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The object of research is motion cueing along angular degrees of freedom on flight simulators of non-maneuvering aircraft. One of the most problematic places is lack of statement and effective solution of the problem to ensure high-quality motion cues along angular degrees of freedom on flight simulators, which would correspond to motion cues along angular degrees of freedom in real flight with the same control actions. In the course of the research, on the basis of the peculiarities of human movement perception, a set of characteristic attributes of perception of motion cues is determined: character, direction, duration, intensity and time of motion perception (according to Gibson’s perception theory). Based on the system approach principles, the mathematical formulation of the solution to the problem of motion cueing along angular degrees of freedom on flight simulators of non-maneuvering aircraft is used. Such approach made it possible, taking into account the existing constructive resource of flight simulator motion system, to bring as close as possible motion cueing along angular degrees of freedom on flight simulators of non-maneuvering aircraft to motion cues along angular degrees of freedom in real flight with the same control actions. Due to this the character and direction of motion cues fully correspond to the real motion cues, the difference between the perception time of motion cues on airplane and simulator is minimal and meets the current requirements. The duration and intensity of the motion cue perception on simulator are proportional duration and intensity of motion cue perception on airplane. Such approach significantly improves the quality of training and retraining of pilots on flight simulators. Implementation of the developed problem formulation on aircraft simulators, in particular on An-74TK-200, showed its high efficiency. In the future, the proposed approach can be used on flight simulators of aircraft developed in Ukraine and modernization of operated flight simulators. [ABSTRACT FROM AUTHOR]

This article investigates the potential of a motion-based driving simulator in the field of vehicle dynamics testing, specifically for heavy vehicles. For this purpose, a case study was prepared embodying the nature of a truck dynamics test setup. The goal was to investigate if the drivers in the simulator could identify the handling differences owed to changes in vehicle parameters, while driving the simulated trucks. Results show that the drivers could clearly identify the differences in vehicle behaviour for most of the performed tests, which motivates further investigative work in this area and exposes the feasibility of heavy vehicle dynamics testing in simulators. [ABSTRACT FROM AUTHOR]

• Optimal trajectories for motion simulators can be calculated offline. • Motion simulator parameters can be optimized simultaneously with the trajectories. • Human sensory model can be taken into account in the optimization. • The software to perform such optimization is developed and made publicly available. This paper presents a method to simultaneously compute optimal simulator motions and simulator parameters for a predefined set of vehicle motions. The optimization can be performed with a model of human motion perception or sensory dynamics taken into account. The simulator dynamics, sensory dynamics, and optimality criterion are provided by the user. The dynamical models are defined by implicit index-1 differential-algebraic equations (DAE). The direct collocation method is used to find the numerical solution of the optimization problem. The possible applications of the method include calculating optimal simulator motion for scenarios when the future motion is perfectly known (e.g., comfort studies with autonomous vehicles), optimizing simulator design, and evaluating the maximum possible cueing fidelity for a given simulator. To demonstrate the method, we calculated optimal trajectories for a set of typical car maneuvers for the CyberMotion Simulator at the Max-Planck Institute for Biological Cybernetics. We also optimize the configurable cabin position of the simulator and assess the corresponding motion fidelity improvement. The software implementation of the method is publicly available. [ABSTRACT FROM AUTHOR]

• Simulator replicates the leans illusion when the pilot's head position is varied. • Findings can help to choose the kinematic characteristics of an actual SD simulator. • Human-perception-based approach can be useful to evaluate motion cueing algorithms. • The proposed approach is independent of the technology within the motion system. Spatial disorientation (SD) is one of the major threats to aircraft pilots, therefore a ground-based SD training is an important factor in reducing the SD‐related accidents. This paper examines some of the kinematic issues relating to the motion control of the SD simulator. The problem of kinematics concerns the evaluation of the impact of the pilots' head position on their own perceived motion cues. It was interesting whether the simulator was able to replicate the leans illusion with the same quality when the translational position of the head has been changed. This problem was solved by numerical calculations, in which the pilot's head kinematics were used. The SD simulator was modeled as a three-joint revolute robotic manipulator, where the head is the end effector. We found that the simulator was able to replicate the leans illusion with the same quality when the head position was changed. Obtained results can help to choose the kinematic characteristics of an actual SD simulator, and could be useful in developing an illusion at novel flight profile. [ABSTRACT FROM AUTHOR]

