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.@en

This paper presents a three-step validation approach for subjective rating predictions of driving simulator motion incongruences based on objective mismatches between reference vehicle and simulator motion. This approach relies on using high-resolution rating predictions of open-loop driving (participants being driven) for ratings of motion in closed-loop driving (participants driving themselves). A driving simulator experiment in an urban scenario is described, of which the rating data of 36 participants was recorded and analyzed. In the experiment's first phase, participants actively drove themselves (i.e., closed-loop). By recording the drives of the participants and playing these back to themselves (open-loop) in the second phase, participants experienced the same motion in both phases. Participants rated the motion after each maneuver and at the end of each drive. In the third phase they again drove open-loop, but rated the motion continuously, only possible in open-loop driving. Results show that a rating model, acquired through a different experiment, can well predict the measured continuous ratings. Second, the maximum of the measured continuous ratings correlates to both the maneuver-based (ρ =0.94) and overall (ρ =0.69) ratings, allowing for predictions of both rating types based on the continuous rating model. Third, using Bayesian statistics it is then shown that both the maneuver-based and overall ratings between the closed-loop and open-loop drives are equivalent. This allows for predictions of maneuver-based and overall ratings using the high-resolution continuous rating models. These predictions can be used as an accurate trade-off method of motion cueing settings of future closed-loop driving simulator experiments.

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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.@en

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.@en

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.@en

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.@en

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.@en

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.@en

Users of automated vehicles will engage in other activities and take their eyes off the road, making them prone to motion sickness. To resolve this, the current paper validates models predicting sickness in response to motion and visual conditions. We validate published models of vestibular and visual sensory integration that have been used for predicting motion sickness through sensory conflict. We use naturalistic driving data and laboratory motion (and vection) paradigms, such as sinusoidal translation and rotation at different frequencies, Earth-Vertical Axis Rotation, Off-Vertical Axis Rotation, Centrifugation, Somatogravic Illusion, and Pseudo-Coriolis, to evaluate different models for both motion perception and motion sickness. We investigate the effects of visual motion perception in terms of rotational velocity (visual flow) and verticality. According to our findings, the SVC_I model, a 6DOF model based on the Subjective Vertical Conflict (SVC) theory, with visual rotational velocity input is effective at estimating motion sickness. However, it does not correctly replicate motion perception in paradigms such as roll-tilt perception during centrifuge, pitch perception during somatogravic illusion, and pitch perception during pseudo-Coriolis motions. On the other hand, the Multi-Sensory Observer Model (MSOM) accurately models motion perception in all considered paradigms, but does not effectively capture the frequency sensitivity of motion sickness, and the effects of vision on sickness. For both models (SVC_I and MSOM), the visual perception of rotational velocity strongly affects sickness and perception. Visual verticality perception does not (yet) contribute to sickness prediction, and contributes to perception prediction only for the somatogravic illusion. In conclusion, the SVC_I model with visual rotation velocity feedback is the current preferred option to design vehicle control algorithms for motion sickness reduction, while the MSOM best predicts perception. A unified model that jointly captures perception and motion sickness remains to be developed.

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Touch interfaces are replacing physical buttons, dials, and switches in the new generation of cars, aircraft, and vessels. However, vehicle vibrations and accelerations perturb finger movements and cause erroneous touchscreen inputs by users. Furthermore, unlike physical buttons, touchscreens cannot be operated by touch alone and always require users' visual focus. Hence, despite their numerous benefits, touchscreens are not inherently suited for use in vehicles, which results in an increased risk of accidents. In a recently awarded research project, titled "Right Touch Right Time: Future In-vehicle Touchscreens (FITS)", we aim to address these problems by developing novel in-vehicle touchscreens that actively predict and correct perturbed finger movements and simulate physical touch interactions with artificial tactile feedback.

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In moving-base driving simulators, the sensation of the inertial car motion provided by the motion system is controlled by the motion cueing algorithm (MCA). Due to the difficulty of reproducing the inertial motion in urban simulations, accurate prediction tools for subjective evaluation of the simulator's inertial motion are required. In this article, an open-loop driving experiment in an urban scenario is discussed, in which 60 participants evaluated the motion cueing through an overall rating and a continuous rating method. Three MCAs were tested that represent different levels of motion cueing quality. It is investigated under which conditions the continuous rating method provides reliable data in urban scenarios through the estimation of Cronbach's alpha and McDonald's omega. Results show that the better the motion cueing is rated, the lower the reliability of that rating data is, and the less the continuous rating and overall rating correlate. This suggests that subjective ratings for motion quality are dominated by (moments of) incongruent motion, while congruent motion is less important. Furthermore, through a forward regression approach, it is shown that participants' rating behavior can be described by a first-order low-pass filtered response to the lateral specific force mismatch (66.0%), as well as a similar response to the longitudinal specific force mismatch (34.0%). By this better understanding of the acquired ratings in urban driving simulations, including their reliability and predictability, incongruences can be more accurately targeted and reduced.

