This paper presents a three-step validation approach for subjective rating predictions of driving simulator motion incongruences based on objective mismatches between reference vehicle and simulator motion. This approach relies on using high-resolution rating predictions of open-loop driving (participants being driven) for ratings of motion in closed-loop driving (participants driving themselves). A driving simulator experiment in an urban scenario is described, of which the rating data of 36 participants was recorded and analyzed. In the experiment's first phase, participants actively drove themselves (i.e., closed-loop). By recording the drives of the participants and playing these back to themselves (open-loop) in the second phase, participants experienced the same motion in both phases. Participants rated the motion after each maneuver and at the end of each drive. In the third phase they again drove open-loop, but rated the motion continuously, only possible in open-loop driving. Results show that a rating model, acquired through a different experiment, can well predict the measured continuous ratings. Second, the maximum of the measured continuous ratings correlates to both the maneuver-based (ρ =0.94) and overall (ρ =0.69) ratings, allowing for predictions of both rating types based on the continuous rating model. Third, using Bayesian statistics it is then shown that both the maneuver-based and overall ratings between the closed-loop and open-loop drives are equivalent. This allows for predictions of maneuver-based and overall ratings using the high-resolution continuous rating models. These predictions can be used as an accurate trade-off method of motion cueing settings of future closed-loop driving simulator experiments.

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.

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.

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.

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.

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.