• Developed a new objective method of assessing driving simulator fidelity. • Showed that the method is sensitive to different simulator configurations. • A large motion base can be effective for reproducing real world low mu conditions. • Proper representation of lateral friction is critical to reproduce low mu conditions. Driving simulators can be used to test vehicle designs earlier, prior to building physical prototypes. One area of particular interest is winter testing since testing is limited to specific times of year and specific regions in the world. To ensure that the simulator is fit for purpose, an objective assessment is required. In this study a simulator and real world comparison was performed with three simulator configurations (standard, no steering torque, no motion) to assess the ability of a utility triplet of analyses to be able to quantify the differences between the real world and the different simulator configurations. The results suggest that the utility triplet is effective in measuring the differences in simulator configurations and that the developed "Virtual Sweden" environment achieved rather good behavioural fidelity in the sense of preserving absolute levels of many measures of behaviour. The main limitation in the simulated environment seemed to be the poor match of the dynamic lateral friction limit on snow and ice when compared to the real world. [ABSTRACT FROM AUTHOR]

Highlights • We propose a method to for deriving kinematic requirements of a driving simulator. • An optimized motion is calculated by using a MCA based on Model Predictive Control. • Kinematic requirements correspond to extreme positions, velocities and accelerations. • Large lateral, longitudinal ranges are needed to replicate two pre-recorded maneuvers. • Large yaw range, velocity and acceleration are needed. Abstract In motion-base driving simulators, the simulator kinematic design is a critical factor for achieving a desired simulation fidelity. This paper presents a method to derive kinematic requirements that a theoretical simulator should meet to best replicate reference maneuvers within a predefined workspace. This method uses a Motion Cueing Algorithm based on Model Predictive Control to calculate the optimal motion of the theoretical simulator. Then, extreme positions, velocities and accelerations of the theoretical simulator are used to derive kinematic requirements. The method was applied to two maneuvers previously recorded in a real car. The quality of the resulting simulator motion and kinematic requirements were evaluated for different workspaces. Results showed that the theoretical simulator was able to replicate the two maneuvers with high quality when the lateral and longitudinal ranges were at least 10 m. Interestingly, there was no evidence that a large vertical range improved the quality of the replicated motion. Furthermore, large yaw range, velocity and acceleration were needed for replicating the recorded maneuvers. These results can help to choose the kinematic characteristics of a real driving simulator. [ABSTRACT FROM AUTHOR]

Highlights • A Model Predictive Control (MPC) based Motion Cueing Algorithms (MCA) is implemented. • The MPC predictive feature is exploited to virtually extend the workspace. • A real time implementation is provided, suitable for a 100 Hz control frequency. Abstract The use of dynamic driving simulators is nowadays common practice in the automotive industry. The effectiveness of such devices is strongly related to their capabilities of well reproducing the driving sensations, hence it is crucial that the motion control strategies generate both realistic and feasible inputs to the platform. Such strategies are called Motion Cueing Algorithms (MCAs). Model Predictive Control (MPC) has been successfully applied to MCAs, being well suited to solve constrained optimal control problems. However, the predictive aspect of the algorithm has not been exploited effectively yet, mainly due to the hard real-time requirement when using a significantly long prediction window. In this paper, a real time implementation of the so called Look-Ahead (LA) strategy is presented, that is based on an effective manipulation of the reference along the prediction horizon, and on an on-line switching policy to a Non-Look-Ahead strategy when the expected driver behavior is not reliable. An optimal tuning of the MCA is computed by means of a multi-objective optimization, where both performance improvement due to the prediction exploitation, and robustness to varying driver behavior are taken into account. Finally, a characterization of the driver skill level is proposed and validated in experimental environment. [ABSTRACT FROM AUTHOR]

