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

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

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

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

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

The understanding of human responses to visual information in car driving tasks requires the use of system identification tools that put constraints on the design of data collection experiments. Most importantly, multisine perturbation signals are required, including a multisine road geometry, to separately identify the different driver steering responses in the frequency domain. It is as of yet unclear, however, to what extent drivers steer differently along such multisine roads than they do for real roads. This paper presents a method for approximating real-world road geometries with multisine signals, and applies it to a stretch of road used in an earlier investigation into driver steering. In addition, a human-in-the-loop experiment is performed to collect driver steering data for both the realistic real-world road and its multisine approximation. Overall, the analysis of driver performance metrics and driver identification data shows that drivers adopt equivalent control behaviour when steering along both roads. Hence, the use of such multisine approximations allows for the realization of realistic roads and driver behaviour in car driving experiments, in addition to supporting the application of quantitative driver identification techniques for data analysis.

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