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.

The shift from driver to passenger may increase the risk of Motion Sickness (MS) for Automated Vehicle (AV) occupants. Motion anticipation, which is believed to mitigate MS, relies to a large extent on visual cues. Yet, the mechanisms through which visual information influences MS remain poorly understood. This paper investigates the effect of visual translation on MS in AVs. In a simulator study, eighteen participants experienced three ‘AV rides’ with identical repetitive braking and accelerating motion on a straight road, differing only in their ‘out-the-window’ view to manipulate the amount of global optic flow. A rural, low optic flow, ride and an urban, high optic flow, ride were compared to a baseline without visual movement. Results suggest that visual translation in both the central and peripheral view, that is congruent with inertial cues, may only slightly reduce MS, as MS seemed to be slightly less in the rural and urban rides compared to the baseline. The amount of global optic flow seems to have little effect, with minimal differences in MS between the rural and urban rides. Nonetheless, it remains uncertain to what extent the type of visual content has affected MS development. A study using more generic visuals could help isolate these effects, by eliminating any recognizable visual elements, while still manipulating the global optic flow rate.

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.

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.

Air traffic control is advancing digitalization by developing advanced decision-support systems, where the way information is presented to operators plays a central role in shaping performance. However, the effects of different visual representations within these systems on human decision-making remain not fully understood. In this study, we compared two Conflict Detection and Resolution (CD&R) tools: the Highly Interactive Problem Solver (HIPS) and the Solution Space Diagram (SSD). Although both systems are grounded in the same control problem, they differ in how they represent the control constraints that define conflict conditions and feasible responses. Through a human-in-the-loop experiment under low-and high-traffic conditions, we analyzed how these differences influence decision-making. Results showed that, particularly in low-density traffic, HIPS enabled quicker responses, fewer commands, and smaller safety margins, whereas SSD, despite receiving more favorable subjective ratings, led to greater variability in actions. These findings suggest that visualization significantly impacts decision-making consistency and efficiency. However, in highly complex environments, overall effectiveness may depend more on operators’ ability to shift and adapt decision-making patterns facilitated by the interface than on specific visual elements.

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 ($\rho =0.94$) and overall ($\rho =0.69$) 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.

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 thebetterthe motion cueing is rated, thelowerthe 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.

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

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.

Air traffic controller workload is a limiting factor in the current air traffic management system. Adaptive support systems have the potential to balance controller workload and gain acceptance as they provide support during times of need. Challenges in the design of adaptive support systems are to decide when and how to trigger support. The goal of this study is to gain empirical insights into these challenges through a human-in-the-loop experiment, featuring a simplified air traffic control environment in which a novel triggering mechanism uses the quality of the controller's decisions to determine when support is needed. The designed system seeks to prevent high workload conditions by providing resolution advisories when the controller exceeds a threshold of “self-complicating” decisions. Results indicate that the new system is indeed capable of increasing the efficiency and safety compared to full manual control without intervention. More adaptive support, however, increased the frustration of participants, decreased acceptance, and did not result in improved workload ratings. These findings suggest that, unless we can better infer human intent in complex work environments, adaptive support at the level of decision-making is problematic. A potentially more fruitful direction is to provide support at the level of information integration, with full decision-making authority with the human.

Modern aircraft can be equipped with a flight envelope protection system: automation which modifies pilot control inputs to ensure that the aircraft remains within the allowable limits. Overruling the pilot inputs may lead to mode confusion, even when visual or auditory feedback is provided to alert pilots. We advocate using active control devices to make the flight envelope protection system tangible to the pilot. This paper presents the main findings of an evaluation of three haptic feedback designs for flight envelope protection. The first concept used both force feedback and vibro-tactile alerts, producing promising, yet inconclusive, results. The second concept used asymmetric vibrations to give directional alerting cues, which did not result in improved performance on initial use, but which did yield improved learning rate for the task. The third system employed force feedback to physically guide the pilot away from flight envelope limits, which yielded safety improvements from the first use, but created dependence: pilot performance degraded immediately after the force feedback was removed. From this, we advise to use asymmetric vibrations during training for flight envelope excursions, to leverage active control interfaces for providing force feedback during operation, and reevaluate a combination of both to combine their advantages for single-pilot operations.

The 1960s crossover model is widely applied to quantitatively predict a human controller's (HC's) manual control behavior. Unfortunately, the theory captures only compensatory tracking behavior and, as such, a limited range of real-world manual control tasks. This article finalizes recent advances in manual control theory toward more general pursuit and preview tracking tasks. It is quantified how HCs adapt their control behavior to a final crucial task variable: the target trajectory bandwidth. Beneficial adaptation strategies are first explored offline with computer simulations, using an extended crossover model theory for pursuit and preview tracking. The predictions are then verified with data from a human-in-the-loop experiment, in which participants tracked a target trajectory with bandwidths of 1.5, 2.5, and 4 rad/s, using compensatory, as well as pursuit and preview displays. In stark contrast to the crossover regression found in compensatory tasks, humans attenuate only their feedforward response when tracking higher-bandwidth trajectories in pursuit tasks, while their behavior is generally invariant in preview tasks. A full quantitative theory is now available to predict HC manual control behavior in tracking tasks, which includes HC adaptation to all key task variables.

Novel driver support systems potentially enhance road safety by cooperating with the human driver. To optimize the design of emerging steering support systems, a profound understanding of driver steering behavior is required. This article proposes a new theory of driver steering, which unifies visual perception and control models. The theory is derived directly from measured steering data, without any a priori assumptions on driver inputs or control dynamics. Results of a human-in-the-loop simulator experiment are presented, in which drivers tracked the centerline of straight and winding roads. Multiloop frequency response function (FRF) estimates reveal how drivers use visual preview, lateral position feedback, and heading feedback for control. Classical control theory is used to model all three FRF estimates. The model has physically interpretable parameters, which indicate that drivers minimize the bearing angle to an “aim point” (located 0.25-0.75 s ahead) through simple compensatory control, both on straight and winding roads. The resulting unifying perception and control theory provides a new tool for rationalizing driver steering behavior, and for optimizing modern steering support systems.

Limitations of a haptic device can cause distortions of the force feedback it presents. Just-noticeable difference (JND) in system dynamics is important for creating transparent haptic interaction. Based on the previous work, this article presents a unified model that extends the existing JND rule. Our approach projects the JNDs in the mechanical properties of a second-order mass-spring-damper system onto the real and imaginary components of the system's frequency response function (FRF). We discuss the results of two experiments and show that the JNDs obtained for both the real and imaginary components can be expressed as the same fraction of, and thus are proportional to, the magnitude of the total system's FRF. Furthermore, the findings are generalized to cases where the system's dynamics order is different than two. What results is a unified model that accurately describes the threshold for changes in human perception of any linear system dynamics with only two dimensions: the real and imaginary axes in the complex plane.

Humans can rapidly change their low-frequency arm dynamics to resist forces or give way to them. Quantifying driver time-varying arm dynamics is important to develop steer-by-wire and haptic support systems. Conventional linear time-invariant (LTI) identification, and even time-varying techniques such as wavelets, fail to capture fast changing dynamics. Moreover, such techniques require perturbation signals on the steering wheel (SW), which may affect steering feel and control behavior. We propose a novel two-step method to estimate time-varying driver admittance, using unobtrusive grip-force measurements of the hands on the wheel to schedule a linear parameter-varying (LPV) model that captures the full admittance range. A total of 18 subjects participated in two experiments in a simulator with an actuated SW. In a sensorimotor control experiment, we first establish the grip force and admittance relationship, requiring subjects to perform a boundary tracking task where perturbations on the wheel enabled local LTI identification. Six boundary widths is used to evoke admittance changes, after which a global LPV model is obtained through interpolation between the local models. Results show an inverse relationship between grip force and admittance and that the LPV model accurately captures the admittance settings (fit percentage > 90%). Second, a driving experiment is followed that aims to evoke differences in grip force and admittance in response to varying road widths, offering more realistic data to evaluate the LPV model predictions. Results show that the LPV model accurately describes adaptations in admittance to road width. Our method allows for online estimation of time-varying admittance during driving, without applying force perturbations.

In the near future, air traffic controllers are expected to adhere to stringent time and position constraints in controlling traffic. For this new task, new decision-support tools are required which include higher levels of automation, whilst letting humans remain to be the ultimate responsible for the safety of operations. In previous research, an advanced human-machine interface was designed and evaluated that allows controllers to manipulate four-dimensional flight plans of each individual aircraft. In this research, a higher level of automation is explored by designing a new interface prototype that enables controllers to manipulate multiple flows of traffic by facilitating interaction with a path-planning algorithm. A first evaluation of this interface, in which five participants were asked to structure a perturbed airspace as they saw fit, showed that the participants were able to influence the algorithm as they desired and were supported by the interface that visualized the inner workings of the algorithm. However, human influence did not improve the solutions in terms of sector robustness and efficiency, as compared the previously designed interface for aircraft. Therefore, improvements and its use case warrant further research.

Future Air Traffic Management concepts will require air traffic controllers to move from a tactical to a strategic way of operation. This paper evaluates two novel concepts which support controllers to perform four-dimensional trajectory management in a contingency situation. The Travel Space Representation and Time-Space Diagram are both solution-space based displays where automation calculates all possible actions in real-time. All decision-making is still to be done by the operator but is greatly facilitated by this automation, as it shows all possible actions at a glance. An experiment is described which evaluated the performance of novice controllers in managing a sector where suddenly a bad weather cell emerged, requiring them to re-route traffic in space and time. Results show that the display concepts work well and support operators even in complex situations with a heterogeneous mix of aircraft types and speeds. Performance and workload indicators become worse for the higher-density, higher-heterogeneity situations.

Future Air Traffic Management is expected to shift towards four dimensional trajectory (4DT) management, requiring new decision support tools for air traffic controllers to meet stringent time and position constraints. In previous work, a prototype human-machine interface has been developed for 4D trajectory manipulations of single aircraft. This paper describes a tool for multi-aircraft manipulation and investigates its potential control efficiency benefits. A human-in-the-loop experiment (N = 13) has been conducted using scenarios with sector disruptions and varying conflict geometry. Results show that participants preferred to use multi-aircraft manipulation for groups of aircraft having small convergence angles. Since the current implementation involves re-routing all selected aircraft through one common waypoint (referred to as a 'merge point'), extra additional track miles were flown and airspace robustness reduces. Regarding efficiency and safety, multi-aircraft trajectory manipulation seems favourable only for smaller convergence angles, although this also depends on the way the operators place the aircraft merging points. For future development, attention should be devoted to making flight efficiency constraints of each aircraft more salient, enabling controllers to better time the rerouting multiple aircraft and more fairly distribute re-routing costs.

