Control for Motion Sickness Minimisation in Autonomous Vehicles
posterposted on 2018-10-22, 09:13 authored by Zaw HtikeZaw Htike
Poster presented at the Cranfield Doctoral Network Annual Event 2018.
Autonomous vehicles or self-driving vehicles are expected to become a wide scale deployment for public use in the very near future. Recent study shown that there will be increase in frequency and severity of motion sickness due to engaging in non-driving tasks. This establishes motion sickness as being the elephant in the room and the increase in occurrence of motion sickness is predicting to be a limitation to the successful introduction of full vehicle automation. Motion sickness is a condition marked by symptoms of nausea dizziness, and other physical discomfort. The accepted cause of motion sickness is being the sensory conflict between inputs from the visual, vestibular and somatosensory systems of human body. Factors that might increase or decrease the severity of sickness symptoms includes ages, genders, alcohols, drugs,motion environments, other environmental and psychological aspect. Nevertheless, motion sickness in road vehicles is most closely related to low-frequency fore-and-aft, lateral, yaw acceleration. The range of these frequencies stated in the Standards guideline (International Standard, British Standards and Military Standards) for human exposed to whole-body mechanical vibration and shock, are in the range between 0.1 to 0.5 Hz. Previous experiments studies also shown that passenger motion sickness
increases with increased exposure to lateral motion at low frequencies less than 0.5 Hz. This project aims to develop a control strategy that could minimise motion sickness in
autonomous vehicles. The first part of the project explores the empirical formulations outlined in the Standards to evaluate motion sickness as a form of predicted illness
rating or motion sickness incidence. A simple optimisation algorithm is developed to investigate the effectiveness of reducing motion sickness based from such formulations.
The second part of the project looks at the sensory conflict theory for estimating motion sickness by adopting the existing 6-DOF subjective vertical conflict model. This model
would later incorporate with vehicle model, and an optimal control strategy would be implemented to minimise motion sickness.