US statistics reveal drowsy drivers are five times more likely to be involved in an accident, or a near-crash incident, than alert drivers. Furthermore, drowsy or fatigued drivers are responsible for an estimated 56,000 crashes annually with more than 40,000 of them resulting in fatal and non-fatal injuries.

One of the most dangerous aspects of drowsy or fatigued drivers is that although 37% of them admit to having fallen asleep behind the wheel, research shows drivers are very poor at gauging their sleepiness before being involved in an accident.

Research recently completed at Clemson University sought to determine the most effective way to detect a driver’s sleepiness. Many previous studies have focused on measuring psychophysiological metrics, including driver eye movements, muscle activity and changes in heart rate to determine alertness. The biometric measurements have been shown to be inaccurate at times and intrusive to a driver’s actions.

The Clemson study tested 20 volunteers whose attentiveness was measured in a vehicle simulator during a 26-hour stretch without sleep. The simulator tested volunteer drivers for about 20 minutes on a 15–mile course that included nine curves. Driving performance was measured for lateral lane position, lane heading and vehicle heading.

The Clemson University research, aimed at improving the detection of drowsy driving and finding solutions to mitigate it, was conducted by Drew Morris, a human factors psychology Ph.D. student; June Pilcher, alumni distinguished professor of psychology; and Fred Switzer, professor of psychology. The research was published in the journal Accident Analysis & Prevention.

The idea of utilizing GPS to detect a vehicle’s deviations and signaling almost immediate warnings to drivers has real practical safety applications to the auto industry. Pilcher said it’s a workable approach to detecting inattentiveness that goes beyond fatigue or drowsiness.

This kind of technology may work the same way if the inattentiveness is caused by texting, picking up something off the vehicle’s floor or any other distraction that can lead to a dangerous situation.

—June Pilcher

Resources

• Drew M. Morris, June J. Pilcher, Fred S. Switzer III (2015) “Lane heading difference: An innovative model for drowsy driving detection using retrospective analysis around curves,” Accident Analysis & Prevention, Volume 80, Pages 117-124, doi: 10.1016/j.aap.2015.04.007