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Users of automated vehicles will move away from being drivers to passengers,preferably engaged in other activities such as reading or using laptops andsmartphones, which will strongly increase susceptibility to motion sickness.Similarly, in driving simulators, the presented visual motion with scaled oreven without any physical motion causes an illusion of passive motion, creatinga conflict between perceived and expected motion, and eliciting motionsickness. Given the very large differences in sickness susceptibility betweenindividuals, we need to consider sickness at an individual level. This papercombines a group-averaged sensory conflict model with an individualizedaccumulation model to capture individual differences in motion sicknesssusceptibility across various vision conditions. The model framework can beused to develop personalized models for users of automated vehicles and improvethe design of new motion cueing algorithms for simulators. The feasibility andaccuracy of this model framework are verified using two existing datasets withsickening. Both datasets involve passive motion, representative of being drivenby an automated vehicle. The model is able to fit an individuals motionsickness responses using only 2 parameters (gain K1 and time constant T1), asopposed to the 5 parameters in the original model. This ensures uniqueparameters for each individual. Better fits, on average by a factor of 1.7 ofan individuals motion sickness levels, are achieved as compared to using onlythe group-averaged model. Thus, we find that models predicting group-averagedsickness incidence cannot be used to predict sickness at an individual level.On the other hand, the proposed combined model approach predicts individualmotion sickness levels and thus can be used to control sickness.

Modelling individual motion sickness accumulation in vehicles and driving simulators