (as opposed to pursuit tasks, see Fig. 11, bottom right). Therefore, different-bandwidth target signals provide no incentive for HCs to strongly adapt their control behavior in preview tasks. (Figure Presneted) CORRECTED VERSION In preview tasks, bandwidth changes yield only minor adaptations in the model parameters, see Fig. 10. Only the average target smoothing time-constant T l, f decreases slightly with bandwidth [from around 1.05 to 0.9 s, Fig. 10(f)], but with substantial between-participant variability, as indicated by the overlapping confidence intervals. The lower T l, f indicates that slightly more smoothing is applied to reduce tracking of the more high-amplitude high-frequency sinusoids in the 2.5 and 4 rad/s bandwidth signals through the feedforward response, see also Fig. 8(c). The general way in which participants use the available preview for control is, however, not affected by the target signal bandwidth: the target response gain [K f ≈ 0.95, Fig. 10(d)] and look-ahead time [τ f ≈ 1.15 s, Fig. 10(e)] are approximately invariant. The estimated control dynamics in Fig. 12 show that the target trajectory is tracked almost perfectly at all frequencies below 4 rad/s, mostly because the phase lead due to τ f allows for synchronizing the CE output with the target signal (as opposed to pursuit tasks, see Fig. 11, bottom right). Therefore, different-bandwidth target signals provide no incentive for HCs to strongly adapt their control behavior in preview tasks. (Figure presented).
One of the most widely applied identification methods for stall modeling using flight test data is based on Kirchhoff’s method of flow separation. However, this approach has not lead to a satisfactory aerodynamic pitching moment model. The introduction of the so-called X-variable, representing the point of flow separation on the wing, interferes with identification of a pitch damping term, that is required for dynamic stability. In general, Kirchhoff methods lead to models that are incompatible with nominal flight envelope models. This paper presents a nonlinear unsteady model of the pitching moment using lag states of the angle of attack measurements, identified from flight test data collected with a Cessna Citation II laboratory aircraft. The model is formulated in terms of well-known stability derivatives and is a one-on-one extension of the nominal envelope model. Model regressors are selected from a large pool of candidates using Multivariate Orthogonal Function Modeling. The candidate pool is based on a newly formulated mathematical model, such that each model contribution has a clear physical interpretation. The model has good predictive abilities and results in a reduction of 55.9% in validation MSE compared to Kirchhoff based pitching moment models.
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One of the most widely applied identification methods for stall modeling using flight test data is based on Kirchhoff’s method of flow separation. However, this approach has not lead to a satisfactory aerodynamic pitching moment model. The introduction of the so-called X-variable, representing the point of flow separation on the wing, interferes with identification of a pitch damping term, that is required for dynamic stability. In general, Kirchhoff methods lead to models that are incompatible with nominal flight envelope models. This paper presents a nonlinear unsteady model of the pitching moment using lag states of the angle of attack measurements, identified from flight test data collected with a Cessna Citation II laboratory aircraft. The model is formulated in terms of well-known stability derivatives and is a one-on-one extension of the nominal envelope model. Model regressors are selected from a large pool of candidates using Multivariate Orthogonal Function Modeling. The candidate pool is based on a newly formulated mathematical model, such that each model contribution has a clear physical interpretation. The model has good predictive abilities and results in a reduction of 55.9% in validation MSE compared to Kirchhoff based pitching moment models.
Objective
This study investigates the impact of whole-body vibrations caused by external vehicle perturbations, such as aircraft turbulence, on the perception of electrovibration displayed on touchscreens.
Background
Electrovibration is a promising technology for providing tactile feedback on future touchscreens, potentially addressing usability challenges in vehicle cockpits. However, its performance under dynamic conditions, such as whole-body vibrations caused by turbulence, remains largely unexplored.
Method
We measured the absolute detection thresholds of 24 human participants for short (0.2 s) and long (0.5 s) duration electrovibration stimuli displayed on a touchscreen. These measurements were taken in the absence and presence of two types of turbulence motion (Gaussian and Multisine) generated by a motion simulator. Concurrently, we recorded participants’ applied contact force and finger displacements.
Results
Electrovibration stimuli displayed on vehicle cockpit touchscreens were more reliably perceived with a 0.5-s duration than a 0.2-s duration, both in the presence and absence of turbulence. Both turbulence types led to increased vibration-induced finger displacements and scan speeds in the direction of turbulence, as well as higher applied forces and force fluctuation rates. Gaussian turbulence significantly elevated perception thresholds, but only for short-duration electrovibration stimuli.
