Influence of a new discrete‐time LQR‐based motion cueing on driving simulator

Abstract

is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. SUMMARYThis study proposes a method and an experimental validation to analyze dynamics response of the simulator's cabin and platform with respect to the type of the control used in the hexapod driving simulator. In this article, two different forms of motion platform tracking control are performed as a classical motion cueing algorithm and a discrete-time linear quadratic regulator (LQR) motion cueing algorithm. For each situation, vehicle dynamics and motion platform level data are registered from the driving simulation software. In addition, the natural frequencies of the roll accelerations are obtained in real-time by using FFT. The data are denoised by using wavelet 1D transformation. The results show that by using discrete-time LQR algorithm, the roll acceleration amplitudes that correspond to the natural frequencies and the total roll jerk have decreased at the motion platform level. Also, the natural frequencies have increased reasonably by using the discrete LQR motion cueing (1.5-2.2 Hz) compared with using the classical algorithm (0.4-1.5 Hz) at the motion platform, which is an indicator of motion sickness incidence avoidance. The literature shows that lateral motion (roll, yaw, etc.) in the frequency range of 0.1-0.5 Hz induces motion sickness. Furthermore, using discrete-time LQR motion cueing algorithm has decreased the sensation error (motion platform-vehicle (cabin) levels) two times in terms of total roll jerk. In conclusion, discrete-time LQR motion cueing has reduced the simulator sickness more than the classical motion cueing algorithm depending on sensory cue conflict theory.

Effect of Closed-Loop Motion Cueing Algorithm for a Six-Degrees-of-Freedom Dynamic Simulator on Pupil Diameter as a Driver Stress Factor

“…This broadly observed effect is used in estimating mental workload in human machine interaction [15, 36,37,38,39,40,56]. We also applied the novel method as of wavelet transform to decrease the data complexity for the registered signals [44,45,46] by removing the noise from the high-frequent part of the signal. In Figure 8, the PD change of the driver participants (N = 28) (in millimeters) who participated in the driving simulator experiments was illustrated.…”

“…The motion cueing algorithm, which was a coupling of the two different algorithms (model reference adaptive control (MRAC) and linear quadratic regulator (LQR), seen in Figure 3) explained in [5,6,46,50], was included in the SCANeRstudio ® driving simulation software via dll plugin in order to accomplish the real-time driving experiments with the participations of the subjects.…”

“…One promising technique for reducing data complexity in recorded signals is wavelet analysis [44,45,46]. It was proposed for the use of wavelet analysis for studying PD time series and related to the happening of high-frequency changes (i.e., higher than 50 Hz) to instances of cognitive pressure.…”

A meta-analysis of simulator sickness as a function of simulator fidelity

“…Thus, the total score can range between 0 and 235.62 (i.e., 3 × (3 × 7) × 3.74 ). Other studies instead used either the Motion Sickness Assessment Questionnaire (MSAQ, Gianaros et al (2001); 1/41), which has a range of 16-144 on the total score that is then converted to a percentage; a Motion Sickness Questionnaire, which ranges between 0 and 78 (MSQ, Frank et al (1983); 1/41); the Fast Motion Sickness scale, ranging between 0 and 20 (FMS, Keshavarz and Hecht (2011); 3/41), or some variation of a magnitude estimation (ME) scale (e.g., (Hartfiel and Stark 2019;Aykent et al 2014); 5/41). Because two studies used both the SSQ and the FMS (Sawada et al 2020;Keshavarz and Hecht 2012), there are 43 methods for 41 studies.…”

“…Visualization modality We distinguish between four types of visualization modalities. Out of all studies, 20 used monitors, in 36 conditions (Zou et al 2021;Gemonet et al 2021;Almallah et al 2021;Parduzi et al 2020;Suwarno et al 2019;Hohorst et al 2019;Walch et al 2017;Romano et al 2016;Bridgeman et al 2014;Park et al 2005;Sekar et al 2020;Parduzi et al 2019;Ujike and Watanabe 2011;Garcia et al 2010;Stelling et al 2020;Somrak et al 2019;Mittelstaedt et al 2018;Häkkinen et al 2006;Klüver et al 2016); 16 studies used projection systems, in 54 conditions (Talsma et al 2022;Jurisch et al 2020;Benz et al 2019;Weidner et al 2017;Aykent et al 2014;Park et al 2005;Lin et al 2002b;Colombet et al 2016;Parduzi et al 2019;Will et al 2017;Schmieder et al 2017;Keshavarz and Hecht 2012;Boustila et al 2017;Emoto et al 2008;Damveld et al 2010;Klüver et al 2016); 21 studies used HMDs, in 54 conditions (Zou et al 2021;Sawada et al 2020;Parduzi et al 2020;Suwarno et al 2019;…”