Lytx ActiveVision uses machine learning to detect behaviors due to fatigue and distracted driving.Video safety systems are increasingly able to detect a wide range of risky driving behaviors by using machine vision, or algorithms, to detect complex patterns of behavior.
Omnitracs is working on a feature for its Critical Event Video (CEV) product, for instance, that will monitor driver behaviors at stop signs and stop lights.
CEV uses an outward and optional inward-facing camera to capture and transmit video of safety-critical events such as sudden deceleration and speeding. With the new feature, Omnitracs CEV will be able to ascertain if a driver stopped at an intersection where a stop-sign or light was present, says Lauren Dominick, senior director of analytics and modeling.
Omnitracs is also working on new machine vision algorithms that will use video from its inward-facing camera to detect patterns of driver distraction. Examples include eye movement, cell phone and seat belt usage, she says.
Two years ago, Lytx released an optional service called ActiveVision that layers on top of its DriveCam platform. ActiveVision uses machine vision to detect additional patterns of risk caused by lane departures, distracted driving and unsafe following distances.
Comparing the results of ActiveVision verses the stand-alone DriveCam platform shows that ActiveVision identifies:
- 60 percent more lane departures and the root causes behind them
- Up to 24 percent more drivers who are driving while distracted
- Up to 65 percent more events where the driver is identified as following too closely, and the root causes behind them.
Lytx now has ActiveVision outfitted on more than 50,000 vehicles, and its DriveCam platform is installed in more than 400,000 vehicles. Overall, the company has more than 70 billion driving miles in its database, which is growing at a rate of one billion miles every two weeks, says Brandon Nixon, chief executive officer.
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