Open Access

Stanislava Rangelova

• This thesis investigates the prediction of simulation sickness induced by a virtual reality driving simulation. It assesses the effects of individual, behavioral, and physiological factors on the onset of simulation sickness and evaluates the prediction models that apply these factors. The users’ sense of presence, as induced by virtual reality, is additionally explored as a contributory factor to simulation sickness. Simulation sickness is a condition of physiological discomfort experienced during or after exposure to any virtual environment. It is a well-known phenomenon that is often compared to motion sickness. While virtual reality has and will continue to positively impact the development and testing of new automotive interior concepts, simulation sickness remains one of its most significant drawbacks. Despite advances in technology, the discomfort caused by using head- mounted displays has yet to be resolved. To contribute to a solution, this thesis examines in experimentsThis thesis investigates the prediction of simulation sickness induced by a virtual reality driving simulation. It assesses the effects of individual, behavioral, and physiological factors on the onset of simulation sickness and evaluates the prediction models that apply these factors. The users’ sense of presence, as induced by virtual reality, is additionally explored as a contributory factor to simulation sickness. Simulation sickness is a condition of physiological discomfort experienced during or after exposure to any virtual environment. It is a well-known phenomenon that is often compared to motion sickness. While virtual reality has and will continue to positively impact the development and testing of new automotive interior concepts, simulation sickness remains one of its most significant drawbacks. Despite advances in technology, the discomfort caused by using head- mounted displays has yet to be resolved. To contribute to a solution, this thesis examines in experiments with a total of 94 participants the effects of a moving platform, gender, and types of driving, as well as evaluating different prediction models of simulation sickness. These features are considered a relevant combination of factors related to simulation sickness experienced in automotive interior development. As automated driving is considered the future of transportation, a driving simulation that imitates real-life automated driving could be a perfect testing or training tool. Nonetheless, virtual automated driving could potentially inherit one of the common problems of real-life automated driving, namely, motion sickness. In order to assemble a virtual reality driving system for user evaluations, a moving platform and physiological sensors were implemented. Three experiments, including static and dynamic driving, as well as automated and standard driving, were carried out. Furthermore, several prediction models based on individual factors (such as gender, motion sickness history, and previous experiences), behavioral factors (such as head movements and the speed of the virtual vehicle), and physiological factors (such as heart rate variability, skin conductance, and respiration) were evaluated. The findings showed that simulation sickness induced by virtual reality driving simulation could be successfully predicted by individual, behavioral, and physiological factors. However, the individual factors showed a low variance in the explanation of the models. A further investigation showed that the features extracted from the cardiovascular signal could predict simulation sickness with similar accuracy to the combination model (behavioral and physiological factors). Moreover, simulation sickness was not significantly affected by the addition of motion cues or by changes in the driving conditions. Gender, as revealed by the significant main effect on simulation sickness, is a simulation sickness factor with a high susceptibility potential. Further work is required to establish the importance of individual factors as well as physiological factors as predictors of simulation sickness.…