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

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

This paper describes how the kinematic configuration of a driving simulator's motion system affects the rendered inertial motion. The specific force and rotational rate equations between the point where the motion is applied (Motion Reference Point (MRP)), and the point in which the driver perceives the motion (Cueing Reference Point (CRP)), are derived for three kinematic configurations: (i) a hexapod, (ii) a hexapod with an xy-drive and a yaw-drive below, and (iii) the same system as (ii), but with the yaw-drive on top. The rotational rate equations show that having a yaw-drive on top greatly complicates the motion control. Furthermore, simulation results show that, regardless of the yaw-drive location, the difference between MRP and CRP becomes noticeable for large yaw-drive excitations. For such driving simulators, the positional offset between MRP and CRP can therefore not be ignored, complicating the motion control.

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BMW’s new driving simulation center operates multiple motion-base simulators – each with a different kinematic configuration – to serve various experiment use-cases and requirements of simulator users. The selection of a simulator for each experiment should ideally be based on their relative strengths and weaknesses. To support this decision-making process, subjective and objective predictions of motion cueing quality can be used. This paper provides an example comparison of four motion-base driving simulators. The kinematic configurations of the simulators considered differed in the additional presence of a yaw-drive and/or a linear xy-drive. The comparison is made by calculating offline, optimization-based motion cueing with perfect prediction capabilities (the ‘Oracle’) for nine urban drives. A prediction of subjective motion incongruence ratings is made for each simulator. In addition, an error type identification method is used (identifying scaling, missing cue, false cue and false direction cue errors) and evaluated per simulator. As Oracle can fully utilize the available workspace, the employed evaluation methods provide an insight in the fundamental capabilities of each simulator. Both the modelled ratings and the error type analysis show the benefits of adding a xy-drive in urban use-cases: predicted ratings reduce by 19% (i.e., better), while scaling and missing cue errors in the yaw rate are reduced when adding a yaw-drive. The presence of both of these additional motion systems allow for practically one-to-one and therefore error-free motion cueing. The proposed methods provide a straight-forward, yet insightful basis for simulator selection. The presented methods can be extended towards the analysis of multiple motion cueing algorithms and/or other usecases for systematically selecting the best-suited motion cueing method. @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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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