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

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When designing driving simulation experiments with motion cueing, it is often necessary to make choices between Motion Cueing Algorithms (MCAs) without being fully able to know how well an MCA will perform during the experiment. Choices between MCAs can therefore be greatly supported by previous measurements or predictions of motion cueing quality. This paper describes a data collection experiment on a nine degree-of-freedom motion-base simulator, in which participants are asked to continuously rate the motion cueing quality during a pre-recorded drive through an urban environment. Three benchmark MCAs are compared: a Model-Predictive Control (MPC) algorithm with infinite prediction horizon, a Classical Washout Algorithm (CWA) tuned for the use-case, and the same algorithm (CWA), but with the tilt-coordination channels turned off. By comparing ratings for the whole scenario, as well as ratings for each maneuver individually, the results show a preference of the presence of tilt-coordination, as well as a preference for the optimization-based MPC algorithm over the CWA condition. The collected data will be used directly for modeling and predicting motion cueing quality for future experiments at BMW, such that the best-suited MCA and parameter setting can be selected before experiments.@en

When designing driving simulation experiments with motion cueing, it is often necessary to make choices between Motion Cueing Algorithms (MCAs) without being fully able to know how well an MCA will perform during the experiment. Choices between MCAs can therefore be greatly supported by previous measurements or predictions of motion cueing quality. This paper describes a data collection experiment on a nine degree-of-freedom motion-base simulator, in which participants are asked to continuously rate the motion cueing quality during a pre-recorded drive through an urban environment. Three benchmark MCAs are compared: a Model-Predictive Control (MPC) algorithm with infinite prediction horizon, a Classical Washout Algorithm (CWA) tuned for the use-case, and the same algorithm (CWA), but with the tilt-coordination channels turned off. By comparing ratings for the whole scenario, as well as ratings for each maneuver individually, the results show a preference of the presence of tilt-coordination, as well as a preference for the optimization-based MPC algorithm over the CWA condition. The collected data will be used directly for modeling and predicting motion cueing quality for future experiments at BMW, such that the best-suited MCA and parameter setting can be selected before experiments.

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Motion cueing algorithm design often involves a trade-off between priorities due to the limited workspace of the simulator. Such a trade-off requires a detailed understanding of human perception, which we do not yet have. For that reason, objective motion cueing quality metrics, based on the difference between vehicle and simulator signals, offer a fast and simple alternative. Next to motion cueing quality, we argue that the total motion cueing algorithm (MCA) quality is about more than only the quality of the motion, and can also entail implementation and operational aspects of an MCA for a specific use-case and simulator combination, i.e., it is a task-dependent issue. In this paper this idea is discussed by comparing three objective motion cueing quality metrics (absolute difference, delay and cross-correlation) from literature and two metrics regarding simulator operations (workspace management and energy consumption). Comparing such metrics is difficult, but is nevertheless useful to improve the process of simulator operations if various MCAs and/or simulators are available, to aid their selection pro- cess. As a first step towards such a method, a Virtual Test Environment (VTE) was developed as a versatile software environment to compare these metrics, as well as to visualize simulator motion and its characteristics in a 3D-animation. This aims at helping MCA designers in making choices between different MCA types, their configurations, simulators and use-cases, guiding them to select the best-suited motion cueing solution. @en

A necessity in driving simulation testing is to understand and attenuate simulator sickness, to reduce the number of undesired drop-outs. Especially urban environments, with its many turns and changes in the velocity profile, are a challenge. This paper describes the motion sickness rating results of a between-subjects experiment (n = 63), which investigated the effects of adding scaled yaw motion to a simulator on the sickness incidence and severity while being driven as passenger through an urban environment. Three cases were considered: no motion, scaled yaw motion, and including the vehicle pitch and roll rotations in addition to the scaled yaw motion. The misery scale (MISC) was obtained every minute, and the simulator sickness questionnaire (SSQ) was completed before and after the 45-minute trial. Experimental results show that less participants became sick when some form of yaw-motion was provided. @en

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.

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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.

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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.@en