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

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

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