The ZF teamed up with neuroscientists from the German state of Saarland to study how to detect passenger motion sickness at an early stage and how to prevent it from adversely affecting passengers. It is reported that ZF will combine artificial intelligence to use dynamic driving data and motion sickness physiological response data to develop driving modes that help prevent motion sickness.
The scientific basis for this measure comes from a subject study jointly conducted by the Institute for Systemic Neuroscience and Neurotechnology (SNNU) of the University of Saarland and the University of Technology and Economics of Technology, Saarland, Germany. These studies validate the subject's physiological response under various driving conditions.
Motion sickness is a symptom caused by the perception imbalance of the human body: the movement information collected by the inner ear balance organs is not synchronized with other senses (such as the eyes). This is most likely to happen when a passenger is staring at the screen or reading a book. When this happens, the response shown by the human body is similar to the poisoning response in many ways, and the degree also varies from individual reactions to mild discomfort to severe motion sickness and other manifestations.
The motion sickness experimental vehicle enables researchers to record a large amount of physiological data, camera data, and measurements related to driving dynamics with the help of a high-performance computing platform. At the same time, the vehicle can also be used as an algorithm development and verification platform. In some studies, researchers at ZF and SNNU not only analyzed the physiological response data most relevant to subjective perception of individual motion sickness, they also studied the relationship between motion sickness and vehicle dynamics.
The mileage of the experimental vehicle has exceeded 10,000 kilometers. The research team collected more than 50,000 GB of central and autonomic nervous system response data in the form of thermal imaging, imaging and driving dynamics data, forming a unique multimodal data resource on motion sickness. The challenge is to develop a car-compatible system that detects motion sickness in multiple progressive steps without physical contact. With this system, important information that can accurately grasp the individual phenomenon of motion sickness can be obtained.
Everyone's body will react differently in the process of driving a car, and their comfort experience will also be different. ZF uses artificial intelligence-based algorithms to create personalized user information that includes these physical response factors by acquiring the physical response information of each passenger. According to the personal data of each passenger in the vehicle collected by the system, the autonomous driving vehicle can store the preferred driving mode of each passenger.