Mitigating motion sickness in automated vehicles with vibration cue system

On the road to comfort: Evaluating the influence of motion predictability on motion sickness in automated vehicles

Automated vehicles could increase the risk of motion sickness because occupants are not involved in driving and do not watch the road. This paper aimed to investigate the influence of motion predictability on motion sickness in automated vehicles, as better motion anticipation is believed to mitigate motion sickness. In a simulator-based study, twenty participants experienced two driving conditions differing only in turn directions. The repetitive condition featured a repeating turn direction pattern. The non-repetitive condition contained pseudo-randomly ordered turn directions. To mimic an 'eyes-off-the-road' setting and prevent visual motion anticipation, road visuals were omitted. No significant differences in sickness or head motion, a metric for motion anticipation, were found between the conditions. No participant recognised the repeating turn pattern. This suggests no increased motion anticipation in the repetitive condition, possibly due to a reduced ability to recognise a repeating motion pattern in one degree of freedom within more complex motion.

Feeling unwell of passenger travel by small vehicles and associated risk factors in the North Shewa Zone, Oromiya, Ethiopia

The current study investigated how and why sociocultural structures, situational conditions, and personal behavioural factors cause passengers to feel ill when travelling by minibuses, drawing on ideas from the social construction theory of illness. A significant objective was to investigate associated risk variables that influence passengers' feelings of illness related to the social environment, addressing their beliefs, meanings, practices, and behaviours. A survey method was used to obtain data from 384 passengers for the study. The results of logistic regression indicated that feeling ill when travelling by minibuses differed from passenger to passenger; then, they had their own set of practical and emotional challenges that had no known medical reason. Compared with male and older passengers, female and younger passengers were more likely to feel ill. Furthermore, stress and role-set effects increased passengers' experiences of feeling ill more than did passengers who had no stress prior to the trip and who had only one role. Additionally, passengers who travelled intermittently, utilized suppression techniques to lessen travel discomfort, and fastened seat belts were less likely to experience symptoms of illness. Passengers who travelled on unsafe roads and used alcohol before travel, on the other hand, were more likely to feel ill than those who travelled on safer roads and did not use alcohol before the trip. The findings suggest that passengers should be aware of predisposing conditions that result in illness, be able to rest before travelling, and use all suppressive methods to reduce or prevent illness while travelling by small buses.

Development and Evaluation of Vibration Canceling System Utilizing Macro-Fiber Composites (MFCs) and Long Short-Term Memory (LSTM) Vibration Prediction AI Algorithms for Road Driving Vibrations

This study developed an innovative active vibration canceling (AVC) system designed to mitigate non-periodic vibrations during road driving to enhance passenger comfort. The macro-fiber composite (MFC) used in the system is a smart material that is flexible, soft, lightweight, and applicable in many fields as a dual-purpose sensor and actuator. The target vibrations are road vibration data that were collected while driving on standard urban (Seoul) and highway roads at 40 km/s. To predict and cancel the target vibration accurately before passing it, we modeled the vibration prediction algorithm using a long short-term memory recurrent neural network (LSTM RNN). We regenerated vibrations on Seoul and highway roads at 40 km/s using MFCs and measured the displacements of the measured, predicted, and AVC vibrations of each road condition. To evaluate the vibration, we computed the root mean squared error (RMSE) and compared standard deviation (SD) values. The accuracies of LSTM RNN vibration prediction algorithms are 97.27% and 96.36% on Seoul roads and highway roads, respectively, at 40 km/s. Although the vibration ratio compared with the AVC results are different, there was no difference between the values of the AVC vibrations. According to a previous study and the principle of the AVC system, the target vibrations decrease by canceling the inverse vibration of the MFC actuator.

A computational model of motion sickness dynamics during passive self-motion in the dark

Predicting the time course of motion sickness symptoms enables the evaluation of provocative stimuli and the development of countermeasures for reducing symptom severity. In pursuit of this goal, we present an observer-driven model of motion sickness for passive motions in the dark. Constructed in two stages, this model predicts motion sickness symptoms by bridging sensory conflict (i.e., differences between actual and expected sensory signals) arising from the observer model of spatial orientation perception (stage 1) to Oman's model of motion sickness symptom dynamics (stage 2; presented in 1982 and 1990) through a proposed "Normalized innovation squared" statistic. The model outputs the expected temporal development of human motion sickness symptom magnitudes (mapped to the Misery Scale) at a population level, due to arbitrary, 6-degree-of-freedom, self-motion stimuli. We trained model parameters using individual subject responses collected during fore-aft translations and off-vertical axis of rotation motions. Improving on prior efforts, we only used datasets with experimental conditions congruent with the perceptual stage (i.e., adequately provided passive motions without visual cues) to inform the model. We assessed model performance by predicting an unseen validation dataset, producing a Q2 value of 0.86. Demonstrating this model's broad applicability, we formulate predictions for a host of stimuli, including translations, earth-vertical rotations, and altered gravity, and we provide our implementation for other users. Finally, to guide future research efforts, we suggest how to rigorously advance this model (e.g., incorporating visual cues, active motion, responses to motion of different frequency, etc.).

The (in)effectiveness of anticipatory vibrotactile cues in mitigating motion sickness

The introduction of (fully) automated vehicles has generated a re-interest in motion sickness, given that passengers suffer much more from motion sickness compared to car drivers. A suggested solution is to improve the anticipation of passive self-motion via cues that alert passengers of changes in the upcoming motion trajectory. We already know that auditory or visual cues can mitigate motion sickness. In this study, we used anticipatory vibrotactile cues that do not interfere with the (audio)visual tasks passengers may want to perform. We wanted to investigate (1) whether anticipatory vibrotactile cues mitigate motion sickness, and (2) whether the timing of the cue is of influence. We therefore exposed participants to four sessions on a linear sled with displacements unpredictable in motion onset. In three sessions, an anticipatory cue was presented 0.33, 1, or 3 s prior to the onset of forward motion. Using a new pre-registered measure, we quantified the reduction in motion sickness across multiple sickness scores in these sessions relative to a control session. Under the chosen experimental conditions, our results did not show a significant mitigation of motion sickness by the anticipatory vibrotactile cues, irrespective of their timing. Participants yet indicated that the cues were helpful. Considering that motion sickness is influenced by the unpredictability of displacements, vibrotactile cues may mitigate sickness when motions have more (unpredictable) variability than those studied here.