The human controller (HC) in manual control of a dynamical system often follows a visible and predictable reference path (target). The HC can adopt a control strategy combining closed-loop feedback and an open-loop feedforward response. The effects of the target signal waveform shape and the system dynamics on the human feedforward dynamics are still largely unknown, even for common, stable, vehicle-like dynamics. This paper studies the feedforward dynamics through computer model simulations and compares these to system identification results from human-in-the-loop experimental data. Two target waveform shapes are considered, constant velocity ramp segments and constant acceleration parabola segments. Furthermore, three representative vehicle-like system dynamics are considered: 1) a single integrator (SI); 2) a second-order system; and 3) a double integrator. The analyses show that the HC utilizes a combined feedforward/feedback control strategy for all dynamics with the parabola target, and for the SI and second-order system with the ramp target. The feedforward model parameters are, however, very different between the two target waveform shapes, illustrating the adaptability of the HC to task variables. Moreover, strong evidence of anticipatory control behavior in the HC is found for the parabola target signal. The HC anticipates the future course of the parabola target signal given extensive practice, reflected by negative feedforward time delay estimates.

Vehicle control tasks require simultaneous control of multiple degrees-of-freedom. Most multi-axis human-control modeling is limited to the modeling of multiple fully independent single axes. This paper contributes to the understanding of multi-axis control behavior and draws a more realistic and complete picture of dual-axis manual control. A human-in-the-loop experiment was performed to study four distinctive phenomena that can occur in multi-axis control: performance degradation, axis asymmetry, crossfeed, and intermittency. In a simulator, three conditions were tested in the presence and absence of physical motion: the full dual-axis control task, single-axis roll task, and single-axis pitch task. Controlled element dynamics, stick dynamics, and forcing functions were equal in all cases. Results show that performance is worse in dual-axis tasks. Performance in roll axis is consistently worse than pitch, thereby proving axis asymmetry. Physical motion improves the performance and stability of the system. The application of independent forcing function signals in both controlled axes resulted in the detection of crossfeed in dual-axis tasks from spectral analysis. Using a novel extended Fourier coefficient method, the identified crossfeed dynamics can explain up to 20% of the measured control inputs and improves modeling accuracy by up to 5%. Dual-axis control behavior is less accurately modeled with linear time-invariant models and is more intermittent.

In many work domains, the push toward higher levels of automation raises the concern of diminishing human expertise. Ecological interfaces could help operators in retaining and potentially even in acquiring expertise as they are hypothesized to lead to a deeper understanding of the work domain. This study explores the short-term impact of ecological interfaces on knowledge development and compares the results with an instruction-based training method. To monitor and compare students' progress, their decision-making strategies, identified from verbal comments recorded in “think-aloud” simulator sessions, are mapped onto the decision ladder. This method has been applied to an experiment (N = 16) aimed at training novices in conflict detection and resolution (CD&R) within a simplified air traffic control context. Results show that the overall CD&R performance in the final measurement sessions, featuring a transfer manipulation, was not significantly different between the “ecological” and “instructional” groups. In terms of cognitive behavior, however, students in the ecological group exhibited more laborious rule and knowledge-based behaviors that sparked goal-oriented thoughts and corresponding control performances beyond the CD&R task. These findings indicate that ecological interfaces can change how people think and approach a control problem, even after removing the support. It is therefore reasonable to believe that ecological interfaces can play an important role in the early stages of deep knowledge development.

Time delays in haptic teleoperation affect the ability of human operators to assess mechanical properties (damping, mass, and stiffness) of the remote environment. To address this, we propose a unified framework for human haptic perception of the mechanical properties of environments with delayed force feedback. In a first experiment, we found that the delay in the force feedback led our subjects to underestimate all the three mechanical properties. Moreover, subjects perceived additional damping or stiffness properties that the environment did not possess. It was found that the extents of these changes in the perception depend on both time-delay magnitude and the frequency of the movement with which subjects interacted with the environment. This was due to the fact that subjects were not able to distinguish the delay-caused phase shift in the movement-force relation from changes in the three mechanical properties. Based on this, we proposed a framework that allowed for a prediction of the change associated with delayed force in perception of mass-spring-damper environments. The framework was corroborated by a second experiment, in which a combined mass-damper environment was tested. Our hypotheses that the delay would cause subjects to underestimate the mass but overestimate the damping and that the extents of the under- A nd overestimation would differ between individual subjects due to the difference in the interaction frequency were confirmed.

Just notable difference (JND) thresholds for the perception of manipulator dynamic properties are relevant for tele-operation and simulation of vehicles. Manipulator dynamic properties are characterized by multiple variables (describing mass, spring and damping for a linear manipulator) and the JND threshold for any of these variables is affected by variation in the remaining variables. In previous work, we demonstrated and modeled the coupling of the stiffness JND and the mass JND, and investigated the effects of stiffness and mass properties on the damping JND. In this work we investigate how changes in the damping parameter affect the JND in perceiving stiffness and mass. In an experiment our subjects were instructed to discriminate between different levels of manipulator's stiffness or mass, while tracking a prescribed sinusoidal manipulator movement. Results show that the JND in spring force and the JND in inertia force are identical, and increase for higher damping levels. The JND model developed in our previous work can successfully describe the experimental observations, thereby providing an extension of Weber's law. The impedance of the manipulator is considered as the reference stimulus in the frequency domain, so that a single ratio describes the JND thresholds for all three properties.

Wave-variable transformation is a means to maintain stability of haptic teleoperation in the presence of communication time delays. Its drawback is that it affects haptic perception of remote properties and thereby degrades transparency. This paper studies the effect of wave-variable transformation on human haptic perception. Based on a framework of haptic perception developed in previous work, we systematically investigated how the wave variable affects human perception of damping, mass and stiffness properties of an arbitrary linear environment. Both the original wave-variable approach and the generalized wave-variable approach are investigated. Results show how both approaches change human perception of all three mechanical properties of the environment, and how these changes vary with both excitation frequency and time delay. The generalized wave-variable approach on the whole outperforms the original in terms of rendering mass and stiffness, but not always for rendering damping. Results also show that human perception of the dynamics rendered by both approaches is similar to that of the original environment only when time delays are small. As the time delay increases, evaluating the mechanical properties can become very difficult for a human operator if the interaction with the environment is not static.

Perspective flight-path displays are a viable alternative for the aircraft primary flight display, but increases the pilot head-down time. A haptic interface is developed to counter this effect and increase the task-sharing performance during approach. An experiment (n=12) was conducted to test the effects of the haptic design on primary task performance with a tunnel-in-the-sky display, in a low and high workload condition. To investigate the effects of the haptic interface on the headdown time, a secondary task was presented on the simulator outside visual, in the form of bucket-shaped figures, requiring participants to indicate the direction of the one divergent figure. Secondary task performance was measured by success rate, average time to answer correctly and – by means of eye-tracker measurements – head-up time and number of gaze switches. Pilots also provided a subjective measure of their mental effort after each run. Results show that haptic feedback significantly increases both primary and secondary task performance of the pilots, especially when the primary task is more challenging. Workload ratings are significantly lower, and head-up time increases with haptic feedback.

Novel (semi-)automated systems are rapidly being introduced into modern road vehicles, but anticipating possibly critical human-machine interaction issues is difficult, because the human driver's behavior is as of yet still poorly understood. This paper aims to improve our understanding and models of driver steering behavior on winding roads, using Frequency-Response Function (FRF) measurements of drivers' feedforward, heading feedback, and lateral position feedback dynamics. The steering behavior data were collected in a human-in-the-loop simulator experiment, in which drivers followed the road centerline at constant forward velocity, while being perturbed laterally by wind-gust disturbances. All three measured FRFs can be captured with a multiloop, single preview-point driver model, which has only five parameters. These parameters provide unmatched understanding of - otherwise lumped - driver internal steering processes, quantifying how and what portion of the previewed centerline trajectory is used for control, and how lateral position and heading feedback are weighed. The gained insights may help to reduce driver-automation interaction issues in modern road vehicles, to quantify between-driver steering variations, adaptation and learning, and to design human-like and individualized automatic and shared steering controllers.

Understanding speed control in driving is important for analysis of road geometry and for the development of driver support assistance devices. Current models for speed selection are primarily based on the relation between road geometry and observed speeds. This study proposes a more detailed model that relates individual speed control to accelerator and brake pedal, based on perception of the visual scene as captured by the Extended Tangent Point (ETP). We investigated the potential of the the Time to ETP (TETP) as input for accelerator and brake pedal control. Based on observations from driving studies, we propose a model, and tuning rules to adjust the model parameters to observed behavior. A simulator experiment showed that, after individualization of the thresholds using a binary classification method, the model is capable of accurately capturing individual speed adaptation of 15 drivers on single lane roads with multiple curves.

Haptic interfaces have been developed to assist human operators controlling unmanned aerial vehicles (UAV) beyond line-of-sight. These systems complement the predominantly camera-based visual interfaces and act like a collision avoidance system: when nearing obstacles, the operator gets re-directed by the haptic force feedback. Previous research showed that, although being successful in reducing the number of collisions, these haptic interfaces also lead to lower user acceptance, as the operators do not always understand the system's intentions. This paper discusses two novel visualizations which were developed to increase operator acceptance. Both designs were evaluated in a human-in-the-loop experiment (n = 12). Results from acceptance-related questionnaires show that our subjects preferred teleoperating the UAV with the visualizations active. Acceptance ratings were higher for the same levels of safety, performance and operator workload.

In manual tracking tasks with preview of the target trajectory, humans have been modeled as dual-mode "near" and "far" viewpoint controllers. This paper investigates the physical basis of these two control mechanisms, and studies whether estimated viewpoint positions represent those parts of the previewed trajectory which humans use for control. A combination of human gaze and control data is obtained, through an experiment which compared tracking with full preview (1.5 s), occluded preview, and no preview. System identification is applied to estimate the two look-ahead time parameters of a two-viewpoint preview model. Results show that humans focus their gaze often around the model's near-viewpoint position, and seldom at the far viewpoint. Gaze measurements may augment control data for the online identification of preview control behavior, to improve personalized monitoring or shared-control systems in vehicles.