Conclusion
The findings indicate that whole-body vibrations impair the perception of short-duration electrovibration stimuli, primarily due to unintentional finger movements and increased fluctuations in applied normal force.
Application
Our findings offer valuable insights for the future design of touchscreens with tactile feedback in vehicle cockpits. ...
This study investigates the impact of whole-body vibrations caused by external vehicle perturbations, such as aircraft turbulence, on the perception of electrovibration displayed on touchscreens.
Background
Electrovibration is a promising technology for providing tactile feedback on future touchscreens, potentially addressing usability challenges in vehicle cockpits. However, its performance under dynamic conditions, such as whole-body vibrations caused by turbulence, remains largely unexplored.
Method
We measured the absolute detection thresholds of 24 human participants for short (0.2 s) and long (0.5 s) duration electrovibration stimuli displayed on a touchscreen. These measurements were taken in the absence and presence of two types of turbulence motion (Gaussian and Multisine) generated by a motion simulator. Concurrently, we recorded participants’ applied contact force and finger displacements.
Results
Electrovibration stimuli displayed on vehicle cockpit touchscreens were more reliably perceived with a 0.5-s duration than a 0.2-s duration, both in the presence and absence of turbulence. Both turbulence types led to increased vibration-induced finger displacements and scan speeds in the direction of turbulence, as well as higher applied forces and force fluctuation rates. Gaussian turbulence significantly elevated perception thresholds, but only for short-duration electrovibration stimuli.
Conclusion
The findings indicate that whole-body vibrations impair the perception of short-duration electrovibration stimuli, primarily due to unintentional finger movements and increased fluctuations in applied normal force.
Application
Our findings offer valuable insights for the future design of touchscreens with tactile feedback in vehicle cockpits. ...
Objective
This study investigates the impact of whole-body vibrations caused by external vehicle perturbations, such as aircraft turbulence, on the perception of electrovibration displayed on touchscreens.
Background
Electrovibration is a promising technology for providing tactile feedback on future touchscreens, potentially addressing usability challenges in vehicle cockpits. However, its performance under dynamic conditions, such as whole-body vibrations caused by turbulence, remains largely unexplored.
Method
We measured the absolute detection thresholds of 24 human participants for short (0.2 s) and long (0.5 s) duration electrovibration stimuli displayed on a touchscreen. These measurements were taken in the absence and presence of two types of turbulence motion (Gaussian and Multisine) generated by a motion simulator. Concurrently, we recorded participants’ applied contact force and finger displacements.
Results
Electrovibration stimuli displayed on vehicle cockpit touchscreens were more reliably perceived with a 0.5-s duration than a 0.2-s duration, both in the presence and absence of turbulence. Both turbulence types led to increased vibration-induced finger displacements and scan speeds in the direction of turbulence, as well as higher applied forces and force fluctuation rates. Gaussian turbulence significantly elevated perception thresholds, but only for short-duration electrovibration stimuli.
Conclusion
The findings indicate that whole-body vibrations impair the perception of short-duration electrovibration stimuli, primarily due to unintentional finger movements and increased fluctuations in applied normal force.
Application
Our findings offer valuable insights for the future design of touchscreens with tactile feedback in vehicle cockpits.
This study investigates the impact of whole-body vibrations caused by external vehicle perturbations, such as aircraft turbulence, on the perception of electrovibration displayed on touchscreens.
Background
Electrovibration is a promising technology for providing tactile feedback on future touchscreens, potentially addressing usability challenges in vehicle cockpits. However, its performance under dynamic conditions, such as whole-body vibrations caused by turbulence, remains largely unexplored.
Method
We measured the absolute detection thresholds of 24 human participants for short (0.2 s) and long (0.5 s) duration electrovibration stimuli displayed on a touchscreen. These measurements were taken in the absence and presence of two types of turbulence motion (Gaussian and Multisine) generated by a motion simulator. Concurrently, we recorded participants’ applied contact force and finger displacements.
Results
Electrovibration stimuli displayed on vehicle cockpit touchscreens were more reliably perceived with a 0.5-s duration than a 0.2-s duration, both in the presence and absence of turbulence. Both turbulence types led to increased vibration-induced finger displacements and scan speeds in the direction of turbulence, as well as higher applied forces and force fluctuation rates. Gaussian turbulence significantly elevated perception thresholds, but only for short-duration electrovibration stimuli.
Conclusion
The findings indicate that whole-body vibrations impair the perception of short-duration electrovibration stimuli, primarily due to unintentional finger movements and increased fluctuations in applied normal force.
Application
Our findings offer valuable insights for the future design of touchscreens with tactile feedback in vehicle cockpits.