A Full-Featured, Enhanced Cost Function to Mitigate Motion Sickness in Semi- and Fully-autonomous Vehicles

Current full- and semi- Autonomous car prototypes increasingly feature complex algorithms for lateral and longitudinal control of the vehicle. Unfortunately, in some cases, they might cause fussy and unwanted effects on the human body, such as motion sickness, ultimately harnessing passengers' comfort, and driving experience. Motion sickness is due to conflict between visual and vestibular inputs, and in the worst case might causes loss of control over one's movements, and reduced ability to anticipate the direction of movement. In this paper, we focus on the five main physical characteristics that affect motion sickness, including them in the function cost, to provide quality passengers' experience to vehicle passengers. We implemented our approach in a state-of-the-art Model Predictive Controller, to be used in a real Autonomous Vehicle. Preliminary tests using the Unreal Engine simulator have already shown that our approach is viable and effective, and we implemented and evaluated using Motion Sickness Dose Value and Illness Rating and then tested it in an embedded platform. We implemented it on our embedded platform, NVIDIA Jetson AGX Xavier that is representative of the next-generation AV Domain Controller.