Traditional Frequency Response Function (FRF) estimation techniques used for analysis of Human Controller (HC) dynamics in tracking tasks assume HC dynamics to be linear, but generally do not quantify or compensate for the effects of human nonlinearities. The robust and fast Best Linear Approximation (BLA) techniques for estimating an FRF do provide such quantification of nonlinear distortions caused by Period-In-Same-Period-Out (PISPO) nonlinearities and can reduce the effect of PISPO nonlinear operations on the FRF estimate. This paper investigates the application of these BLA techniques to both measured and simulated HC data. For the simulated data, a linear HC model was deliberately extended with a symmetric PISPO deadzone nonlinear operator and a realistic level of HC "remnant" noise. Overall, both the measured and the simulated data indicate that due to the high levels of remnant noise inherent to HC data, no consistent estimate of PISPO nonlinear contributions could be made. This also means that the improvement of using BLA techniques and averaging over multiple forcing function realizations does not result in a substantial improvement over the current practice of estimating HC FRFs from repeated measurements of a single forcing function.

This paper investigates the effects of simulator motion on driver steering performance, and how this depends on the available visual information and external disturbances such as wind gusts. A human-in-the-loop driving experiment was performed in which twelve participants steered a fixedvelocity car to follow a winding road (target tracking, TT) while suppressing side-wind gusts (disturbance-rejection, DR). Driver performance with and without motion feedback is compared in six tasks: "regular" lane-keeping with optic flow, centerline tracking with optic flow, and centerline tracking without optic flow, all with both 5 and 100 m of preview. Performance is calculated in the frequency domain to separate TT and DR contributions. The results show that motion feedback always yields improved DR performance, but the actual improvement depends strongly on the available simulator visuals. TT performance is generally unaffected by motion feedback, except when preview is limited. We conclude that simulator motion is required to evoke realistic driver performance in tasks where substantial external disturbances are present, but not in disturbance-free tasks where a winding road is being followed.

Realistic manual control tasks typically involve predictable target signals and random disturbances. The human controller (HC) is hypothesized to use a feedforward control strategy for target-following, in addition to feedback control for disturbance-rejection. Little is known about human feedforward control, partly because common system identification methods have difficulty in identifying whether, and (if so) how, the HC applies a feedforward strategy. In this paper, an identification procedure is presented that aims at an objective model selection for identifying the human feedforward response, using linear time-invariant autoregressive with exogenous input models. A new model selection criterion is proposed to decide on the model order (number of parameters) and the presence of feedforward in addition to feedback. For a range of typical control tasks, it is shown by means of Monte Carlo computer simulations that the classical Bayesian information criterion (BIC) leads to selecting models that contain a feedforward path from data generated by a pure feedback model: "false-positive" feedforward detection. To eliminate these false-positives, the modified BIC includes an additional penalty on model complexity. The appropriate weighting is found through computer simulations with a hypothesized HC model prior to performing a tracking experiment. Experimental human-in-the-loop data will be considered in future work. With appropriate weighting, the method correctly identifies the HC dynamics in a wide range of control tasks, without false-positive results.

This paper investigates how humans use a previewed target trajectory for control in tracking tasks with various controlled element dynamics. The human's hypothesized "near" and "far" control mechanisms are first analyzed offline in simulations with a quasi-linear model. Second, human control behavior is quantified by fitting the same model to measurements from a human-in-the-loop experiment, where subjects tracked identical target trajectories with a pursuit and a preview display, each with gain, single-, and double-integrator controlled element dynamics. Results show that target-tracking performance improves with preview, primarily due to the far-viewpoint response, which allows humans to cancel their own and the controlled element's lags, without additional control activity. The near-viewpoint response yields better target tracking at higher frequencies, but requires substantially more control activity. The control-theoretic approach adopted in this paper provides unique quantitative insights into human use of preview, which can help to explain human behavior observed in other preview control tasks, like driving.

Motion cueing algorithms are used in motion simulation to map the inertial vehicle motion onto the limited simulator motion space. This mapping causes mismatches between the unrestricted visual motion and the constrained inertial motion, which results in perceived motion incongruence (PMI). It is still largely unknown what exactly causes visual and inertial motion in a simulator to be perceived as incongruent. Current methods for measuring motion incongruence during motion simulation result in time-invariant measures of the overall incongruence, which makes it difficult to determine the relevance of the individual and short-duration mismatches between visual and inertial motion cues. In this paper, a novel method is presented to subjectively measure the time-varying PMI continuously throughout a simulation. The method is analyzed for reliability and validity of its measurements, as well as for its applicability in relating physical short-duration cueing errors to PMI. The analysis shows that the method is reliable and that the results can be used to obtain a deeper insight into the formation of motion incongruence during driving simulation.

Due to linear perspective, the visual stimulus provided by a previewed reference trajectory reduces with increasing distance ahead. This paper investigates the effects of linear perspective on human use of preview in manual control tasks. Results of a human-in-the-loop tracking experiment are presented, where the linear perspective's horizontal and vertical deformations along the previewed trajectory were applied separately and combined, or were absent (plan-view task). Measurements are analyzed with both nonparametric and parametric system identification techniques, in combination with a quasi-linear human controller model for plan-view preview tracking tasks. Results show that reduced visual stimuli in perspective tasks evoke less aggressive control behavior, but that the human's underlying control mechanisms are still accurately captured by the model. We conclude that human controllers use preview information similar in plan-view and perspective tasks.

In manual control tasks, preview of the target trajectory ahead is often limited by poor lighting, objects, or display edges. This paper investigates the effects of limited preview, or preview time, in manual tracking tasks with single- and double-integrator controlled element dynamics. A quasi-linear human controller model is used to predict the human behavior adaptations offline, by finding the model parameters that yield optimal performance at each preview time. These predictions are then verified by fitting the same model to measurements from a human-in-the-loop experiment, where subjects performed a tracking task with eight different preview time settings between 0 and 2 s. Results show that the tracking performance improves and the model's “look-ahead” time parameters increase with increasing preview time. Beyond a certain preview time, approximately 0.6 s and 1.15 s in single- and double-integrator tasks, respectively, additional preview evokes no further adaptations. The offline model predictions closely match the experimental results, which thereby promises to facilitate similar quantitative insights in other tasks with restricted preview.

We discuss an extension of the basic principles underlying the human haptic just noticeable difference (JND) in perceiving a manipulator's mechanical properties from force feedback. Two cases are studied: first, the JND in perceiving the stiffness of manipulators with various masses and, second, the JND in perceiving the damping of a combined mass-spring-damper system with varying stiffness and mass. The extended JND laws are obtained through mapping psychophysical findings to JND formulations based on frequency response functions. We first present two human-factor experiments in which subjects discriminated between different levels of manipulator stiffness/damping while moving the manipulator with a prescribed sinusoidal deflection. For the two testing cases, both JNDs violate Weber's law: Due to the increases in mass, the normalized stiffness JND (the Weber fraction) decreases as the reference stiffness level increases; the damping JND for a constant reference damping increases with higher combined responses of stiffness and mass. On the basis of weighting the frequency response magnitude of mechanical properties, we performed model identification that fit the experimental observations and extended the JND laws for the two testing cases. Our extended JND laws indicate that, first, the stiffness and mass affect the stiffness JND in the same way, the stiffness JND is a fixed proportion of the combined frequency response of stiffness and mass, and, second, the frequency response magnitude of the damping JND is a fixed proportion of the frequency response magnitude of the combined system (the mass-spring-damper system).

Manual control cybernetics aims to understand and describe how humans control vehicles and devices using mathematical models of human control dynamics. This “cybernetic approach” enables objective and quantitative comparisons of human behavior, and allows a systematic optimization of human control interfaces and training associated with manual control. Current cybernetics theory is primarily based on technology and analysis methods formalized in the 1960s and has shown to be limited in its capability to capture the full breadth of human cognition and control. This paper reviews the current state-of-the-art in our knowledge of human manual control, points out the main fundamental limitations in cybernetics, and proposes a possible roadmap to advance the theory and its applications. Central in this roadmap will be a shift from the current linear time-invariant modeling approach that is only truly valid for human behavior under tightly controlled and stationary conditions, to methods that facilitate the analysis of adaptive, and possibly time-varying, human behavior in realistic control tasks. Examples of key current developments in the field of cybernetics-human use of preview, predictable discrete maneuvering, skill acquisition and training, time-varying human modeling, and neuromuscular system modeling-that contribute to this shift are presented in this paper. The new foundations for cybernetics that will emerge from these efforts will impact all domains that involve humans in manual and semiautomatic control.

Ecological interface design (EID) was originally developed in the context of process control but has been extended into many domains where technology has resulted in both changing work demands and increased opportunities for improved interface applications. This paper gives an overview of the application of the EID to the control of vehicle locomotion, either from within the vehicle, as a driver or a pilot, or from the outside, as an operator or a (air traffic) controller. It discusses lessons learned from the application of the EID for the vehicle locomotion control task and focuses on how the methodology can be applied to this domain. Specific issues identified are that the planning and control of a vehicle simultaneously spans multiple time scales and that the interface must be designed considering the format in which the control input is defined. Also, due to the extensive standardization of the instrumentation and training certification, changes introduced by the new displays must initially be additional to the existing displays. Chosen representations must also be shown in a format that matches the current instrumentation and the directly observable outside world.

A control strategy is proposed to deal with the fundamental gain selection problem of optical flow landings. It involves detecting the height by means of an oscillating movement and setting the control gains accordingly at the start of a landing. Then, during descent, the gains are reduced exponentially, with mechanisms in place to ensure high-performance landings. Real-world experiments with a quadrotor demonstrate successful landings in both indoor and outdoor environments.

