Car crashes in the US (400k+ in 2015) have shown a 2-years rise, after a decade of slow but steady declines. Although safer cars and improved driving assist equipment have helped prevent crashes, distracted driving is more than offsetting all these benefits.
State bans on the use of cell phones in cars don't seem to be effective as accidents caused by distracted driving continue to rise.
Mobile applications that intercept distracting calls or the use of apps are easy to circumvent and distractions can also come from sources other than mobile phones.
The old-fashioned way to solve the problem is to have a front seat passenger who 1) is aware of sudden driving risks, 2) can tell whether the driver is paying attention, and 3) will warn the driver when needed.
Driver Safety Solution with Co-Driver Expertise
Having a virtual passenger that doesn't rest.
Breakthoughs in video technology have made computer vision affordable.
Driving habits will change over time and dreyev will learn them.
If the driver looks away from the road for too long or too often, or the car is zigzagging in the lane, the virtual passenger warns the driver with specific signals or with spoken utterances.
Driver attentiveness and alertness is measured using a computer vision technology to analyze head pose, eye gaze, and eyelid closing to flag possible distraction and drowsiness.
To increase adoption and effectiveness, Dreyev uses AI and Machine Learning to create custom model of driving habits and experiences to ensure driver tendencies and paired with road conditions.
Machine learning enables the creation of custom models of driving habits and experience to ensure that warning are only given when necessary.