Highlights • Optimization-based cueing algorithms show improvements w.r.t. filter-based cueing. • Continuous and overall rating result in similar perceived motion mismatch measures. • Roundabout simulations remain difficult even with optimization-based cueing. • Small detectable rotation errors are preferred over large scaling errors. • Cueing mechanism (e.g. prepositioning, scaling) usage per algorithm was identified. Abstract This paper describes a driving simulation experiment, executed on the Daimler Driving Simulator (DDS), in which a filter-based and an optimization-based motion cueing algorithm (MCA) were compared using a newly developed motion cueing quality rating method. The goal of the comparison was to investigate whether optimization-based MCAs have, compared to filter-based approaches, the potential to improve the quality of motion simulations. The paper describes the two algorithms, discusses their strengths and weaknesses and describes the experimental methods and results. The MCAs were compared in an experiment where 18 participants rated the perceived motion mismatch, i.e., the perceived mismatch between the motion felt in the simulator and the motion one would expect from a drive in a real car. The results show that the quality of the motion cueing was rated better for the optimization-based MCA than for the filter-based MCA, indicating that there exists a potential to improve the quality of the motion simulation with optimization-based methods. Furthermore, it was shown that the rating method provides reliable and repeatable results within and between participants, which further establishes the utility of the method. [ABSTRACT FROM AUTHOR]

The use of robotic motion platforms in vehicle simulators is relatively common. However, the process of testing and tuning the so-called washout algorithms, used for motion cueing generation in motion-based vehicle simulators, is complex. This process can be reduced in cost, simplified, improved, shortened and performed safer if virtual motion platforms are used instead of real devices. This paper deals with identifying a method to perform a fast but reliable simulation of parallel mechanisms to be used for motion cueing generation. The method relies on the use of Laplacian polynomial transfer function models by means of using AM-FM bi-modulated signals as reference inputs to achieve system identification. The proposed simulation scheme is implemented and assessed using optical tracking methods by testing a real 2-DOF pitch-roll parallel robot, although the method is not intrinsically restricted to this type of device. The performance of the proposed solution is also assessed with the classical washout algorithm. Results show that the accuracy of the method is satisfactory and the performance of the proposed simulation scheme is very high, being suitable to test and tune washout algorithms. [ABSTRACT FROM AUTHOR]

Most state-of-the-art works in trajectory forecasting for automotive target predicting the pose and orientation of the agents in the scene. This represents a particularly useful problem, for instance in autonomous driving, but it does not cover a spectrum of applications in control and simulation that require information on vehicle dynamics features other than pose and orientation. Also, multi-step dynamic simulation of complex multibody models does not seem to be a viable solution for real-time long-term prediction, due to the high computational time required. To bridge this gap, we present a deep-learning framework to model and predict the evolution of the coupled driver-vehicle system dynamics jointly on a complex road geometry. It consists of two components. The first, a neural network predictor, is based on Long Short-Term Memory autoencoders and fuses the information on the road geometry and the past driver-vehicle system dynamics to produce context-aware predictions. The second, a Bayesian optimiser, is proposed to tune some significant hyperparameters of the network. These govern the network complexity, as well as the features importance. The result is a self-tunable framework with real-time applicability, which allows the user to specify the features of interest. The approach has been validated with a case study centred on motion cueing algorithms, using a dataset collected during test sessions of a non-professional driver on a dynamic driving simulator. A 3D track with complex geometry has been employed as driving environment to render the prediction task challenging. Finally, the robustness of the neural network to changes in the driver and track was investigated to set guidelines for future works. • Deep learning can be used to forecast complex driver-vehicle dynamics jointly. • 3D road geometries can be encoded using Long Short-Term Memory autoencoders. • Neural networks can be faster than multibody models in long-term vehicle simulation. • Bayesian optimisation can balance model complexity for real-time applicability. [ABSTRACT FROM AUTHOR]