A large variation of the haptic Just Noticeable difference (JND) in stiffness is found in literature. But no underlying model that explains this variation was found, limiting the practical use of the stiffness JND in the evaluation work of control loading system (CLS). To this end, we investigated the cause of this variation from humans' strategy for stiffness discrimination, by two experiments in which a configurable manipulator was used to generate an elastic force proportional to its angular displacement (deflection). In a first experiment, the stiffness JND was measured for three stiffness levels, and an invariant Weber fraction was obtained. We found that for stiffness discrimination, subjects reproduced the same amount of the manipulator deflection and used the difference in the terminal forces as the indication of the stiffness difference. We demonstrated that the stiffness Weber fraction and the force Weber fraction could be related by a systematic bias in the deflection reproduction, which was caused by the difference in the manipulator stiffness. A second experiment with two conditions was done to verify this model. In one condition, we measured the stiffness JND while asking subjects to move the manipulator to a target angular displacement. Thus the bias in the deflection reproduction was eliminated, and this resulted a stiffness Weber fraction that equaled the force Weber fraction. In the other condition, the stiffness JND was measured without the deflection target, and a bias in deflection reproduction was again observed. This bias related the measurements for the two conditions by the formulation obtained from the first experiment. This suggests that the accuracy of reproducing the manipulator position for stiffness discrimination, which may be susceptible to experimental setting, can be used to explain the variation of stiffness JND in literature. Suggestions are given for CLS evaluation and applications requiring precise manipulator motion control.

Biodynamic feedthrough (BDFT) refers to the feedthrough of vehicle accelerations through the human body, leading to involuntary control device inputs. BDFT impairs control performance in a large range of vehicles under various circumstances. Research shows that BDFT strongly depends on adaptations in the neuromuscular admittance dynamics of the human body. This paper proposes a model-based approach of BDFT mitigation that accounts for these neuromuscular adaptations. The method was tested, as proof-of-concept, in an experiment where participants inside a motion simulator controlled a simulated vehicle through a virtual tunnel. Through evaluating tracking performance and control effort with and without motion disturbance active and with and without cancellation active, the effectiveness of the cancellation was evaluated. Results show that the cancellation approach is successful: the detrimental effects of BDFT were largely removed.

Haptic guidance is a promising way to support unmanned aerial vehicle (UAV) operators, but the design of haptic guidance forces is often heuristic. This paper describes the design and experimental validation of a systematic neuromuscular analysis-based tuning procedure for haptic guidance, here applied to haptic collision avoidance system for UAV teleoperation. This tuning procedure is hypothesized to reduce operator workload as compared with current heuristic tuning methods. The proposed procedure takes into consideration the estimated mechanical response of the neuromuscular system (NMS) to haptic cues. A “relax-task” setting of the NMS, for which reflexive and muscular activation is minimal, is chosen as the design point for tuning the haptic support, as this setting is expected to yield minimal physical workload. The paper first presents a neuromuscular identification experiment, performed to estimate the “relax task” admittance of an operator's arm. The averaged admittance of a group of subjects (n=10) was then used for tuning the haptic shared controller, which was subsequently evaluated in its ability to support different operators (n=12) in a simulated unmanned aerial vehicle surveillance task. Results show that our novel tuning procedure indeed reduces operator workload and also improves situation awareness compared with haptic settings that ignore the NMS. In fact, it is shown that overtuning, which frequently occurs for these heuristically tuned systems, leads to even lower user acceptance scores than interfaces without any haptic support.

The development of autonomous lightweight MAVs, capable of navigating in unknown indoor environments, is one of the major challenges in robotics. The complexity of this challenge comes from constraints on weight and power consumption of onboard sensing and processing devices. In this paper, we propose the “Droplet” strategy, an avoidance strategy based on stereo vision inputs that outperforms reactive avoidance strategies by allowing constant speed maneuvers while being computationally extremely efficient, and which does not need to store previous images or maps. The strategy deals with nonholonomic motion constraints of most fixed and flapping wing platforms, and with the limited field-of-view of stereo camera systems. It guarantees obstacle-free flight in the absence of sensor and motor noise. We first analyze the strategy in simulation, and then show its robustness in real-world conditions by implementing it on a 20-gram flapping wing MAV.

Real-life tracking tasks often show preview information to the human controller about the future track to follow. The effect of preview on manual control behavior is still relatively unknown. This paper proposes a generic operator model for preview tracking, empirically derived from experimental measurements. Conditions included pursuit tracking, i.e., without preview information, and tracking with 1 s of preview. Controlled element dynamics varied between gain, single integrator, and double integrator. The model is derived in the frequency domain, after application of a black-box system identification method based on Fourier coefficients. Parameter estimates are obtained to assess the validity of the model in both the time domain and frequency domain. Measured behavior in all evaluated conditions can be captured with the commonly used quasi-linear operator model for compensatory tracking, extended with two viewpoints of the previewed target. The derived model provides new insights into how human operators use preview information in tracking tasks.

The introduction of 4-D trajectory-based operations will require the development of new and more advanced “human-centered” decision support tools for future air traffic controllers. One approach to the design of human-centered decision aids is ecological interface design, which focuses on visualizing the boundaries of safe system performance rather than prescribing predetermined strategies or discrete solutions. Previous studies with ecological interfaces in the aviation domain revealed that humans sometimes opted for control actions close to these boundaries, giving rise to a general concern about the robustness of control actions. The goal of this study has been to empirically investigate how effectively an ecological interface for 4-D trajectory management, as developed in a previous study, supports the preservation of airspace robustness. For this purpose, a metric has been developed to evaluate both minimum and average sector-based and control-based robustness. Special attention was paid to quantifying and measuring the effect of expertise level on the robustness of human-generated control actions. Results of a human-in-the-loop experiment indicate that expert participants were most robust in their control actions, as compared with either skilled or novice participants. This result suggests that boundary-seeking control actions with ecological interfaces are mainly dependent on the level of expertise and the control strategies of the end user.

Hap tic shared control systems combine control inputs of driver and intelligent vehicle by means of forces at the steering wheel, establishing a physical link combining the strengths of each agent. The majority of hap tic shared control systems generates forces in response to the driving environment in a 'one size-fits-all' manner, without adapting the feedback to the requirements and preferences of individual drivers. This results in conflicts that could be avoided by allowing the hap tics shared to adapt to the environments in a human-like way. Drivers can easily adapt their neuromuscular system to changing steering dynamics, but little is known how drivers adapt it to changes in the driving environment. To enable individualized shared control we need to extend our understanding of human adaptation to environmental factors during driving. This paper describes a fixed-base driving simulator experiment that shows increased speed and reduced road width both decrease neuromuscular admittance of the driver.

In real-life manual control tasks, human controllers are often required to follow a visible and predictable reference signal, enabling them to use feedforward control actions in conjunction with feedback actions that compensate for errors. Little is known about human control behavior in these situations. This paper investigates how humans adapt their feedforward control dynamics to the controlled element dynamics in a combined ramp-tracking and disturbance-rejection task. A human-in-the-loop experiment is performed with a pursuit display and vehicle-like controlled elements, ranging from a single integrator through second-order systems with a break frequency at either 3, 2, or 1 rad/s, to a double integrator. Because the potential benefits of feedforward control increase with steeper ramp segments in the target signal, three steepness levels are tested to investigate their possible effect on feedforward control with the various controlled elements. Analyses with four novel models of the operator, fitted to time-domain data, reveal feedforward control for all tested controlled elements and both (nonzero) tested levels of ramp steepness. For the range of controlled element dynamics investigated, it is found that humans adapt to these dynamics in their feedforward response, with a close to perfect inversion of the controlled element dynamics. No significant effects of ramp steepness on the feedforward model parameters are found.

The velocity obstacle (VO) representation, a mathematical representation that supports separation assurance for autonomous vehicle navigation, can provide improved performance when the intended trajectory (intent) of the involved vehicles is shared among each other. This paper proposes a fully analytical closed mathematical form for VOs as an intuitive and flexible calculation method, which is able to incorporate time-varying concepts such as trajectory prediction errors. Several study cases for specific traffic situations are explored, and possibilities to extend the method to the third dimension are discussed. Analysis showed that the calculation of the VOs with intent information could show self-curve intersections when maneuvers at close distance are required. Further elimination of these intersections might be necessary for certain specific applications.

To lower the workload and increase the productivity of air traffic controllers in future scenarios, decision-support systems that functions on a higher level of control authority would be desired. However, increasing the control authority of automation asks for some important considerations in terms of design to ensure effective human-machine coordination. One of the factors that is hypothesized to affect human-machine collaboration, and one that is often overlooked in the design of such systems, is the solution transparency offered by the automated system. That is, when an automated system reveals some of its inner workings, it is expected to promote better system understanding and an increased acceptance of resolution advisories offered by that system. To this end, this paper investigates the effects of automation transparency on the acceptance of automated resolution advisories in conflict detection and resolution task for air traffic controllers. An exploratory experiment, featuring twelve aerospace students, was performed to study these effects. The results indicated that no main effects of transparency on acceptance were found, but also that there was a significant interaction effect between conflict geometry and transparency. For future research, an experiment is proposed with professional air traffic controllers and a larger sample size to increase the power of the statistics.

The current evolution of the Air Traffic Management system towards trajectory-based operations is foreseen to bring large changes to the work domain of the Air Traffic Controller. Although this new form of Air Traffic Control leans heavily on the introduction of advanced automation, the general consensus is that the human must remain actively involved in the decision-making loop, and retain the ultimate responsibility for the safety of operations. These responsibilities, together with the complexities of the new task, require the development of innovative decision support tools. In previous research, and following the principles of Ecological Interface Design, a constraint-based decision support tool has been developed for the task of strategic trajectory manipulation. Rather than presenting discrete optimized solutions to the controller, this Travel Space Representation visualizes the constraints for safe control in the form of a set of `go' and `no go' areas. A validation experiment demonstrated that when using this tool, controllers sometimes opted for controlling close to the boundaries of safe control, or for resolutions in narrow control spaces. These results gave rise to concern that such a representation could actually work against the flexibility of the system to cope with inherent system variability. In this study, a metric and two measures have been developed in order to quantify and compare trajectory-based robustness to probabilistic disturbances. A batch-analysis has been performed to investigate how these measures vary for a crossing pair of aircraft under various geometry. Results show that the metric captures additional information which is currently not represented in the tool. When visualized to the controller, this could support them to choose more robust control strategies.

This paper describes a novel method for analyzing the training effectiveness for skill-based manual control tasks based on multimodal human operator models. For skill-based tracking tasks, it is known that the adopted human operator dynamics can be modeled accurately with multimodal human operator models. In this paper, estimated human operator model parameters are used to explicitly quantify the changes that occur in the operator's use of visual and motion feedback during skill-acquisition and transfer. A quasi-transfer-of-training experiment is described, in which inexperienced participants were trained to perform an aircraft pitch attitude tracking task, either in a fixed-base or in moving-base simulator environment. After the training phase, the participants were transferred to the other simulator setting, to reveal possible transfer effects. Preliminary results from one participant in each experiment group indicate that the fitted models are successful in revealing the changes that occur in the multimodal manual control characteristics of the participants, and show that convergence to a final skill-based control strategy requires significant training. Furthermore, the presented results suggest that there might be limited direct transfer from training in a fixed-base environment to a moving-base environment.

