Humans always wanted to go faster and higher than their own legs could carry them, leading them to invent numerous types of vehicles to move fast over land, water and air. As training how to handle such vehicles and testing new developments can be dangerous and costly, vehicle motion simulators were invented. Motion-based simulators in particular, combine visual and physical motion cues to provide occupants with a feeling of being in the real vehicle. While visual cues are generally not limited in amplitude, physical cues certainly are, due to the limited simulator motion space. A motion cueing algorithm (MCA) is used to map the vehicle motions onto the simulator motion space. This mapping inherently creates mismatches between the visual and physical motion cues. Due to imperfections in the human perceptual system, not all visual/physical cueing mismatches are perceived. However, if a mismatch is perceived, it can impair the simulation realism and even cause simulator sickness. For MCA design, a good understanding of when mismatches are perceived, and ways to prevent these from occurring, are therefore essential. In this thesis a data-driven approach, using continuous subjective measures of the time-varying Perceived Motion Incongruence (PMI), is adopted. PMI in this case refers to the effect that perceived mismatches between visual and physical motion cues have on the resulting simulator realism. The main goal of this thesis was to develop an MCA-independent off-line prediction method for time-varying PMI during vehicle motion simulation, with the aim of improving motion cueing quality. To this end, a complete roadmap, describing how to measure and model PMI and how to apply such models to predict and minimize PMI in motion simulations is presented. Results from several human-in-the-loop experiments are used to demonstrate the potential of this novel approach. @en

Despite gaining popularity, the use of Motion Cueing Algorithms (MCAs) based on Model Predictive Control (MPC) remains challenging due to the required tuning of a large number of cost function parameters. This paper investigates the effects of two critical MPC cost function parameters, the lateral specific force and roll rate error weights (Way and Wp), on the motion cueing quality achieved with an MPC-based MCA for a curve driving scenario. An offline sensitivity analysis, which quantified the effects of varying Way and Wp on the Root Mean Square Error (RMSE) and Pearson Correlation Coefficient (PCC) of the resulting simulator motion outputs, shows that for the same percentage-wise variation, Way has a more pronounced effect on both cueing quality predictors than Wp. In addition, for both RMSE and PCC, the effects of Way and Wp are also found to be largely independent, i.e., without interaction effects. This was further tested in a passive human-in-the-loop experiment with 20 participants and with nine different Way and Wp parameter combinations as test conditions, performed in the hexapod moving-base simulator of the Max Planck Institute for Biological Cybernetics in T¨ubingen. The collected continuous rating data, which were found to be reliable for 18/20 participants, show a statistically significant variation across all experiment conditions, and especially a strong interaction effect of Way and Wp. Somewhat surprisingly, the overall lowest continuous ratings were given to the combination of both reference weight settings from earlier research (our baseline condition). In line with the interaction effect in the continuous data, an extended post-experiment correlation analysis shows that a weighted combination of lateral specific force RMSE and and roll rate RMSE above the roll rate perception threshold strongly correlates (_ = 0.98) with the variation in mean continuous ratings across all experiment conditions. This approach can potentially be used for straightforward prediction of perceived motion cueing quality and offline MCA optimization. @en

In this paper the potential of Motion Incongruence Rating (MIR) models for the optimization of Motion Cueing Algorithms (MCAs) is investigated. In a human-in-the-loop simulator experiment, two optimization-based MCAs are compared for a roundabout scenario simulated on a medium-stroke hexapod simulator. The first MCA uses standard cueing error weights from reference literature in its cost function, while for the second case these weights were based on a MIR model fitted to previous experiment data. Results show that such models provide a promising cueing error weight estimation method for optimization-based MCAs, but also highlight the limitations of these models due to, for example, their dependency on the richness of the datasets to which they are fitted. @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.@en

In motion simulation, motion input scaling is often applied to deal with the limited motion envelopes of motion simulators. In this research, the time-varying effects of scaling the lateral specific force up or down during passive curve driving in a car driving simulation are investigated through a simulator experiment. It is concluded that lateral specific force scaling has a time-varying effect on the perceived fidelity of a curve-driving simulation. In particular, motion scaling during a curve entry is found to be less detrimental than motion scaling during a curve's sustained part and during the curve exit.@en

Optimization-based motion cueing algorithms based on model predictive control have been recently implemented to reproduce the motion of a car within the limited workspace of a driving simulator. These algorithms require a reference of the future vehicle motion to compute a prediction of the system response. Assumptions regarding the future reference signals must be made in order to develop effective prediction strategies. However, it remains unclear how the prediction of future vehicle dynamics influences the quality of the motion cueing. In this study two prediction strategies are considered. Oracle: the ideal prediction strategy that knows exactly what the future reference is going to be. Constant: a prediction strategy that ignores every future change and keeps the current vehicle’s linear accelerations and angular velocities constant. The two prediction strategies are used to reproduce a sequence of maneuvers between 0 and 50 km/h. A comparative analysis is carried out to objectively evaluate the influence of the prediction strategies on motion cueing quality. Dedicated indicators of correlation, delay and absolute error are used to compare the effects of the adopted prediction on simulator motion. Also the motion cueing mechanisms adopted by the different conditions are analyzed, together with the usage of simulator workspace. While the constant strategy provided reasonable cueing quality, the results show that knowledge of the future vehicle trajectory reduces the delay and improves correlation with the reference trajectory, it allows the combined usage of different motion cueing mechanisms and increases the usage of workspace.@en

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.@en

Negotiating intersections is a complex driving task that is particularly difficult for older drivers. This task requires accurate coordination of multiple driving subtasks, placing high demands on perception, attention and motor control that are known to decline with age. We analyzed intersection negotiation behavior in an instrumented vehicle and found striking differences in how drivers of different ages synchronize speed and heading control when turning right. The older drivers performed most of their steering while standing still instead of while accelerating as younger drivers do. This shift from parallel to serial control is a compensatory solution that drivers employ in response to age related decline in perception, cognition, and motor control abilities. Serialization of turning at an intersection reduces attentional demands largely by eliminating the need to switch attention between different driving sub-tasks. @en