Falling asleep while driving accounts for a considerable proportion of vehicle accidents under monotonous driving conditions. Many of these accidents are related to work--for example, drivers of lorries, goods vehicles, and company cars. It can also result from driving home after a night shift. Older driver are vulnerable to the taking of siester on the wheel. There is ever a need for drivers to remain awake on-board.
In the past, video based driver drowsiness detection systems are used to help alert a napping driver. However, "Video-based systems that use cameras to detect when a car is drifting out of its lane are cumbersome and expensive," said Hans Van Dongen, research professor at the Washington State University Sleep and Performance Research Center. "They don't work well on snow-covered or curvy roads, in darkness or when lane markers are faded or missing.
Researchers have now developed a new technology that can detect when drivers are about to nod off behind the wheel. The technology is based on steering wheel movements - which are more variable in drowsy drivers - and offers an affordable and more reliable alternative to currently available video-based driver drowsiness detection systems, researchers said.
"Our invention provides an inexpensive and user-friendly technology that overcomes these limitations and can help catch fatigue earlier, well before accidents are likely to happen," said Van Dongen, who developed the technology with postdoctoral research fellow Pia Forsman.
In an experiment, 29 participants were on a simulated 10-day night shift schedule that caused moderate levels of fatigue, as assessed by their performance on a widely used alertness test known as the psychomotor vigilance task (PVT).
During each night shift, participants spent four 30-minute sessions on a high-fidelity driving simulator, which captured data for 87 different metrics related to speed, acceleration, steering, lane position and other factors.
Data analysis indicated that the two factors that best predicted fatigue were variability in steering wheel movements and variability in lane position.
Researchers then showed that data on steering wheel variability can be used to predict variability in lane position early on, making it possible to detect driver drowsiness before the car drifts out of its lane.
"We wanted to find out whether there may be a better technique for measuring driver drowsiness before fatigue levels are critical and a crash is imminent," Van Dongen said.
"Our invention provides a solid basis for the development of an early detection system for moderate driver drowsiness. It could also be combined with existing systems to extend their functionality in detecting severe driver drowsiness," he said.
The solution uses inexpensive, easy-to-install parts - including a sensor that measures the position of the steering wheel.
The science behind the invention was described in the journal Accident Analysis & Prevention.
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