This research investigates a neuromuscular analysis based tuning procedure for haptic shared control systems that has been hypothesized to improve subjective operator workload when compared to heuristic tuning methods. Here, the tuning procedure takes into consideration the response of the neuromuscular system to haptic cues. Human arm stiffness, the neuromuscular property of concern, can be changed by modulating reflex strength. The `relax task' setting of the neuromuscular system, for which reflexes are minimized, is chosen as the design point for tuning haptic cues as it is hypothesized to lead to the lowest workload. A simulated haptic collision avoidance system for unmanned aircraft teleoperation is used as a platform to experimentally validate the tuning method. The results show that the novel tuning procedure, particularly for relax task tuning, substantially improves workload and situational awareness over conditions that ignores the neuromuscular system. Additionally, over-tuning, which frequently occurs for heuristic methods, leads to worse user acceptance than a condition without haptic support.

Biodynamic feedthrough (BDFT) is the feedthrough of vehicle accelerations through the human body, leading to involuntary control device inputs. BDFT is a relevant problem as it reduces control performance in a large range of vehicles under various circumstances. This paper proposes an approach to mitigate BDFT. What differentiates this method from other mitigation approaches is that it accounts for adaptations in the neuromuscular dynamics of the human body. It is known that BDFT is strongly dependent on these dynamics. The approach was tested, as proof-of-concept, in an experiment in a motion simulator where participants were asked to fly a simulated vehicle through a virtual tunnel. By evaluating the performance with and without motion disturbance active and with and without cancellation active, the performance of the cancellation approach was evaluated. Results showed that the cancellation approach was successful. The detrimental effects of BDFT, such as a decrease in control performance and increase in control effort, were largely removed.

In manual control, a pursuit display may support the use of a multimodal “pursuit” control strategy by the human operator. This paper evaluates two methods that may be used to directly estimate describing functions for such multimodal human operator control dynamics in pursuit tracking. The first is a previously developed frequency-domain method based on Fourier coefficients. The second method makes use of linear-time invariant ARMAX models. An experiment is described in which participants performed tracking tasks with quasi-random multisine target and disturbance forcing functions, for single and double integrator controlled elements and with compensatory and pursuit displays. The experiment data confirms the findings from previous experiments, where it was found that multimodal pursuit control dynamics are adopted in control of systems with double integrator dynamics, but not for single integrator control tasks. Furthermore, the direct multimodal identification methods were found to give improved insight into the internal organization and dynamics of the human operator in pursuit tracking.

Air traffic control uses arrival managers (AMANs) to schedule an inbound stream of aircraft. Most state-of-the-art AMANs require the operator to analyse and integrate the schedule data to optimize the use of runways. None of the currently operational systems explicitly visualize inefficient use of runway capacity or the imbalance between available capacity and demand. Rather, these decisions are based on predicted arrival times and require an initial planning to determine potential delay in the active runway configuration. The research presented in this paper focuses on supporting the runway planning decision-making process using an experimental ecological AMAN by visualizing imbalances between capacity and demand. An indepth analysis of the arrival management process has been conducted and a concept interface is proposed. A human-in-the-loop experiment was conducted to investigate the effect of visualizing performance deviation information on the quality of runway planning. No significant differences in planning performance have been found between these two conditions, most likely due to the nature of the various traffic scenarios and the high overall workload during the experiment. However, more consistent performance has been observed when the performance deviation information was available to the participants and more improvements of the runway planning closer to landing were visible. Both trends are desired effects of visualizing performance deviation and warrant further research.

A biodynamic feedthrough (BDFT) model is proposed that describes how vehicle accelerations feed through the human body, causing involuntary limb motions and so involuntary control inputs. BDFT dynamics strongly depend on limb dynamics, which can vary between persons (between-subject variability), but also within one person over time, e.g., due to the control task performed (within-subject variability). The proposed BDFT model is based on physical neuromuscular principles and is derived from an established admittance model-describing limb dynamics-which was extended to include control device dynamics and account for acceleration effects. The resulting BDFT model serves primarily the purpose of increasing the understanding of the relationship between neuromuscular admittance and biodynamic feedthrough. An added advantage of the proposed model is that its parameters can be estimated using a two-stage approach, making the parameter estimation more robust, as the procedure is largely based on the well documented procedure required for the admittance model. To estimate the parameter values of the BDFT model, data are used from an experiment in which both neuromuscular admittance and biodynamic feedthrough are measured. The quality of the BDFT model is evaluated in the frequency and time domain. Results provide strong evidence that the BDFT model and the proposed method of parameter estimation put forward in this paper allows for accurate BDFT modeling across different subjects (accounting for between-subject variability) and across control tasks (accounting for within-subject variability).

Biodynamic feedthrough (BDFT) occurs when vehicle accelerations feed through the human body and cause involuntary control inputs. This paper proposes a model to quantitatively predict this effect in rotorcraft. This mathematical BDFT model aims to fill the gap between the currently existing black box BDFT models and physical BDFT models. The model structure was systematically constructed using asymptote modeling, a procedure described in detail in this paper. The resulting model can easily be implemented in many typical rotorcraft BDFT studies, using the provided model parameters. The model's performance was validated in both the frequency and time domain. Furthermore, it was compared with several recent BDFT models. The results show that the proposed mathematical model performs better than typical black box models and is easier to parameterize and implement than a recent physical model.

Biodynamic feedthrough (BDFT) is a complex phenomenon, which has been studied for several decades. However, there is little consensus on how to approach the BDFT problem in terms of definitions, nomenclature, and mathematical descriptions. In this paper, a framework for biodynamic feedthrough analysis is presented. The goal of this framework is two-fold. First, it provides some common ground between the seemingly large range of different approaches existing in the BDFT literature. Second, the framework itself allows for gaining new insights into BDFT phenomena. It will be shown how relevant signals can be obtained from measurement, how different BDFT dynamics can be derived from them, and how these different dynamics are related. Using the framework, BDFT can be dissected into several dynamical relationships, each relevant in understanding BDFT phenomena in more detail. The presentation of the BDFT framework is divided into two parts. This paper, Part I, addresses the theoretical foundations of the framework. Part II, which is also published in this issue, addresses the validation of the framework. The work is presented in two separate papers to allow for a detailed discussion of both the framework's theoretical background and its validation.

Biodynamic feedthrough (BDFT) is a complex phenomenon, that has been studied for several decades. However, there is little consensus on how to approach the BDFT problem in terms of definitions, nomenclature, and mathematical descriptions. In this paper, the framework for BDFT analysis, as presented in Part I of this dual publication, is validated and applied. The goal of this framework is twofold. First of all, it provides some common ground between the seemingly large range of different approaches existing in BDFT literature. Secondly, the framework itself allows for gaining new insights into BDFT phenomena. Using recently obtained measurement data, parts of the framework that were not already addressed elsewhere, are validated. As an example of a practical application of the framework, it will be demonstrated how the effects of control device dynamics on BDFT can be understood and accurately predicted. Other ways of employing the framework are illustrated by interpreting the results of three selected studies from the literature using the BDFT framework. The presentation of the BDFT framework is divided into two parts. This paper, Part II, addresses the validation and application of the framework. Part I, which is also published in this journal issue, addresses the theoretical foundations of the framework. The work is presented in two separate papers to allow for a detailed discussion of both the framework's theoretical background and its validation.

We describe initial steps in the design of a haptic shared control system for obstacle avoidance in unmanned aerial vehicle (UAV) teleoperation. In the considered scenario, the operator is supported by force feedback on the control interface, by which a seamless integration of his commands and the assistance from an automatic collision avoidance system is created. To find proper tuning for this haptic shared control system, analysis of the operator's neuromuscular system is used as a guideline. Relevant properties of human NMS are discussed together with corresponding choices for tuning of the haptic shared control system. Tuning the system for the intrinsic stiffness is selected here, to ensure that during teleoperation only the minimal physical workload of the operator is required. A new way to determine the intrinsic stiffness, even when the operator needs to exert forces on the control inceptor, is described here. Proposed method uses wide bandwidth perturbation signal in the NMS identification. In this way natural reflexive feedback paths are suppressed which allows to determine the intrinsic stiffens of the operator arm. The method is discussed in details and experimental results are shown. The paper then presents results of a NMS identification experiment with 10 participants. The procedure resulted in a set of representative frequency responses functions for the NMS stiffness, on the basis of which the haptic feedback for the UAV can be tuned.

In the manual control of a dynamic system, the human controller (HC) often follows a visible and predictable reference path. Compared with a purely feedback control strategy, performance can be improved by making use of this knowledge of the reference. The operator could effectively introduce feedforward control in conjunction with a feedback path to compensate for errors, as hypothesized in literature. However, feedforward behavior has never been identified from experimental data, nor have the hypothesized models been validated. This paper investigates human control behavior in pursuit tracking of a predictable reference signal while being perturbed by a quasi-random multisine disturbance signal. An experiment was done in which the relative strength of the target and disturbance signals were systematically varied. The anticipated changes in control behavior were studied by means of an ARX model analysis and by fitting three parametric HC models: two different feedback models and a combined feedforward and feedback model. The ARX analysis shows that the experiment participants employed control action on both the error and the target signal. The control action on the target was similar to the inverse of the system dynamics. Model fits show that this behavior can be modeled best by the combined feedforward and feedback model.

When performing a manual control task, vehicle accelerations can cause involuntary limb motions, which can result in unintentional control inputs. This phenomenon is called biodynamic feedthrough (BDFT). In the past decades, many studies into BDFT have been performed, but its fundamentals are still only poorly understood. What has become clear, though, is that BDFT is a highly complex process, and its occurrence is influenced by many different factors. A particularly challenging topic in BDFT research is the role of the human operator, which is not only a very complex but also a highly adaptive system. In literature, two different ways of measuring and analyzing BDFT are reported. One considers the transfer of accelerations to involuntary forces applied to the control device (CD); the other considers the transfer of accelerations to involuntary CD deflections or positions. The goal of this paper is to describe an approach to unify these two methods. It will be shown how the results of the two methods relate and how this knowledge may aid in understanding BDFT better as a whole. The approach presented is based on the notion that BDFT dynamics can be described by the combination of two transfer dynamics: 1) the transfer dynamics from body accelerations to involuntary forces and 2) the transfer dynamics from forces to CD deflections. The approach was validated using experimental results.

