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. Abstract This paper deals with driving simulation and in particular with the important issue of motion sickness. The paper proposes a methodology to evaluate the objective illness rating metrics deduced from the motion sickness dose value and questionnaires for both a static simulator and a dynamic simulator. Accelerations of the vestibular cues (head movements) of the subjects were recorded with and without motion platform activation. In order to compare user experiences in both cases, the head-dynamics-related illness ratings were computed from the obtained accelerations and the motion sickness dose values. For the subjective analysis, the principal component analysis method was used to determine the conflict between the subjective assessment in the static condition and that in the dynamic condition. The principal component analysis method used for the subjective evaluation showed a consistent difference between the answers given in the sickness questionnaire for the static platform case from those for the dynamic platform case. The two-tailed Mann-Whitney U test shows the significance in the differences between the self-reports to the individual questions. According to the two-tailed Mann-Whitney U test, experiencing nausea (p = 0.019 \ 0.05) and dizziness (p = 0.018 \ 0.05) decreased significantly from the static case to the dynamic case. Also, eye strain (p = 0.047 \ 0.05) and tiredness (p = 0.047 \ 0.05) were reduced significantly from the static case to the dynamic case. For the perception fidelity analysis, the Pearson correlation with a confidence interval of 95% was used to study the correlations of each question with the x illness rating component IR x , the y illness rating component IR y , the z illness rating component IR z and the compound illness rating IR tot . The results showed that the longitudinal head dynamics were the main element that induced discomfort for the static platform, whereas vertical head movements were the main factor to provoke discomfort for the dynamic platform case. Also, for the dynamic platform, lateral vestibular-level dynamics were the major element which caused a feeling of fear.
This paper deals with the effects of different washout algorithms used for Stewart platforms on subjective and objective ratings. Washout algorithms are used to represent vehicle dynamics in a restricted spatial place. An adaptive washout algorithm was realized to control the hexapod platform, depending on the specific force error in longitudinal, lateral and vertical directions, in order to compare user’s experience with those in the case of classical algorithm. In this study, the simulator sickness has been evaluated for three algorithms in dynamic driving simulator situation in objective and subjective way.
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.
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