Comparison between filter- and optimization-based motion cueing algorithms for driving simulation

Cleij, Diane; Venrooij, Joost; Pretto, Paolo; Katliar, Mikhail; Bülthoff, Heinrich H.; Steffen, Dennis; Hoffmeyer, F. W.; Schöner, Hans-Peter

doi:10.1016/j.trf.2017.04.005

Cited by 17 publications

(10 citation statements)

References 6 publications

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“…The analysis of the indicators gives a very important and quantifiable measurement of motion cueing quality. Similarly to a previous study, 15 an analysis of motion cueing mechanisms provides insight to understand how the cueing quality is affected by the prediction strategy. The motion cueing mechanisms that will be analyzed in this study are presented below.…”

Section: Motion Cueing Mechanismsmentioning

confidence: 93%

“…Therefore an optimization of the weights is performed with the aim of finding the best possible set for each prediction strategy. The weights are initialized with values taken from literature 28,15 and reported in Table 3, where the different order of magnitude is to account for the ratio between specific forces (in m/s 2 ) and angular velocities (in rad/s) for typical maneuvers. 28 The simulator motion obtained with oracle and constant strategies for the entire sequence of maneuvers is used to compute the error with respect to the vehicle motion.…”

Section: Methodsmentioning

confidence: 99%

“…This mechanism can be interpreted as the equivalent of prepositioning in velocity and it has been first identified in previous studies. 15 The simulator is moved at a certain velocity in opposite direction with respect to the one needed. At the same time the perceived motion is compensated by tilt coordination, which compensates for the acceleration that is used to generate the linear velocity.…”

Section: Methodsmentioning

confidence: 99%

“…The predictive MCA has shown multiple advantages with respect to the classical algorithm, both in terms of workspace usage and subjective evaluation, when the vehicle trajectory is completely known in advance (passive driving). 15 However, other challenges are introduced with the usage of predictive MCA. Compared with the classical MCA, this algorithm requires more computational time to solve an optimization problem at each time step of the algorithm, making it more challenging to adopt for more common driver-in-the-loop (DIL) simulations (active driving).…”

Section: Introductionmentioning

confidence: 99%

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Objective evaluation of prediction strategies for optimization-based motion cueing

et al. 2018

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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.

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Section: Motion Cueing Mechanismsmentioning

confidence: 93%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Objective evaluation of prediction strategies for optimization-based motion cueing

et al. 2018

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“…• A helicopter in [159] • A car in [27] • A truck in [176] The recommended washout algorithm for the sailing boat simulator has to present higher flexibility and better results. Even if the Adaptive Algorithm is more computationally expensive than the others (for each time step it changes some of the filter coefficients in order to minimize a specific cost function), it is the most flexible along with the Classical one (they use a platform-based frame of reference instead of a frame of reference based on the user head position like the Optimal one) and…”

Section: Classical Washoutmentioning

confidence: 99%

Design of a physical and interactive real-time Simulator based on a dynamic VPP as a support tool for sailing yacht design and operation

Sabadell¹

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Objective and subjective responses to motion sickness: the group and the individual

2020

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We investigated and modeled the temporal evolution of motion sickness in a highly dynamic sickening drive. Slalom maneuvers were performed in a passenger vehicle, resulting in lateral accelerations of 0.4 g at 0.2 Hz, to which participants were subjected as passengers for up to 30 min. Subjective motion sickness was recorded throughout the sickening drive using the MISC scale. In addition, physiological and postural responses were evaluated by recording head roll, galvanic skin response (GSR) and electrocardiography (ECG). Experiment 1 compared external vision (normal view through front and side car windows) to internal vision (obscured view through front and side windows). Experiment 2 tested hypersensitivity with a second exposure a few minutes after the first drive and tested repeatability of individuals’ sickness responses by measuring these two exposures three times in three successive sessions. An adapted form of Oman’s model of nausea was used to quantify sickness development, repeatability, and motion sickness hypersensitivity at an individual level. Internal vision was more sickening compared to external vision with a higher mean MISC (4.2 vs. 2.3), a higher MISC rate (0.59 vs. 0.10 min−1) and more dropouts (66% vs. 33%) for whom the experiment was terminated due to reaching a MISC level of 7 (moderate nausea). The adapted Oman model successfully captured the development of sickness, with a mean model error, including the decay during rest and hypersensitivity upon further exposure, of 11.3%. Importantly, we note that knowledge of an individuals’ previous motion sickness response to sickening stimuli increases individual modeling accuracy by a factor of 2 when compared to group-based modeling, indicating individual repeatability. Head roll did not vary significantly with motion sickness. ECG varied slightly with motion sickness and time. GSR clearly varied with motion sickness, where the tonic and phasic GSR increased 42.5% and 90%, respectively, above baseline at high MISC levels, but GSR also increased in time independent of motion sickness, accompanied with substantial scatter.

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Comparison between filter- and optimization-based motion cueing algorithms for driving simulation

Cited by 17 publications

References 6 publications

Objective evaluation of prediction strategies for optimization-based motion cueing

Objective evaluation of prediction strategies for optimization-based motion cueing

Design of a physical and interactive real-time Simulator based on a dynamic VPP as a support tool for sailing yacht design and operation

Objective and subjective responses to motion sickness: the group and the individual

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