Mitigating Motion Sickness in Automated Vehicles with Vibration Cue System

<p>Motion sickness is very common in road transport. To guarantee ride comfort and user experience, there is an urgent need for effective solutions to motion sickness mitigation in semi- and fully-automated vehicles. Considering both effectiveness and user-friendliness, a vibration cue system is proposed to inform passengers of the upcoming vehicle movement through tactile stimulation. By integrating the motion planning results from automated driving algorithms, the vibration cueing timing and patterns are optimized with the theory of motion anticipation. Using a cushion-based prototype of vibration cue system, 20 participants were invited to evaluate this solution in two conditions of driving simulator experiments. Results show that with the proposed vibration cue system, it could also help participants to comprehend the cues and to generate motion anticipation. The participants’ motion sickness degrees were significantly lowered. This research may serve as one foundation for the detailed system development in practical applications.</p><p>(This article has been accepted for publication in <i>Ergonomics</i>, published by Taylor & Francis.)</p><br>

Chewing gum reduces visually induced motion sickness

Visually induced motion sickness (VIMS) is a common side-effect of exposure to virtual reality (VR). Its unpleasant symptoms may limit the acceptance of VR technologies for training or clinical purposes. Mechanical stimulation of the mastoid and diverting attention to pleasant stimuli-like odors or music have been found to ameliorate VIMS. Chewing gum combines both in an easy-to-administer fashion and should thus be an effective countermeasure against VIMS. Our study investigated whether gustatory-motor stimulation by chewing gum leads to a reduction of VIMS symptoms. 77 subjects were assigned to three experimental groups (control, peppermint gum, and ginger gum) and completed a 15-min virtual helicopter flight, using a VR head-mounted display. Before and after VR exposure, we assessed VIMS with the Simulator Sickness Questionnaire (SSQ), and during the virtual flight once every minute with the Fast Motion Sickness Scale (FMS). Chewing gum (peppermint gum: M = 2.44, SD = 2.67; ginger gum: M = 2.57, SD = 3.30) reduced the peak FMS scores by 2.05 (SE = 0.76) points as compared with the control group (M = 4.56, SD = 3.52), p < 0.01, d = 0.65. Additionally, taste ratings correlated slightly negatively with both the SSQ and the peak FMS scores, suggesting that pleasant taste of the chewing gum is associated with less VIMS. Thus, chewing gum may be useful as an affordable, accepted, and easy-to-access way to mitigate VIMS in numerous applications like education or training. Possible mechanisms behind the effect are discussed.

Effects of Wheel Rotation on Long-Period Wake Dynamics of the DrivAer Fastback Model

Lattice Boltzmann method (LBM) simulations were performed to capture the long-period dynamics within the wake of a realistic DrivAer fastback model with stationary and rotating wheels. The simulations showed that the wake developed as a low-pressure torus regardless of whether the wheels were rotating. This torus shrank in size on the base in the case of rotating wheels, leading to a reduction in the low-pressure footprint on the base, and consequently a 7% decrease in the total vehicle drag in comparison to the stationary wheels case. Furthermore, the lateral vortex shedding experienced a long-period switching associated with the bi-stability in both the stationary and rotating wheels cases. This bi-stability contributed to low-frequency side force oscillations (<1 Hz) in alignment with the peak motion-sickness-inducing frequency (0.2 Hz).

Dizziness Control in Virtual Reality by Vibration Stimulation in Mastoid

Background and Objectives Virtual reality (VR) users have prevalently experienced motion sickness called cybersickness. Recently, it has been suggested that stimulating the mastoid by vibration relieves cybersickness. This study aimed to verify this proposition.Subjects and Method Fifty-four young male adults (aged 18 to 27 years) without any experience of severe motion sickness or cybersickness participated in this study. Participants were divided in half into two groups, the experimental group and control group. The experimental group used VR with mastoid vibration, and the control group experienced VR without mastoid vibration. Participants responded to the simulator sickness questionnaire (SSQ) to quantify cybersickness.Results The total severity scores of cybersickness in the experimental group ranged from 0 to 183.3 with the mean value of 46.7±49.0. The total severity scores of cybersickness in the control group ranged from 0 to 194.9 with the mean value of 44.9±45.1. There were no significant differences between the two groups.Conclusion There was no improvement of cybersickness in the VR participants when the mastoid was stimulated by vibration. However, cybersickness might be relieved with changes in the VR condition or vibration settings.

Simulation and Virtual Reality Using Nonlinear Kinematic Parameters as a Means of Predicting Motion Sickness in Real-Time in Virtual Environments

Objective This article presents two studies (one simulation and one pilot) that assess a custom computer algorithm designed to predict motion sickness in real-time. Background Virtual reality has a wide range of applications; however, many users experience visually induced motion sickness. Previous research has demonstrated that changes in kinematic (behavioral) parameters are predictive of motion sickness. However, there has not been research demonstrating that these measures can be utilized in real-time applications. Method Two studies were performed to assess an algorithm designed to predict motion sickness in real-time. Study 1 was a simulation study that used data from Smart et al. (2014). Study 2 employed the algorithm on 28 new participants’ motion while exposed to virtual motion. Results Study 1 revealed that the algorithm was able to classify motion sick participants with 100% accuracy. Study 2 revealed that the algorithm could predict if a participant would become motion sick with 57% accuracy. Conclusion The results of the present study suggest that the motion sickness prediction algorithm can predict if an individual will experience motion sickness but needs further refinement to improve performance. Application The algorithm could be used for a wide array of VR devices to predict likelihood of motion sickness with enough time to intervene.