In this paper, we investigate the effects of visual and motion stimuli on the manual control of one's direction of self-motion. In a flight simulator, subjects conducted an active target-following disturbance-rejection task, using a compensatory display. Simulating a vehicular control task, the direction of vehicular motion was shown on the outside visual display in two ways: an explicit presentation using a symbol and an implicit presentation, namely, through the focus of radial outflow that emerges from optic flow. In addition, the effects of the relative strength of congruent vestibular motion cues were investigated. The dynamic properties of human visual and vestibular motion perception paths were modeled using a control-theoretical approach. As expected, improved tracking performance was found for the configurations that explicitly showed the direction of self-motion. The human visual time delay increased with approximately 150 ms for the optic flow conditions, relative to explicit presentations. Vestibular motion, providing higher order information on the direction of self-motion, allowed subjects to partially compensate for this visual perception delay, improving performance. Parameter estimates of the operator control model show that, with vestibular motion, the visual feedback becomes stronger, indicating that operators are more confident to act on optic flow information when congruent vestibular motion cues are present.

Advanced driving simulators aim at rendering the motion of a vehicle with maximum fidelity, which requires increased mechanical travel, size, and cost of the system. Motion cueing algorithms reduce the motion envelope by taking advantage of limitations in human motion perception, and the most commonly employed method is just to scale down the physical motion. However, little is known on the effects of motion scaling on motion perception and on actual driving performance. This paper presents the results of a European collaborative project, which explored different motion scale factors in a slalom driving task. Three state-of-the-art simulator systems were used, which were capable of generating displacements of several meters. The results of four comparable driving experiments, which were obtained with a total of 65 participants, indicate a preference for motion scale factors below 1, within a wide range of acceptable values (0.4-0.75). Very reduced or absent motion cues significantly degrade driving performance. Applications of this research are discussed for the design of motion systems and cueing algorithms for driving simulation.

Two experiments, an active conflict resolution task and a passive situation awareness assessment, were conducted that compared two versions of a constraint-based coplanar airborne separation assistance display. A baseline display showed a maneuver space based on 2-D projections of traffic and performance constraints. A second augmented display also incorporated cutting planes that take the dimension orthogonal to the projection into account, thereby providing a more precise visualization of traffic constraints. Results showed that although pilots performed well with either display, the augmented display scored consistently better in terms of performance, efficiency of conflict resolutions, the amount of errors in the initial resolutions, and the level of situation awareness compared with the baseline display. On the other hand, more losses of separation were found with the augmented display, as pilots tried to maximize the maneuvering efficiency according to the precision with which constraints were visualized.

This paper describes a concept for a coplanar airborne self-separation display, which is designed to aid pilots in their separation task, by visualizing the possibilities for conflict resolution that the airspace provides. This study is a part of an ongoing research toward the design of a constraint-based 3-D separation assistance interface that can present all the relevant properties of the spatiotemporal separation problem. A display concept is proposed that presents speed, heading, and altitude action possibilities in two planar projections of the maneuver action space. The interface also visualizes how these projections interact with each other.

Drivers usually maintain an error-neglecting control strategy (passive phase) in keeping their vehicle on the road, only to change to an error-correcting approach (active phase) when the vehicle state becomes inadequate. We developed an algorithm that is capable of detecting whether the driver is currently error-neglecting or error-correcting in straight lane keeping tasks. The development of this algorithm was part of a larger research project, DrivObs, that aims at creating an advanced driver observation tool. Performance of the algorithm in a straight lane driving task with lateral vehicle position perturbations was tested in a Monte Carlo simulation using Matlab/Simulink. Results show that the algorithm is capable of correctly detecting active or passive phase 90–95% of the time, depending on vehicle speed and algorithm settings.

The human in manual control of a dynamical system can use both feedback and feedforward control strategies and will select a strategy based on performance and required effort. Literature has shown that feedforward control is used during tracking tasks in response to predictable targets. The influence of an external disturbance signal on the utilization of a feedforward control strategy has never been investigated, however. We hypothesized that the human will use a combined feedforward and feedback control strategy whenever the predictable target signal is sufficiently strong, and a predominantly feedback strategy whenever the random disturbance signal is dominant. From the data of a human-in-the-loop experiment we conclude that feedforward control is used in all the considered experimental conditions, including those where the disturbance signal is dominant and feedforward control does not deliver a marked performance advantage.

An experiment was conducted to evaluate a concept for a co-planar self-separation display. The display shows performance and traffic constraints on maneuvering, as well as interactions of constraints between the two planar projections. In the experiment, a comparison was made between the new concept and a comparable display that did not show these interactions. Results showed that although pilots performed well with either display, performance was consistently better with the full display. Similar to previous studies, a preference for single-axis maneuvers was found, although this effect was smaller for difficult scenarios. A type of behavior persistent to constraint-based displays was also observed in this experiment: Pilots maneuver close to the constraint boundary, which can have an impact on safety when judgment errors are made.

Changes in Air Traffic Management, exemplified by the NextGen and SESAR projects, intend to achieve ATM systems with a higher capacity and also higher efficiency, through more direct routing. At the same time, air traffic safety should continue to increase. An important step in this process can be the partial or full transfer of responsibility for separation to the flight deck, using Airborne Separation Assurance Systems (ASAS). In previous work, our group has developed flight deck displays that enable pilots to perform the task of self-separation through the visualization of conflicts on the displays. In various tests with these displays, pilots successfully performed the task of self-separation, using only the implicit coordination based on conflict geometry as presented on the display. In order to further investigate the emergent features of the use of such displays, a set of rules was created to mimic the behavior observed in the experiments. Using this simulation, a Monte Carlo experiment was run, in which the properties of encounters between two aircraft were further investigated. The simulations results indicate that the implicit coordination used in our set-up is not always successful. Furthermore, instructions for solving the conflict that emphasize quick resolution can be detrimental to safety.

Biodynamic feedthrough (BDFT) refers to a phenomenon where vehicle accelerations feed through the human body, causing involuntary limb motions, which may cause involuntary control inputs. Many studies have been devoted to mitigating BDFT effects. In the current paper, the effectiveness of a simple, cheap and widely-used hardware component is studied: the armrest. An experiment was conducted in which the BDFT dynamics were measured with and without armrest for different levels of neuromuscular admittance (i.e., different settings of the limb dynamics). The results show that the effect of the armrest on BDFT dynamics varies, both with frequency and neuromuscular admittance.

Driver control behaviour is highly time variant. When studying the neuromuscular system of drivers in interaction with the steering wheel, the common Fourier system identification techniques are only applicable when time-invariant behaviour is assumed. This paper describes how wavelets can be used to identify time-variant neuromuscular admittance. Using the Morlet wavelet transformation, time domain signals are transformed to a time-frequency representation. A non-parametric, time-variant frequency response function can be estimated using the transformed signals. A model of the neuromuscular system of a driver controlling a steering wheel was used to generate time-variant data. This paper shows that the Morlet wavelet transformation is a valid tool for estimating accurate time-variant frequency responses of neuromuscular arm dynamics. The results of this article give us confidence that wavelet analysis can be used on experimental data, with lower signal-to-noise ratio, too. This will allow us to identify how drivers adjust their neuromuscular system during driving.

The aim of this study was to find a realistic control-theoretic visual driver model for curve driving that does not only show simular performance as actual drivers but also applies the same inputs and uses the same information. The model structure must enable system identification and parameter estimation of the model parameters. A large number of existing and adapted models have been evaluated and simulated, and when possible, frequency response functions have been identified using two system identification methods. A significant part of the paper is devoted to review these models. The evaluation shows that two-point models comply best with all system identification requirements while still governing realistic driving behavior. It is recommended to investigate further the positioning and perception part of the two-point models using eye-tracking in driving experiments with real human drivers.

Vehicle accelerations may feed through the human body, causing involuntary limb motions which may lead to involuntary control inputs. This phenomenon is called biodynamic feedthrough (BDFT). Signal cancellation is a possible way of mitigating biodynamic feedthrough. It makes use of a BDFT model to estimate the involuntary control inputs. The BDFT effects are removed by subtracting the modeled estimate of the involuntary control input from the total control signal, containing both voluntary and involuntary components. The success of signal cancellation hinges on the accuracy of the BDFT model used. In this study the potential of signal cancellation is studied by making use of a method called optimal signal cancellation. Here, an identified BDFT model is used off-line to generate an estimate of the involuntary control inputs based on the accelerations present. Results show that reliable signal cancellation requires BDFT models that are both subject and task dependent. The task dependency is of particular importance: failing to adapt the model to changes in the operator's neuromuscular dynamics dramatically decreases the quality of cancellation and can even lead to an increase in unwanted effects. As a reliable and fast on-line identification method of the neuromuscular dynamics of the human operator currently does not exist, real-time signal cancellation is currently not feasible.

The research presented in this paper focuses on the design of a driver support system for the manual longitudinal control of a car during car-following. The aim of the design was to develop a system that would cooperate with the driver in comfortably maintaining (safe) separation with a lead vehicle. Three important design issues for a haptic gas pedal feedback system can be distinguished: 1) quantification of intervehicle separation parameters; 2) the type of haptic feedback; and 3) the relation between haptic feedback and intervehicle separation. Because of the inverse relationship between time-to-contact (TTC) and time-headway (THW)-the smaller the THW, the more important the avoidance of high TTC-THW should act as an amplifier for the haptic gas pedal feedback based on TTC. Using gas pedal stiffness feedback is expected to better facilitate the manual control of intervehicle separation changes, quantified by THW and TTC, because stiffness feedback allows perception of force and force-slope changes. The force changes inform drivers of instantaneous changes in the environment. Force-slope changes prevent drivers from input to the car that would continue to reduce the following gap in situations where this would be undesirable. A review of fixed-base simulator and field tests confirms that haptic gas pedal feedback improves driver vigilance during car-following without increasing the workload.

