Netradyne, a provider of artificial intelligence (AI) and edge computing focusing on driver and fleet safety, announced that Driveri has mapped data from vehicles traveling more than 1 billion miles on U.S. roads, including 1.8 million unique miles, more than any other vision-based driving system, according to the company.

This data is collected by the Driveri system deployed in fleet vehicles around the US, driven by professional drivers and includes numerous passes over the same roads to provide deeper insights into how driving and different road conditions may change in hours, days and weeks, not mapped over the course of many years. 

Additionally, the company has tracked nearly 3 billion minutes of vision-based driving data. It was just back in January that the company announced 1-billion minutes of data. 

The Driveri system leverages a four-camera system looking both within and outside the vehicle, continuously analyzing driving scenes and road data while monitoring drivers for driving behavior using AI embedded directly into the edge computing device. It captures road and traffic scenarios such as commercial driveways, temporary road closures, frontage roads, school zones and more. 

"In addition to mapping more than a billion miles, we are creating a unique database from which to study the trends and patterns regarding accidents and changing roads,” said Avneesh Agrawal, chief executive officer of Netradyne. “This rich data not only is critical for fleet companies and insurers to make our roads safer, but ultimately this data will be immensely valuable to the systems of autonomous vehicles, where the training data is imperative. Lives literally are at stake, our data can be used to help train AV ecosystems which currently must rely on pricey surveys, specialized equipment, and human review; rather, they can lean on the data collected by our Driveri platform in order to gather real-time insights and alerts that will improve safety and optimize overall fleet operations. The more miles humans are safely able to drive today will help the autonomous vehicles of the future drive safely, but only if we have the data to use to train those future systems.”

Netradyne’s dynamic maps are generated in real-time and crowd-sourced from Driveri devices that are deployed in commercial vehicles. Using proprietary real-time computer vision algorithms on the edge, and crowd sourcing in the cloud Netradyne is able to generate and rapidly update maps without requiring any human intervention.