Modelling individual motion sickness accumulation in vehicles and driving simulators

Users of automated vehicles will move away from being drivers to passengers, preferably engaged in other activities such as reading or using laptops and smartphones, which will strongly increase susceptibility to motion sickness. Similarly, in driving simulators, the presented visual motion with scaled or even without any physical motion causes an illusion of passive motion, creating a conflict between perceived and expected motion, and eliciting motion sickness. Given the very large differences in sickness susceptibility between individuals, we need to consider sickness at an individual level. This paper combines a group-averaged sensory conflict model (as in Wada, et al., 2020) with an individualized accumulation model (as in Irmak, et al., 2022; Irmak, Pool, and Happee, 2020; Oman, 1990) to capture individual differences in motion sickness susceptibility across various vision conditions. This consideration of the effect of vision is crucial in driving simulators where there is a strong contribution of visual cues. The model framework can be used to develop personalized models for users of automated vehicles and improve the design of new motion cueing algorithms for simulators. The feasibility and accuracy of this model framework are verified using two existing datasets with sickening conditions in 1) an experimental vehicle with and without outside vision (Irmak, Pool, and Happee, 2020), and 2) comparing vehicle experiments with corresponding driving simulator experiments (Talsma, et al., 2023). Both datasets involve passive motion, representative of being driven by an automated vehicle. The model is able to fit an individual’s motion sickness responses using only 2 parameters (gain K1 and time constant T1), as opposed to the 5 parameters in the original model. This ensures unique parameters for each individual. Better fits, on average by a factor of 1.7 (for Accum 2 model), of an individual’s motion sickness levels, are achieved as compared to using only the group-averaged model (Accum 0 model). Furthermore, this model framework demonstrates robustness by accurately modeling various datasets with distinct motion and vision conditions. Thus, we find that models predicting group-averaged sickness incidence cannot be used to predict sickness at an individual level. On the other hand, the proposed combined model approach predicts individual motion sickness levels and thus can be used to control sickness