The use ofdynamicdriving simulators is constantly increasing in the automotive community, with applications ranging from vehicle development to rehab and driver training. The effectiveness of such devices is related to their capabilities of well reproducing the driving sensations, hence it is crucial that the motion control strategies generate bothrealisticandfeasibleinputs to the platform. Such strategies are called motion cueing algorithms (MCAs). In recent years several MCAs based on model predictive control (MPC) techniques have been proposed. The main drawback associated with the use of MPC is its computational burden, that may limit their application to high performance dynamic simulators. In the paper, a fast, real-time implementation of an MPC-based MCA for 9 DOF, high performance platform is proposed. Effectiveness of the approach in managing the available working area is illustrated by presenting experimental results from an implementation on a real device with a 200 Hz control frequency. [ABSTRACT FROM PUBLISHER]

The majority of motion cueing algorithms have been developed for passenger car applications, with correspondingly less research dedicated to race-car and high-performance vehicle simulators. In the high-performance context, the focus is on cueing the vehicle's behavioural and handling characteristics, particularly when driving near the limits of performance. In race-car simulators the cueing requirements are therefore quite different, with the problem made all the more challenging by the presence of large accelerations. To understand the drivers' cueing needs, the vehicle's stability and handling response characteristics must be examined near the performance boundary. Frozen-time eigenvalue analyses are used to determine stability and response characteristics across all vehicle operating conditions, including accelerating and braking under cornering, with the results used to determine motion cueing algorithm requirements. Lateral acceleration and yaw cueing filters are designed in order to retain information critical to understanding the vehicle's behaviour on its performance boundary. Cueing filters are then tested, with the help of a professional race-car driver, and are found to provide the cues necessary for the driver to control the vehicle on the limit of performance. [ABSTRACT FROM PUBLISHER]

Real road vehicle tests are time consuming, laborious, and costly, and involve several safety concerns. Road vehicle motion simulators (RVMS) could assist with vehicle testing, and eliminate or reduce the difficulties traditionally associated with conducting vehicle tests. However, such simulators must exhibit a high level of fidelity and accuracy in order to provide realistic and reliable outcomes. In this paper, we review existing RVMS and discuss each of the major RVMS subsystems related to the research and development of vehicle dynamics. The possibility of utilising motion simulators to conduct ride and handling test scenarios is also investigated. [ABSTRACT FROM AUTHOR]

Abstract: To pursue a small target moving in front of a drifting background, motion vectors from the target need to be integrated and segmented from those belonging to the background. Smooth pursuit eye movements typically integrate target and background directions initially and after some time shift towards the veridical target direction. The perceived target direction on the other hand is generally stable over time: the target is perceived to move in the same direction as long as the motion information maintains the same properties over time. If illusory target motion is observed, this tends to be shifted away from the background. Here we investigated how initial motion integration and segmentation of such stimuli are modulated by direction cues. We presented a small pursuit target moving along a straight path, in front of a background moving in a different direction. Without a direction cue, initial pursuit was biased towards the background direction before shifting towards the veridical target direction. The target’s perceived direction on the other hand was near veridical. A cue in the background direction increased initial pursuit integration but also caused perception to behave in a similar way: the target initially had an illusory motion component in the background direction and after about 200ms it was perceived to curve towards its veridical direction. This illusion shows that during the initial process of segmenting the direction of a pursuit target from irrelevant background motion, both pursuit and perception can be erroneously influenced by a direction cue and integrate the cued background motion. Both modalities corrected this initial integration error as more information about the target became available. [Copyright &y& Elsevier]