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The collective goal of the driving simulation community should be to share ideas to improve the motion cueing across driving simulators worldwide. Due to the active research and intensive usage of driving simulators over the last decades, knowledge in the field of motion cueing has been gained from experience gathered by the community, or practical experience by performing dedicated experiments. This paper discusses several points of ‘common knowledge’ in designing and evaluating motion cueing, along with their value for driving simulation. The goal of the discussion in this paper is to compare these points of common knowledge to the experiences and ideas gathered at BMW’s driving simulation center in Munich, which was opened in 2021 and hosts a fleet of fourteen driving simulators. Furthermore, we aim to bring across points of interest and outlines for future research that should be of interest to the driving simulation community. With the common goal of improving motion cueing, this contribution thus aims to improve and extend the discussion between those working and researching in the driving simulator industry. @en

This article discusses a long short-term memory (LSTM) recurrent neural network that uses raw time-domain data obtained in compensatory tracking tasks as input features for classifying (the adaptation of) human manual control with single- and double-integrator controlled element dynamics. Data from two different experiments were used to train and validate the LSTM classifier, including investigating effects of several key data preprocessing settings. The model correctly classifies human control behavior (cross-experiment validation accuracy 96%) using short 1.6-s data windows. To achieve this accuracy, it is found crucial to scale/standardize the input feature data and use a combination of input signals that includes the tracking error and human control output. A possible online application of the classifier was tested on data from a third experiment with time-varying and slightly different controlled element dynamics. The results show that the LSTM classification is still successful, which makes it a promising online technique to rapidly detect adaptations in human control behavior.

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Accurate modeling of the unsteady aerodynamics during flow separation is critical for effective pilot stall training in Flight Simulation Training Devices and the development of automatic stall recovery controllers. Kirchhoff’s theory of flow separation has gained popularity due to its relative simplicity and suitability for parameter identification from flight data. The goal of this work is to improve an existing Cessna Citation II dynamic stall model’s fidelity by applying Kirchhoff’s method for each wing surface, separately. The main contribution is the identification of asymmetric flow separation development using the flight-derived roll moment and a roll moment model based on the differential flow separation between the wing surfaces. The longitudinal model structures are adopted from the existing, validated baseline stall model. The lateral-directional model outputs are in good agreement with the validation flight data, showing an average reduction of 48% in Mean Squared Error (MSE) compared to the baseline stall model. In contrast, the longitudinal model output results in an average MSE increase of 88%, suggesting that the estimated asymmetric flow separation parameters are unsuitable for longitudinal stall modeling. Hence, a hybrid approach is proposed that combines separate sets of flow separation parameters for the longitudinal and lateral-directional models. @en

Current capabilities for predicting skill retention, i.e., the extent to which human operators retain learned skills over time, at an individual level are limited due to a requirement for large data sets and methods that can extract relevant patterns in highly dimensional data. This paper investigates the application of Extreme Gradient Boosting (XGBoost) decision tree models for predicting a high-resolution individual skill retention curve. For this, a large skill-based tracking experiment dataset is used to extract different feature classes and train an XGBoost predictive model. To identify the robust predictors, the effects of the different features on the model's output are analyzed using SHapley Additive exPlanations (SHAP). Furthermore, the proposed XGBoost model is trained using both the experiment dataset and a matched synthetic dataset, with both approaches evaluated on the experiment data. Overall, the available experiment dataset was found to include too few retention measurements, and too significant between-group differences, to extract a reliable prediction model. On the synthetic dataset, the XGBoost model was found to accurately capture individuals' skill retention curves, where the features that contributed most (21%) to the prediction model's accuracy were found to be the considered learning curve parameters. Overall, this paper shows that experiment data of skill-based tracking tasks can be used to predict skill decay curves using XGBoost, but that more research and data are needed to achieve sufficient accuracy and reliability at an individual level for practical applications.@en

Due to the non-deterministic nature of longitudinal human driver behaviour, motion cueing algorithms currently cannot fully utilize the workspace of driving simulators. This paper explores the possibility of using various predictor variables to predict longitudinal driving behaviour. Through the development of a logistic regression model, it is shown that a combination of the current vehicle velocity, the speed limit eight seconds ahead and the accelerator pedal deflection yields the most accurate estimate of the probabilities that drivers will accelerate or decelerate. Based on these probabilities, a driving simulator was linearly pre-positioned in combination with a classical washout algorithm. The perceived motion incongruence was subjectively evaluated by the drivers (N = 34), testing: (i) no pre-positioning, (ii) pre-positioning, and (iii) pre-positioning with an increased longitudinal classical washout gain enabled by the pre-positioning. Results show that the pre-positioning improves the margins with respect to the longitudinal workspace limits (better workspace management), without affecting the motion incongruence ratings. When using the increased margins to increase the longitudinal gain, however, no significant reduction in motion incongruence ratings was observed. This is likely due to the small motion space of the hexapod motion system used in the current study. However, this paper shows that longitudinal driving behaviour can be accurately predicted and can enable improved workspace utilization for driving simulators. @en

