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Passengers (drivers) of level 3-5 autonomous personal mobility vehicles(APMV) and cars can perform non-driving tasks, such as reading books andsmartphones, while driving. It has been pointed out that such activities mayincrease motion sickness. Many studies have been conducted to buildcountermeasures, of which various computational motion sickness models havebeen developed. Many of these are based on subjective vertical conflict (SVC)theory, which describes vertical changes in direction sensed by human sensoryorgans vs. those expected by the central nervous system. Such models areexpected to be applied to autonomous driving scenarios. However, no currentcomputational model can integrate visual vertical information with vestibularsensations.  We proposed a 6 DoF SVC-VV model which add a visually perceived verticalblock into a conventional six-degrees-of-freedom SVC model to predict VVdirections from image data simulating the visual input of a human. Hence, asimple image-based VV estimation method is proposed.  As the validation of the proposed model, this paper focuses on describing thefact that the motion sickness increases as a passenger reads a book while usingan AMPV, assuming that visual vertical (VV) plays an important role. In thestatic experiment, it is demonstrated that the estimated VV by the proposedmethod accurately described the gravitational acceleration direction with a lowmean absolute deviation. In addition, the results of the driving experimentusing an APMV demonstrated that the proposed 6 DoF SVC-VV model could describethat the increased motion sickness experienced when the VV and gravitationalacceleration directions were different.

Motion Sickness Modeling with Visual Vertical Estimation and Its Application to Autonomous Personal Mobility Vehicles