A paired comparison experiment using 23 subjects was run on the VIRTTEX driving simulator to compare a lane position based motion drive algorithm (MDA) with a classical MDA for a highway speed, lane change manoeuvre. Two different tuning states of the lane position algorithm and four different tuning states for the classical algorithm were tested. The subjective fidelity of the six different motion cases was compared with each other and a Bradley-Terry model was fit to find the fidelity merit of each case. In addition, the driving performance of the subjects for six motion cases was recorded and compared. The motion-tuning cases were selected such that the trade-off in motion quality between overall motion scaling and motion shape distortion (shape-error), as well as the trade-off between lateral specific force and roll-rate motion errors, could be studied. It was found that when the overall scaling is the same, drivers perform better with the lane position algorithm than with the the classical algorithm. A well-tuned, manoeuvre-specific, classical MDA, however, did achieve a subjective fidelity level on a par with the lane position MDA. A generically tuned classical MDA, however, has a significantly reduced fidelity and driving performance when compared with a lane position algorithm with the same scale factor. A strong trade-off between motion shape-errors and overall motion scaling was found. A small increase in motion cue shape-error, combined with an increase in the scale factor from 0.3 to 0.5, led to improved performance and increased subjective fidelity. The results of the experiment also suggest that simulator motion can be improved by reducing the angular-rate shape-error at the expense of the specific force shape-error (while keeping the total normalised shape-error constant). [ABSTRACT FROM AUTHOR]

Abstract: This study presents a novel motion-cueing strategy, which is applied to a motion simulator with three rotational degrees of freedom (DOF) to perform the roll, pitch, yaw, surge, and sway motions by using an online optimization algorithm. The weighting functions are adaptively tuned in each step, and the optimal Euler angles are obtained analytically. This motion-cueing algorithm is efficient since it requires no recursive search on the optimal solution. Experiments demonstrating the validity of the 5-DOF motion simulation are also included. [Copyright &y& Elsevier]

This paper presents a driving simulation whose aim is twofold: 1) to investigate the possibility of reducing motion clearance to achieve compact and low-cost driving simulators and 2) to evaluate multimodal and immersive virtual reality motion restitution in platooning driving. The choice has been made for a driving simulator having at least two degrees of freedom (DOF). These consist of the longitudinal displacement and seat rotations. The simulator is also equipped with a force feedback steering wheel for virtual drive assistance. These components are gathered on a serial kinematics-type platform to facilitate a control scheme and avoid the architecture complexity. A comparative study was made to devise a motion cueing strategy, taking into account both the psychophysical and technological constraints. Experimentations were carried out for several case combinations of the longitudinal displacement and seat rotations. [ABSTRACT FROM AUTHOR]

The restricted operational space of dynamic driving simulators requires the implementation of motion cueing algorithms that tilt the simulator cabin to reproduce sustained accelerations. In order to avoid conflicting inertial cues, the tilt rate is limited below drivers' perceptual thresholds, which are typically derived from the results of classical vestibular research, where additional sensory cues to self-motion are removed. These limits might be too conservative for an ecological driving simulation, which provides a variety of complex visual and vestibular cues as well as demands of attention which vary with task difficulty. We measured roll rate detection threshold in active driving simulation, where visual and vestibular stimuli are provided as well as increased cognitive load from the driving task. Here thresholds during active driving are compared with tilt rate detection thresholds found in the literature (passive thresholds) to assess the effect of the driving task. In a second experiment, these thresholds (active versus passive) are related to driving preferences in a slalom driving course in order to determine which roll rate values are most appropriate for driving simulators so as to present the most realistic driving experience. The results show that detection threshold for roll in an active driving task is significantly higher than the limits currently used in motion cueing algorithms, suggesting that higher tilt limits can be successfully implemented to better optimize simulator operational space. Supra-threshold roll rates in the slalom task are also rated as more realistic. Overall, our findings indicate that increasing task complexity in driving simulation can decrease motion sensitivity allowing for further expansion of the virtual workspace environment. [ABSTRACT FROM AUTHOR]