Users of automated vehicles will engage in other activities and take their eyes off the road, making them prone to motion sickness. To resolve this, the current paper validates models predicting sickness in response to motion and visual conditions. We validate published models of vestibular and visual sensory integration that have been used for predicting motion sickness through sensory conflict. We use naturalistic driving data and laboratory motion (and vection) paradigms, such as sinusoidal translation and rotation at different frequencies, Earth-Vertical Axis Rotation, Off-Vertical Axis Rotation, Centrifugation, Somatogravic Illusion, and Pseudo-Coriolis, to evaluate different models for both motion perception and motion sickness. We investigate the effects of visual motion perception in terms of rotational velocity (visual flow) and verticality. According to our findings, the SVC_I model, a 6DOF model based on the Subjective Vertical Conflict (SVC) theory, with visual rotational velocity input is effective at estimating motion sickness. However, it does not correctly replicate motion perception in paradigms such as roll-tilt perception during centrifuge, pitch perception during somatogravic illusion, and pitch perception during pseudo-Coriolis motions. On the other hand, the Multi-Sensory Observer Model (MSOM) accurately models motion perception in all considered paradigms, but does not effectively capture the frequency sensitivity of motion sickness, and the effects of vision on sickness. For both models (SVC_I and MSOM), the visual perception of rotational velocity strongly affects sickness and perception. Visual verticality perception does not (yet) contribute to sickness prediction, and contributes to perception prediction only for the somatogravic illusion. In conclusion, the SVC_I model with visual rotation velocity feedback is the current preferred option to design vehicle control algorithms for motion sickness reduction, while the MSOM best predicts perception. A unified model that jointly captures perception and motion sickness remains to be developed.

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A prime concern for automated vehicles is motion comfort, as an uncomfortable ride may reduce acceptance of the technology amongst the general population. However, it is not clear how transient motions typical for travelling by car affect the experience of comfort. Here, we determine the relation between properties of vehicle motions (i.e., acceleration and jerk) and discomfort empirically, and we evaluate the ability of normative models to account for the data. 23 participants were placed in a moving-base driving simulator and presented sinusoidial and triangular motion pulses with various peak accelerations (A_max0.4 − 2 ms⁻²) and jerks (J_max0.5 − 15 ms⁻³), designed to recreate typical vehicle accelerations. Participants provided discomfort judgments on absolute ‘Verbal Qualifiers’ and relative ‘Magnitude Estimates’ associated with these motions. The data show that discomfort increases with acceleration amplitude, and that the strength of this effect depends on the direction of motion. We furthermore find that higher jerks (shorter duration pulses) are considered more comfortable, and that triangular pulses are more comfortable than sinusoidal pulses. ME responses decrease (i.e., reduced discomfort) with increasing pulse duration. Evaluations of normative models of vibration and shock (ISO 2631), and perceived motion intensity provide mixed results. The vibration model could not account for the data well. Reasonable agreement between predictions and observations were found for the shock model and perceived intensity model, which emphasize the role of acceleration. We present novel statistical models that describe motion comfort as a function of acceleration, jerk, and direction. The present findings are essential to develop motion planning algorithms aimed at maximizing comfort.

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The human motion perception system has long been linked to motion sickness through state estimation conflict terms. However, to date, the extent to which available perception models are able to predict motion sickness, or which of the employed perceptual mechanisms are of most relevance to sickness prediction, has not been studied. In this study, the subjective vertical model, the multi-sensory observer model and the probabilistic particle filter model were all validated for their ability to predict motion perception and sickness, across a large set of motion paradigms of varying complexity from literature. It was found that even though the models provided a good match for the perception paradigms studied, they could not be made to capture the full range of motion sickness observations. The resolution of the gravito-inertial ambiguity has been identified to require further attention, as key model parameters selected to match perception data did not optimally match motion sickness data. Two additional mechanisms that may enable better future predictive models of sickness have, however, been identified. Firstly, active estimation of the magnitude of gravity appears to be instrumental for predicting motion sickness induced by vertical accelerations. Secondly, the model analysis showed that the influence of the semicircular canals on the somatogravic effect may explain the differences in the dynamics observed for motion sickness induced by vertical and horizontal plane accelerations.

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The relationship between the amplitude of motion and the accumulation of motion sickness in time is unclear. Here, we investigated this relationship at the individual and group level. Seventeen participants were exposed to four oscillatory motion stimuli, in four separate sessions, separated by at least 1 week to prevent habituation. Motion amplitude was varied between sessions at either 1, 1.5, 2, or 2.5 ms⁻². Time evolution was evaluated within sessions applying: an initial motion phase for up to 60 min, a 10-min rest, a second motion phase up to 30 min to quantify hypersensitivity and lastly, a 5-min rest. At both the individual and the group level, motion sickness severity (MISC) increased linearly with respect to acceleration amplitude. To analyze the evolution of sickness over time, we evaluated three variations of the Oman model of nausea. We found that the slow (502 s) and fast (66.2 s) time constants of motion sickness were independent of motion amplitude, but varied considerably between individuals (slow STD = 838 s; fast STD = 79.4 s). We also found that the Oman model with output scaling following a power law with an exponent of 0.4 described our data much better as compared to the exponent of 2 proposed by Oman. Lastly, we showed that the sickness forecasting accuracy of the Oman model depended significantly on whether the participants had divergent or convergent sickness dynamics. These findings have methodological implications for pre-experiment participant screening, as well as online tuning of automated vehicle algorithms based on sickness susceptibility.