In the design of human-like steering support systems, driver models are essential for matching the supporting automation's behavior to that of the human driver. However, current driver models are very limited in capturing the driver's adaptation to key task variables such as road width and visibility (i.e., 'preview' of the road ahead). This paper uses a recently proposed, novel control-theoretical model for centerline tracking to investigate driver steering in lane-keeping tasks with restricted and unrestricted preview, in an attempt to substantially extend this model's validity. Using data from a tailored driving simulator experiment, three driver control loops (feedforward, heading and position feedback) are separately quantified using system identification techniques. The results show that when preview is restricted, drivers use all of the remaining preview to anticipate the curves of the road ahead, and are no longer able to 'smooth' tight curves in the road trajectory (i.e., corner cutting). When sufficient preview and lane width are available, the time to line crossing increases, and steering behavior is less aggressive and more intermittent, or more 'satisficing'. The novel driver steering model captures these adaptations very well (over 95% of the steering actions) and can thereby be instrumental in realizing human-like steering automation and support systems.

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This paper analyzes the effects of the helicopter dynamics on pilots' learning process and transfer of learned skills during autorotation training. A quasi-transfer-of-training experiment was performed with 10 experienced helicopter pilots in the SIMONA moving-base flight simulator at Delft University of Technology. Pilots had to control an in-house flight dynamics model setup to simulate two types of helicopter dynamics: (1) a "hard"dynamics characterized by a low autorotative flare index requiring high pilot control compensation and (2) a "easy"dynamics characterized by a high autorotative flare index with low pilot control compensation required. Two groups of pilots tested these types of dynamics in a different training sequence: hard-easy-hard (HEH group) and easy-hard-easy (EHE group). The main conclusion of this study proved that simulator training for autorotation can best start with pilots training in the most resource demanding condition. A more challenging helicopter's dynamics will require higher pilot agility and more rapid responses to his/her perceptual changes. This will result in pilots developing more robust and adaptable flying skills. Indeed, a clear positive transfer of training effect was observed in the experiment presented in this paper in terms of acquired pilot skills in the HEH group, but not the EHE group. Positive transfer was especially observed in terms of reduced rate of descent at touchdown. The two groups differed in the control strategy applied, with the HEH group having developed a control technique mimicking more closely the one adopted in a real helicopter.

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The human motion perception system has long been linked to motion sickness through state estimation conflict terms. However, to date, the extent to which available perception models are able to predict motion sickness, or which of the employed perceptual mechanisms are of most relevance to sickness prediction, has not been studied. In this study, the subjective vertical model, the multi-sensory observer model and the probabilistic particle filter model were all validated for their ability to predict motion perception and sickness, across a large set of motion paradigms of varying complexity from literature. It was found that even though the models provided a good match for the perception paradigms studied, they could not be made to capture the full range of motion sickness observations. The resolution of the gravito-inertial ambiguity has been identified to require further attention, as key model parameters selected to match perception data did not optimally match motion sickness data. Two additional mechanisms that may enable better future predictive models of sickness have, however, been identified. Firstly, active estimation of the magnitude of gravity appears to be instrumental for predicting motion sickness induced by vertical accelerations. Secondly, the model analysis showed that the influence of the semicircular canals on the somatogravic effect may explain the differences in the dynamics observed for motion sickness induced by vertical and horizontal plane accelerations.

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In driving simulation, the choice of a simulator, motion cueing algorithm, and associated set of tuning parameters for an experiment is typically made with an exclusive focus on the quality of the motion. In practice, many other metrics could affect this choice as well, such as tuning complexity, algorithm stability, or the financial costs of the simulation. Arguably, the complete motion cueing algorithm quality is thus more than the quality of the motion alone. This paper presents results of a survey which attempted to identify the most important metrics from the perspective of the main experiment stakeholders. Four stakeholder groups in typical driving simulator experi- ments are defined: The experimenters, motion cueing engineers, operators, and participants. All groups received the same survey, asking them to indicate how important various metrics are for them. Results show that, next to the quality of the motion, experimenters and participants are generally interested in reducing simulator sickness. The motion cueing engineers rank tuning effort and tuning complexity as most important metrics. Operators prefer an easy to use and overall stable motion cueing. A typical BMW experiment is discussed as example, which shows that the choice for a simulator and motion cueing algorithm can indeed differ when including these metrics in a trade-off, compared to when only motion quality is considered. The presented methods allow for a better, multi- faceted selection of the simulator, motion cueing algorithm, and associated tuning parameters, improving future driving simulation experiments. @en