In previous research, a driver support system that uses continuous haptic feedback on the gas pedal to inform drivers of the separation to the lead vehicle was developed. Although haptic feedback has been previously shown to be beneficial, the influence of the underlying biomechanical properties of the driver on the effectiveness of haptic feedback is largely unknown. The goal of this paper is to experimentally determine the biomechanical properties of the ankle-foot complex (i.e., the admittance) while performing a car-following task, thereby separating driver responses to visual feedback from those to designed haptic feedback. An experiment was conducted in a simplified fixed-base driving simulator, where ten participants were instructed to follow a lead vehicle, with and without the support of haptic feedback. During the experiment, the lead vehicle velocity was perturbed, and small stochastic torque perturbations were applied to the pedal. Both perturbations were separated in the frequency domain to allow the simultaneous estimation of frequency response functions of both the car-following control behavior and the biomechanical admittance. For comparison to previous experiments, the admittance was also estimated during three classical motion control tasks (resist forces, relax, and give way to forces). The main experimental hypotheses were that, first, the haptic feedback would encourage drivers to adopt a "give way to force task," resulting in larger admittance compared with other tasks and, second, drivers needed less control effort to realize the same car-following performance. Time- and frequency-domain analyses provided evidence for both hypotheses. The developed methodology allows quantification of the range of admittances that a limb can adopt during vehicle control or while performing a variety of motion control tasks. It thereby allows detailed computational driver modeling and provides valuable information on how to design and evaluate continuous haptic feedback systems.

In the context of the Next Generation Air Transportation System and Single European Sky ATM Research future airspace programs, this paper describes a concept for an airborne separation assurance display that is designed to aid pilots in their task of self-separation, by visualizing the possibilities for conflict resolution that the airspace provides. This paper is part of an ongoing research toward an ecological design of a separation assistance interface that can present all the relevant properties of the spatiotemporal separation problem. A work-domain analysis is described from which several perspective projections of traffic properties and travel constraints are derived. A display concept is proposed that presents heading and altitude action possibilities in a flight-path angle-track angle action space. Key issues in the current design are discussed, with recommendations for future work.

Biodynamic feedthrough (BDFT) refers to a phenomenon where accelerations cause involuntary limb motions, which can result in unintentional control inputs that can substantially degrade manual control. It is known that humans can adapt the dynamics of their limbs by adjusting their neuromuscular settings, and it is likely that these adaptations have a large influence on BDFT. The goal of this paper is to present a method that can provide evidence for this hypothesis. Limb dynamics can be described by admittance, which is the causal dynamic relation between a force input and a position output. This paper presents a method to simultaneously measure BDFT and admittance in a motion-based simulator. The method was validated in an experiment. Admittance was measured by applying a force disturbance signal to the control device; BDFT was measured by applying a motion disturbance signal to the motion simulator. To allow distinguishing between the operator's responses to each disturbance signal, the perturbation signals were separated in the frequency domain. To show the impact of neuromuscular adaptation, subjects were asked to perform three different control tasks, each requiring a different setting of the neuromuscular system (NMS). Results show a dependence of BDFT on neuromuscular admittance: A change in neuromuscular admittance results in a change in BDFT dynamics. This dependence is highly relevant when studying BDFT. The data obtained with the proposed measuring method provide insight in how exactly the settings of the NMS influence the level of BDFT. This information can be used to gain fundamental knowledge on BDFT and also, for example, in the development of a canceling controller.

In this article the effect of adding motion cues to a car simulation on human operator behavior and simulator acceptance was researched. This was done by performing a car driving experiment, in which three different settings of the classical linear washout algorithm were used. These settings were: no motion, no tilt-coordination and full motion. Objective measures on human control input and driving performance showed that the addition of motion cues positively affected the driving behavior and improved performance. Subjects also subjectively preferred the conditions where all motion cues were present over the conditions without the presence of motion cues.

In the context of the NextGen and SESAR future airspace programmes, this paper describes a concept for an Airborne Separation Assurance (ASAS) display, that is designed to aid pilots in their task of self-separation, by visualizing the possibilities for conflict resolution that the airspace provides. This work is part of an ongoing research towards an ecological design of a separation assistance interface that can present all the relevant properties of the spatio-temporal separation problem. A work-domain analysis is described from which several perspective projections of traffic properties and travel constraints are derived. A display concept is proposed that presents heading and altitude action possibilities in a flight-path angle - track angle action space. Key issues in the current design are discussed, with recommendations for future work.

Previous research introduced the solution space as a workload metric for air traffic controller merging tasks. The Solution Space Diagram is a mapping of intruding aircraft trajectories to the velocity/heading plane of a considered aircraft. This mapping results in the form of conflict zones and safe areas. Choosing a velocity vector for a controlled aircraft in either one of these zones will provide an unsafe or a safe solution, respectively. Investigation with a static version of the solution space, and a dynamic version, which provides a time-varying complexity metric, indicated that this metric corresponded well with workload ratings given by subjects. This paper considers the applicability of the metric, and discusses further directions of research and development for this metric.

Most collision avoidance systems for highway scenarios are shifting the role of the driver from manual execution to a supervisory position. In the interface design of the proposed collision avoidance system proposed in this article, the human-machine issues complicating task automation were avoided by adopting a human-centered approach. In this paper a method to preserve driver's choice and maneuver initiation through the use of a temporarily unstable steering wheel is presented. In the particular driving condition investigated in a fixed base driving simulator, the driver has to avoid an obstacle following one of two equally safe escape paths (left or right evasive maneuver). The collision avoidance system aims at supporting both available solutions by means of a haptic steering wheel interface. Two feedback modalities are used, namely torque feedback and stiffness feedback. The results of the experiment show that the haptic interface effectively reduced the number of crashes, decreased response time with at least 100 ms while reducing the control effort and activity in the most critical situations.

Vehicle accelerations may lead to involuntary limb motions. These motions can result into involuntary control inputs when performing a manual control task. This phenomenon is called biodynamic feedthrough (BDFT). This paper aims to show that task interpretation plays an important role in the occurrence of BDFT. Results of an experiment are presented, in which biodynamic feedthrough was measured during three different control tasks. Each control task required the human operator to adapt his/her neuromuscular settings. The results show that the level of biodynamic feedthrough depends on the task the human operator is performing. From further analysis, it can be observed that the experiment results are in good agreement with BDFT measurements found in literature. The comparison confirms that the task interpretation plays an important role in BDFT which cannot be ignored when attempting to understand or mitigate BDFT in practical situations.

A haptic gas pedal feedback system is developed that provides car-following information via haptic cues from the gas pedal. During normal car-following situations, the haptic feedback (HF) cues were sufficient to reduce control activity and improve car-following performance. However, in more critical following situations, drivers use the brake pedal to maintain separation with the lead vehicle. A deceleration control (DC) algorithm is designed that, in addition to the HF, provided increased deceleration upon release of the gas pedal during car-following situations that required faster deceleration than releasing the gas pedal alone would do. For the design, a driver model for car following in different situations was estimated from driving simulator data. A Monte Carlo analysis with the driver model yielded subjective decision points, where drivers released the gas pedal to start pressing the brakes. This enabled the definition of a reaction field, which determined the needed deceleration input for the DC algorithm. The tuned DC algorithm was tested in a fixed-base driving simulator experiment. It was shown that the active deceleration support improved the car-following performance while reducing the driver brake pedal input magnitude in the conditions tested.

This study investigates the use of force-stiffness feedback, i.e., a combination of force offset and extra spring load, in UAV tele-operation with transmission time delay. The goal was to further increase the level of safety of tele-operation with a reduction in operator workload with respect to force feedback, i.e., using force offset alone. A theoretical analysis is given of using force-stiffness feedback to improve collision avoidance. Wave variables are included to reduce time delay effects. An experiment was conducted to investigate the effects of force-stiffness feedback on safety of operation, operator performance, control activity, and workload. Results indicate that force-stiffness feedback improves the haptic interface with respect to force feedback alone. Safety of tele-operation increases without increasing operator workload with respect to force feedback.

A haptic gas pedal feedback system was developed at Delft University of Technology that translated the separation to a lead vehicle into continuous haptic cues (force, stiffness) on the gas pedal. During normal car-following situations, the haptic feedback cues were sufficient to reduce control activity and improve car-following performance. However, in more critical following situations drivers use the brake pedal to maintain separation with the lead vehicle. A deceleration control algorithm was designed that, in addition to the haptic feedback, provided increased deceleration upon release of the gas pedal during critical car-following situations. The deceleration control algorithm was tested in a fixed-base driving simulator experiment. Deceleration control improved car-following performance while reducing driver brake pedal control activity in the conditions tested.

In continuous manual control tasks, pilots adapt their control strategy to the dynamics of the controlled element to yield adequate performance of the combined pilot-vehicle system. For a controlled element representing the linearized pitch dynamics of a small jet aircraft, the pilot models described in literature were found to lack the required freedom in the pilot equalization term to accurately model the adopted pilot compensation. An additional lead term in the pilot equalization transfer function was found to significantly increase the accuracy in modeling manual control behavior of aircraft pitch dynamics.

When an operator in a moving vehicle is performing a manual control task, the accelerations to which the operator is subjected can result in unintentional control inputs. This biodynamic feedthrough (BDFT) depends on the properties of the control device and of the control limb. Humans can adjust the dynamics properties of their limbs, effectively changing the limb admittance. Previous studies of BDFT did not consider the effect of this adjustment. This paper describes a model for BDFT and an experiment in a moving base simulator with subjects performing a control task with a side stick. During the experiment the neuromuscular admittance was varied by using different control tasks, each requiring a different neuromuscular setting. The non-parametric results of this experiment show that the level of feedthrough is strongly dependent on both the frequency of the disturbance and the neuromuscular admittance. The results furthermore suggest that a relationship can be established between admittance and biodynamic feedthrough.

The feedback upon which operators in teleoperation tasks base their control actions differs substantially from the feedback to the driver of a vehicle. On the one hand, there is often a lack of sensory information; on the other hand, there is additional status information presented via the visual channel. Haptic feedback could be used to unload the visual channel and to compensate for the lack of feedback in other modalities. For collision avoidance, haptic feedback could provide repulsive forces via the control inceptor. Haptic feedback allows operators to interpret the repulsive forces as impedance to their control deflections when a potential for collision exists. Haptic information can be generated from an artificial force field (AFF) that maps environment constraints to repulsive forces. This paper describes the design and theoretical evaluation of a novel AFF, i.e., the parametric risk field, for teleoperation of an uninhabited aerial vehicle (UAV). The field allows adjustments of the size, shape, and force gradient by means of parameter settings, which determine the sensitivity of the field. Computer simulations were conducted to evaluate the effectiveness of the field for collision avoidance for various parameter settings. Results indicate that the novel AFF more effectively performs the collision avoidance function than potential fields known from literature. Because of its smaller size, the field yields lower repulsive forces, results in less force cancellation effects, and allows for larger UAV velocities. This indicates less operator control demand and more effective UAV operations, both expected to lead to lower operator workload, while, at the same time, increasing safety.

