While advances in technology and public campaigns have placed a focus on distracted driving, it remains the cause of nearly 9 percent of fatal collisions. The rise of advanced driver assistance systems (ADAS), which alert drivers to potential collisions or lane changes and other potential dangers via an array of methods, continues to rank at the top of manufacturer’s research and development efforts. But what if the ADAS itself is the distraction?
Researchers at the University of Missouri (MU) have used eye-tracking technology to gather data that may usher in new ways for drivers to communicate with their vehicles. Their research—“Evaluating Rear-End Vehicle Accident Using Pupillary Analysis in a Driving Similar Environment” and “Pupillary Response and EMG Predict Upcoming Responses to Collision Avoidance Warning”—recently presented at the 2019 International Conference on Applied Human Factors and Ergonomics in Washington, D.C., focuses on the how the pupils respond to vehicle alerts.
“Prior to a crash, drivers can be easily distracted by an alert from a collision avoidance warning—a popular feature in new vehicles—and we feel this could be a growing problem in distraction-related vehicle crashes,” said Jung Hyup Kim, an assistant professor of industrial and manufacturing systems engineering in the MU College of Engineering. “Therefore, a two-way communication channel needs to exist between a driver and a vehicle. For instance, if a driver is aware of a possible crash, then the vehicle does not have to warn the driver as much. However, if a vehicle provides an alert that, by itself, creates a distraction, it could also cause a crash.”
Kim and Xiaonan Yang, a graduate student at MU, studied how pupils change in response to the physical reaction to an ADAS. Their goal was to create a database of visual and physical responses to different warnings, thus creating a way to predict how a driver will react during a situation to help enhance safety systems.
“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 that can understand driver responses after the warnings, then the system will be able to generate more personalized warnings to drivers.”
With significant data in tow, the researchers hope to apply it toward the development of a two-way communication model that can reduce distracted driving.
For more insight into ADAS development, check out White Paper: Fast-Tracking Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles Development with Simulation.