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Distracted driving is a major danger on today's roadways. Employers play a critical role in developing distracted driving policies and promoting a culture of workplace driving safety. The purpose of this study was to evaluate the effectiveness of an in-person work-based class to reduce distracted driving in participating employees. The "Just Drive-Take Action Against Distraction" class was designed by the UC San Diego Training, Research and Education for Driving Safety (TREDS) program to increase awareness of the dangers of distracted driving and to encourage employees to be safe and responsible drivers, both on and off the job. Participants completed pre- and post-anonymous surveys and, in a subset of attendees, volunteers were contacted via email 3 months post-intervention to complete a driving-behavior survey on Surveymonkey.com. 115 classes for 6896 employees were delivered at 54 agencies in Southern California. A total of 4928 participants completed the pre- and post-survey; 2014 n = 2263 and 2015 n = 2665. The course was found useful (85%) and engaging (85.6%). For non-commercial drivers, 55.6% of participants reported an increase of 80-100% in awareness of the dangers of distracted driving, and 67.2% reported an increase of 80-100% in their motivation to change. For commercial drivers, 71.3% reported a motivation increase of 80-100%. There were significant increases in knowledge for both groups. In the three-month follow-up survey, participants identified multiple positive changes in distracted driving behavior. This 1-h employer-supported intervention demonstrated positive changes in short-term intention and medium-term behaviors.

In this article, we examine the performance of different eye blink detection algorithms under various constraints. The goal of the present study was to evaluate the performance of an electrooculogram- and camera-based blink detection process in both manually and conditionally automated driving phases. A further comparison between alert and drowsy drivers was performed in order to evaluate the impact of drowsiness on the performance of blink detection algorithms in both driving modes. Data snippets from 14 monotonous manually driven sessions (mean 2 h 46 min) and 16 monotonous conditionally automated driven sessions (mean 2 h 45 min) were used. In addition to comparing two data-sampling frequencies for the electrooculogram measures (50 vs. 25 Hz) and four different signal-processing algorithms for the camera videos, we compared the blink detection performance of 24 reference groups. The analysis of the videos was based on very detailed definitions of eyelid closure events. The correct detection rates for the alert and manual driving phases (maximum 94%) decreased significantly in the drowsy (minus 2% or more) and conditionally automated (minus 9% or more) phases. Blinking behavior is therefore significantly impacted by drowsiness as well as by automated driving, resulting in less accurate blink detection.