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In future automated vehicles we will often engage in non-driving tasks and will not watch the road. This will affect postural stabilization and may elicit discomfort or even motion sickness in dynamic driving. Future vehicles will accommodate this with properly designed seats and interiors, whereas comfortable vehicle motion will be achieved with smooth driving styles and well-designed (active) suspensions. To support research and development in dynamic comfort, this paper presents the validation of a multi-segment full-body human model, including visuo-vestibular and muscle spindle feedback, for postural stabilization. Dynamic driving is evaluated using a “sickening drive”, including a 0.2 Hz 4 m/s² slalom. Vibration transmission is evaluated with compliant automotive seats, applying 3D platform motion and evaluating 3D translation and rotation of pelvis, trunk and head. The model matches human motion in dynamic driving and reproduces fore–aft, lateral and vertical oscillations. Visuo-vestibular and muscle spindle feedback are shown to be essential, in particular, for head–neck stabilization. Active leg muscle control at the hips and knees is shown to be essential to stabilize the trunk in the high-amplitude slalom condition but not with low-amplitude horizontal vibrations. However, active leg muscle control can strongly affect 4–6 Hz vertical vibration transmission. Compared to the vibration tests, the dynamic driving tests show enlarged postural control gains to minimize trunk and head roll and pitch and to align head yaw with driving direction. Human modelling can enable the insights required to achieve breakthrough comfort enhancements, while enabling efficient developments for a wide range of driving conditions, body sizes and other factors. Hence, modelling human postural control can accelerate the innovation of seats and vehicle motion-control strategies for (automated) vehicles.

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By 2050 a large proportion of the cars on our roads will be self-driving and completely automated. We will no longer be driving these vehicles, but will be transported comfortably as passengers. We will be able to indulge in all sorts of media items in our vehicles, do work, or even just relax and sleep. Indeed, these fully automated vehicles will have a clear positive impact on everybody’s lives. That is, if people do not become too motion sick to enjoy the ride. It is known that drivers of vehicles do not get motion sick because they are in control of the vehicle and, hence, can anticipate upcoming motions. However, many passengers, which we will all eventually become, do experience motion sickness. This is particularly an issue when their eyes are off the road and when they are engaged in other activities. With a rising prospect of motion sickness, these activities may no longer seem attractive. Moreover, motion sickness increases workload and decreases cognitive performance, which means that those wishing to use their commuting time in cognitively demanding activities will be less productive in them. With full automation, it is hoped that vehicle control systems can be optimised to reduce sickening motions to the lowest feasible level, whilst also achieving adequate vehicle performance in terms of, for instance, journey time. However, at this moment we don’t yet have a good model of motion sickness that would enable such optimization. For example, route planning algorithms generally optimise for the shortest time, while some have recently started to optimise for the least polluting route. For this optimisation, the algorithm needs to have, amongst other things, traffic information, the roads, their lengths and legally allowable speeds, with all such information encapsulated in a general mathematical model. There is, however, no analogous model of motion sickness that can be used to optimise our vehicle’s behaviour for the lowest passenger sickness incidence. A mathematical model in this sense is a set of equations that tell us how sickening a certain pattern of motion will be. A pattern of motion could be the vehicle taking a turn, switching a lane, stopping at a traffic light; anything that makes the vehicle change speed or direction. By being able to predict how much sickness will result from certain manoeuvres, the vehicle can be programmed to perform these manoeuvres in the least sickening manner. One problem with developing mathematical models for motion sickness minimisation is that there is a great variability in how motion sickness manifests itself in individuals. This means that the motion sickness symptoms, their accumulation and even the nature of the motions that cause sickness are highly individual. Therefore, any algorithm meant for vehicle control must take the individual as the subject of its concern. Prior to this thesis, the individual was not seen as a feasible unit of study. Instead, literature mainly focused on group-level responses. However, because motion sickness is so variable, it is likely that optimising group-averaged criteria, will not optimise for group-averaged comfort. Instead, individualisation is needed. This need directly shaped the objective of this thesis, which was to understand and model motion sickness accumulation and its individual differences for the comfortable control of automated vehicles...@en