This paper aims to provide recommendations for improving the effectiveness of automatic, student-adaptive, simulation-based driver training. Using experiments and recorded data in driving simulators, three distinct issues are discussed: 1) the student, 2) the virtual driving instructor (VDI), and 3) the student-profile. We found that: first, students seek task-relevant information themselves; not providing them with feedback can be beneficial. Second, an intelligent VDI that emulates a human driving instructor is not favored. To the contrary, regressive instruction -- a relatively simple principle -- was effective in letting students drive away autonomously. Third, constructing a student-profile based on individual characteristics, such as a strength-weakness report, is viable for providing student-adaptive feedback.

In future airspace environments, there is a need for on-board self-separation interfaces that support pilots in the resolution and prevention of aircraft conflict situations. Current designs offer pilots explicit evasive maneuvers, but it is questionable whether such a design approach promotes conflict Situation Awareness (SA) and task-independent support. As an alternative, an ecological display was designed to promote pilot SA. This display maps functional overlays on the Navigation Display (ND) that show pilots how their manoeuvring is constrained by own aircraft performance and by limits imposed by surrounding traffic. This paper discusses a comparison experiment between the ecological display and a viable design alternative using explicit resolution commands. The experiment was aimed at measuring the pilot's awareness of the conflict situation using a situation-centered approach. Results confirm that the ecological display improves SA, but did not indicate significant differences in safety and performance measurements.

To reduce the workload of flying an Unmanned Areal Vehicle (UAV), some of the tasks of the human operator can be automated. In the Levels Of Sophistication (LOS) structure, the capabilities of a UAV system are subdivided. Using this structure, the capabilities concerned with the aviating of an UAV are selected to be automated for this research. The task of the aviating capabilities of an UAV is to integrate all the higher level mission and navigation constraints with constraints from the environment, in order to find a route for the UAV to fly which is safe and efficient. This is done by expressing the constraints in cost maps. The A* pathfinding algorithm then uses these cost maps to find a route for the UAV to fly, taking into account these constraints. The UAV aviating automation has been implemented in a simulation program. The system is capable of finding the best route in the cost map representing the constraints from different levels of sophistication, and let the UAV fly this route in the simulation. The complete automation system was tested in the 2007 Micro UAV flight competition, where the UAV was required to execute some autonomous tasks.

Modeling multi-modal perception and control of pilots can be an important tool when designing new manual control systems or assessing simulator fidelity. This paper introduces a new pilot model parameter estimation technique based on maximum likelihood estimation. The method provides new possibilities for analyzing multi-modal control behavior in a target-following task. The new method is applied to data from an experiment comparing pilot control behavior in real flight and in a flight simulator. Previously, the data from this experiment could only be used to estimate a lumped, single modality, pilot model. The results from the maximum likelihood estimation technique indeed give new insights into the pilots use of different modalities in the aircraft and in the simulator.

In a free-flight airspace environment, pilots have more freedom to choose user-preferred trajectories. An onboard pilot support system is needed that exploits travel freedom while maintaining spatial separation with other traffic. Ecological interface design is used to design an interface tool that assists pilots with the tactical planning of efficient conflict-free trajectories toward their destination. Desired pilot actions emerge from the visualization of workspace affordances in terms of a suitable description of aircraft (loco)motion. Traditional models and descriptions for aircraft motion cannot be applied efficiently for this purpose. Through functional modeling, more suitable locomotion models for trajectory planning are analyzed. As a result, a novel interface, the state vector envelope, is presented that is intended to provide the pilot with both low-level information, allowing direct action, and high-level information, allowing conflict understanding and situation awareness.

Driver assistance systems that supply force feedback (FF) on the accelerator commonly use relative distance and velocity with respect to the closest lead vehicle in front of the own vehicle. This 1-D feedback might not accurately represent the situation and can cause unwanted step-shaped changes in the FFs during lateral maneuvers. To address these shortcomings, a 2-D system is proposed that calculates FF using a weighted average of the influences of lead vehicles. Offline simulations and an experiment in a driving simulator were performed to compare no feedback, 1-D systems, and the novel 2-D system during a car-following task with cut-in maneuvers. Results show that the 2-D feedback resulted in lower mean forces, lower response times to cut-in vehicles, and favorable subjective experiences as compared to the 1-D systems.

Given the increased flexibility and autonomy that aircraft will have in future airspace environments, pilots need proper support for airborne self-separation. A support tool was developed following the guidelines of Ecological Interface Design, and was evaluated by pilot experiments. This paper describes how the inclusion of turn dynamics, and the use of intruder intent information was used to extend the original design in terms of representation format and used maneuver strategies. An extended intent-based design is proposed for further research and experimental evaluation.

Tele-operation of an unmanned aerial vehicle (UAV) may involve time delay due to signal transmission. This will result in poor operator performance and control difficulties. Particularly, in a tele-operation system with haptic feedback, time delay may lead to instability, which leads to unsafe tele-operation. Wave variables are often suggested as a method for increasing operator performance in a bilateral system with time delay. However, very little has been reported about effects on human-machine interactions, such as control activity and workload, when using wave variables. This paper describes a theoretical analysis of using wave variables with a collision avoidance system for UAV tele-operation with time delay. An experiment was conducted to evaluate the effectiveness of wave variables in a collision avoidance system for tele-operation with time delay in a UAV control station equipped with haptic feedback. Operator performance, safety, control activity, and workload were studied. Results indicate that wave variables can increase the performance of a collision avoidance system with presence of time delay. The amount of collisions, control activity, and workload decreased. Operator performance remained the same as with no time delay.

In the tele-operation of a UAV, haptic feedback can be used to complement visual feedback, resulting in a multi-sensory interface that allows operators to enhance their situation awareness. However, when inconsistencies between haptic information and visual information exist, it is questionable whether haptic feedback is still useful. This paper describes an experimental evaluation of using haptic feedback with a degraded visual interface. The display failure was expressed by errors in the coordinates of objects projected on the navigation display. Experiment results indicate that errors in the navigation display leads to a considerable increase of collisions. Haptic feedback helps reducing the increase of the number of collisions, hence, less deterioration of the level of safety.

Haptic feedback is often suggested to complement visual information through the sense of touch to improve efficiency and safety in the tele-operation of unmanned aerial vehicles. It is, however, of great importance that the improvements are not at the cost of higher operator workload and control activity, which strongly depends on how haptic feedback is presented to the operator. This paper describes the study on two different techniques of haptic feedback for collision avoidance in the tele-operation of a UAV helicopter. The first technique considers the addition of an external force offset generated by an artificial force field, whereas the second technique considers the addition of an external spring constant. The study involves a theoretical analysis and an experimental evaluation of the two haptic feedback implementations. Experiment results indicate that the use of the external spring constant outperforms the force offset. Operator performance improves and workload decreases with equivalent control activity and level of safety with respect to the use of force offset.

In future command and control (C2) for anti-air warfare (AAW) the nature of warfare changes from open seas to coastal areas, budget reductions force to 'do more with less', and technology causes increasing speed and maneuverability of weapons. This results in a higher workload for operators on ships, who are responsible for accomplishing a mission. In this paper, ecological interface design (EID) is applied to C2 to reduce workload and improve situation awareness. New displays were developed to support operators in engagement planning during high load scenarios. The design rationale of the engagement planning interfaces and the results of a first experimental evaluation are discussed.

Cornering during rally driving is a difficult task, because the rally car is a complex non-linear system and the driver has limited knowledge of the geometry of the road that lies ahead. New technologies and Ecological Interface Design principles provide new opportunities to design a replacement of the current support system, provided by a copilot and pacenotes, that demand high levels of cognitive processing. An abstraction hierarchy provides insight in the relevant system constraints: performance, curve and velocity, acceleration, wheel slip and control inputs. A head up display is designed, using an ideal line mapped on the road surface for guidance purposes. The support system consists of two prediction curves: a trajectory curve that delivers a prediction of the cars’ path when the current acceleration is maintained; and a second curve, called boundary curve, which shows the drivers’ affordances regarding maximum lateral and longitudinal accelerations.

This paper describes a theoretical study on the use of an artificial force field in a collision avoidance system to generate force feedback to a neuromuscular arm. The study involves two separate tasks. The first task identifies the intrinsic and reflexive parameters of the human neuromuscular arm model. The second task is to include the neuromuscular arm model in closed-loop offline simulations in order to achieve more insight into the human-machine interaction, when applying haptic feedback. Results indicate that the human neuromuscular arm model responds well to the force feedback generated by the artificial force field and offers the possibility to tune the force field in order to achieve good performance without excessive control activity.

A car-following experiment was conducted to investigate the effects of lead vehicle speed and separation distance on driver car-following behavior. Furthermore, the influence of a driver support system with haptic gas pedal feedback was investigated as well. The experiment was set up such that system identification techniques could be applied using a quasi-linear multi-loop car-following model. It was assumed that car-following is a compensatory task in which errors in relative velocity and relative distance are minimized. Results of the experiment show that drivers mainly use relative velocity for their control task. Low crossover frequencies and high phase margins indicate that car-following is a relatively simple and robust task, which is difficult to improve on when drivers can make full use of visual feedback.

A procedure is developed to identify the driver frequency response functions (FRF) of a serial multi-loop car-following model. It is shown that with a single forcing function both the inner-loop and outer-loop FRF's can be identified, although indirectly. Parameterisation of the driver FRF's is done using the concepts of the simplified precision model developed by McRuer and Krendel in the 1970's. The forcing function characteristics, data processing procedures, non-parametric and parametric identification are described. Experimental data are used to show the results of the identification procedure. In non-critical car-following situations (not requiring braking) the simplified precision model can be used as a linear description of driver car-following behaviours.

This paper presents the results of the design and evaluation of a context based intent inference system for highway lane changes. Traditionally, intent inference for this task is based on information from the subject vehicle only. Because the context, e.g. traffic situation, plays an important role in the driver's situational awareness, a study was performed to investigate what results can be achieved by including this context information in the intent inference system. It was observed that traditional state based systems have a 90% accuracy and reach their maximum performance much earlier than a simple lateral threshold. The addition of various context information variables to the state based system decreased performance, caused by the models having problems capturing the complexity of the context data.