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Mizzou research aims to improve driver alerts through science

A man with a watch drives a car.

The study utilized pupillary response methods and electromyography, which studies the response of muscles, to study driver responses to ADAS warnings. The goal is to build a robust database of both visual and physical responses to various warnings in order to predict how most drivers will react in certain situations. Photo by Why Kei.

Advanced Driver Assistance Systems (ADAS) are increasingly prevalent in modern automobiles, letting motorists know when they’re close to a collision, drifting out of their lane and more.

But alerts are only as good as the people responding to them. If you continually get alerts every time you try to legally and safely change lanes, for example, you’re less likely to pay attention to the signal or may disable it entirely. Or, what if the alert causes a distraction of its own, leading to a crash?

One possible solution? Create a system that allows the driver to communicate with the various sensors. But to do that, manufacturers first must understand how drivers respond to the alerts and what creates these responses.

Enter Jung Hyup Kim, an Industrial and Manufacturing Systems Engineering assistant professor at Mizzou.

Kim and graduate student Xiaonan Yang recently published “Pupillary Response and EMG Predict Upcoming Responses to Collision Avoidance Warning,” for the International Conference on Applied Human Factors and Ergonomics earlier this year.

The study utilized pupillary response methods and electromyography, which studies the response of muscles, to study driver responses to ADAS warnings. The goal is to build a robust database of both visual and physical responses to various warnings in order to predict how most drivers will react in certain situations.

“My concern is, do ADAS warnings really help the driver all around?” Kim said. “If the vehicle generates more warnings than the drivers can handle, they could be more distracted by the warnings.

“If we understand how ADAS warnings influence drivers’ pupil responses and EMG signals, we can predict the upcoming driver’s responses corresponding to the warnings.”

If researchers can predict how drivers will react, they can better tailor systems either broadly or potentially to individual motorists. Kim’s research opens the door to potentially creating a more interactive system.

“After drivers had been exposed to lane departure warnings repeatedly, their responses to the warnings became negative, and they feel the warnings as a nagging critique of their driving styles. ” Kim said. “If we develop a smart ADAS, which can understand driver responses after the warnings, then the system will be able to generate more personalized warnings to drivers.”

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