High levels of vehicle automation are expected to increase the risk of motion sickness, which is a major detriment to driving comfort. The exact relation between motion sickness and discomfort is a matter of debate, with recent studies suggesting a relief of discomfort at the onset of nausea. In this study, we investigate whether discomfort increases monotonously with motion sickness and how the relation can best be characterized in a semantic experiment (Experiment 1) and a motion sickness experiment (Experiment 2). In Experiment 1, 15 participants performed pairwise comparisons on the subjective discomfort associated with each item on the popular MIsery SCale (MISC) of motion sickness. In Experiment 2, 17 participants rated motion sickness using the MISC during exposures to four sustained motion stimuli, and provided (1) numerical magnitude estimates of the discomfort experienced for each level of the MISC, and (2) verbal magnitude estimates with seven qualifiers, ranging between feeling ‘excellent’ and ‘terrible’. The data of Experiment 1 show that the items of the MISC are ranked in order of appearance, with the exception of 5 (‘severe dizziness, warmth, headache, stomach awareness, and sweating’) and 6 (‘slight nausea’), which are ranked in opposite order. However, in Experiment 2, we find that discomfort associated with each level of the MISC, as it was used to express motion sickness during exposure to a sickening stimulus, increases monotonously; following a power law with an exponent of 1.206. While the results of Experiment 1 replicate the non-linearity found in recent studies, the results of Experiment 2 suggest that the non-linearity is due to the semantic nature of Experiment 1, and that there is a positive monotonous relation between MISC and discomfort in practice. These results support the suitability of MISC to assess motion sickness.

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Previous literature suggests a relationship between individual characteristics of motion perception and the peak frequency of motion sickness sensitivity. Here, we used well-established paradigms to relate motion perception and motion sickness on an individual level. We recruited 23 participants to complete a two-part experiment. In the first part, we determined individual velocity storage time constants from perceived rotation in response to Earth Vertical Axis Rotation (EVAR) and subjective vertical time constants from perceived tilt in response to centrifugation. The cross-over frequency for resolution of the gravito-inertial ambiguity was derived from our data using the Multi Sensory Observer Model (MSOM). In the second part of the experiment, we determined individual motion sickness frequency responses. Participants were exposed to 30-minute sinusoidal fore-aft motions at frequencies of 0.15, 0.2, 0.3, 0.4 and 0.5 Hz, with a peak amplitude of 2 m/s² in five separate sessions, approximately 1 week apart. Sickness responses were recorded using both the MIsery SCale (MISC) with 30 s intervals, and the Motion Sickness Assessment Questionnaire (MSAQ) at the end of the motion exposure. The average velocity storage and subjective vertical time constants were 17.2 s (STD = 6.8 s) and 9.2 s (STD = 7.17 s). The average cross-over frequency was 0.21 Hz (STD = 0.10 Hz). At the group level, there was no significant effect of frequency on motion sickness. However, considerable individual variability was observed in frequency sensitivities, with some participants being particularly sensitive to the lowest frequencies, whereas others were most sensitive to intermediate or higher frequencies. The frequency of peak sensitivity did not correlate with the velocity storage time constant (r = 0.32, p = 0.26) or the subjective vertical time constant (r = − 0.37, p = 0.29). Our prediction of a significant correlation between cross-over frequency and frequency sensitivity was not confirmed (r = 0.26, p = 0.44). However, we did observe a strong positive correlation between the subjective vertical time constant and general motion sickness sensitivity (r = 0.74, p = 0.0006). We conclude that frequency sensitivity is best considered a property unique to the individual. This has important consequences for existing models of motion sickness, which were fitted to group averaged sensitivities. The correlation between the subjective vertical time constant and motion sickness sensitivity supports the importance of verticality perception during exposure to translational sickness stimuli.

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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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Background: Motion sickness (MS) as an area of scientific inquiry has mostly seen experimental work. A range of models attempt to predict MS, but have not been validated for a broad selection of sickening motion stimuli. By doing so this study aims to identify models that lead to better sickness predictions. Lastly, the study will also elaborate on the effects of vision, a key factor in motion sickness of automated driving in particular. Methods: Three models of spatial orientation are compared, the TNO model, the Newman model and the probalistic particle filter model. The models are tuned and validated with respect to the perceptual responses to earth-vertical axis rotation, off-vertical axis rotation and centrifugation. Internal conflict terms are used as a proxy for sickness prediction. Sickness responses are evaluated for five motion paradigms: pure roll, pure lateral and vertical acceleration, off-vertical axis rotation and cross-coupled coriolis stimulation. MS susceptibility as a function of frequency is derived for 3D acceleration and rotation for 3 different visual conditions; internal vision, external vision and darkness. Results: Preliminary results show that models do not adequately explain differences in sickness response observed between motion paradigms. For the TNO model, MS susceptibility as function of frequency matches experiments for vertical acceleration in darkness, but not for horizontal plane acceleration. The inclusion of vision shifts the center frequency from 0.2 Hz to 0.1 Hz for vertical acceleration, but does not affect the horizontal response. The Newman model predictions are the least accurate due to a direct coupling between state estimates and conflict terms. The particle filter model shows promise in that parameter variations approximately reproduce the MS susceptibility observed for lateral accelerations. However unlike the TNO & Newman models, it cannot account for vertical motion sickness as by design its predictions are based on the somatogravitic illusion.@en