ZF fights travel sickness with AI

ZF fights travel sickness with AI

Technology News |
By Christoph Hammerschmidt

ZF wants to solve the problem of travel sickness and goes beyond a purely vehicle-related approach: “We focus on the occupants themselves and their individual driving experience,” says Florian Dauth, responsible for activities in the area of Human Centered Vehicle Motion Control at ZF advanced research. “Our goal is to individually diagnose travel sickness and develop measures based on the passenger’s current condition.

Together with the Systems Neuroscience & Neurotechnology Unit (SNNU) at the University of Saarland and HTW Saar, the scientific basis for this concept is provided by test person studies conducted in which the physiological reactions of test persons to different driving situations were investigated. “Our joint research with ZF covers areas of neurotechnology, psychophysiology, artificial intelligence, and vehicle dynamics,” explains Prof. Dr. Daniel J. Strauss, Director of SNNU.

The travel or motion sickness, in technical terms kinetosis, is caused by a discrepancy in perception: The organ of balance located in the inner ear feels a movement that is not confirmed by other sensory organs such as the eyes – this happens in particular when the passenger looks concentrated at a screen or a book. In this situation, the human body reacts similarly to poisoning. Symptoms range from mild discomfort to severe nausea.

In several studies, the researchers from ZF and SNNU analyzed which physiological markers have the highest correlation with the subjective travel sickness perception of humans in real road traffic and which correlation exists with the driving dynamics of the vehicle. Indicators are, for example, changes in body temperature and skin conductivity. “Our Motion Sickness Research Vehicle allows us to record a large number of physiological measurement data, camera data, and vehicle dynamics measurement values with the help of a high-performance computer. At the same time, the vehicle serves as a platform for developing and validating the algorithms,” explains Dauth.

The research team collected more than 50,000 gigabytes of physiological markers of the central and autonomous nervous system over more than ten thousand kilometers as thermography, image and driving dynamics data. In the industry, this is a unique multimodal database on travel sickness. “They help us use a scientific approach to gain an understanding of the phenomenon of motion sickness and at the same time form the basis for AI-based algorithms,” Dauth explains the development process.

A sensor set in the interior of the vehicle and wearables worn by the test persons for non-invasive measurement on the body are part of the research. “The challenge is to develop an automotive-compatible system that allows contact-free detection of travel sickness across evolutionary stages. We see this as key information in order to get the very individual phenomenon of travel sickness under control,” said Dauth. This enables the driver – or later the control system of the automated vehicle – to recognize at an early stage if, for example, a child is unwell in the back seat and adapt the driving behavior accordingly.

Everyone reacts differently to vehicle movements and has an individual feeling for driving comfort. ZF maps this fact in an algorithm that learns the body reactions of the passenger based on AI methods and thus creates a personalized profile. Since individual data is thus available for each passenger, automated vehicles would even be able to implement the preferred driving style of each passenger. Only – unfortunately – not at the same time.

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