Autonomous vehicle technologies are rapidly growing and are expected to change transportation habits radically. Autonomous cars increase the likelihood of motion sickness by allowing everyone in the vehicle to become passengers and perform non-driving tasks such as reading, working, and socializing. Comfort is one of the critical factors in the acceptance of autonomous vehicles. This makes accurate estimation of motion sickness a necessity in the development stages of autonomous vehicles. The sensory conflict theory is a widely accepted theory that explains the mechanism of motion sickness. Computational models based on the sensory conflict theory are used to predict motion sickness and contain two main parts: a human perception model and a nonlinear fitting function to the subjective feeling of motion sickness. Models of the human perception, including the dynamics of the vestibular system, are used to calculate the difference between sensory inputs and the predicted motions in the brain, i.e. the conflict signal, which is the primary cause of motion sickness. One of the main limitations of motion sickness prediction is how to mathematically model human perception because of the complexity of the psychophysiological systems. The aim of this work is to implement and analyse different human perception modelling techniques, such as observer framework in the control theory and optimal estimator approach using Kalman filters, to evaluate their abilities to integrate with motion sickness prediction. In this study, the different human perception models are implemented and analysed using MATLAB / Simulink and the advantages, as well as disadvantages of the models, are discussed.