Machine Learning: A Road’s Automated Doctor
EVENTSPERSPECTIVES

Machine Learning: A Road’s Automated Doctor

Self-driving cars might be just the long-term solution that we’re looking for. Developing such technology has the potential to save thousands of human lives. But, the goal to create fully autonomous vehicles takes as much attention to technical details as agreement on safety standards. We need to keep the lines of communication open between car makers and their vendors.

What’s the state of autonomous cars? To find out, the Autotech Council—a Silicon Valley-based ecosystem of automobile industry players—held an industry gathering. The half-day event, hosted by Western Digital, brought together 300 leaders in the autonomous cars industry. We partnered with SiliconANGLE, a leading digital media platform, to spend a few minutes talking with a select group of these leaders. Watch the latest expert interview here.


There’s a new way to look out for fractures and potholes in the road. And, it doesn’t need eyes to see them.

But, it does need a camera mounted on its dashboard. It’s the crux of a system that uses computer vision and machine learning to read the surface of a road. Then, the road examination begins. Like a doctor diagnosing a patient, this advanced machine vision system is able to identify health issues, such as road fractures and splits. With accuracy down to 10-foot sections, keeping up with the health of roads has been shifted over to the machines.

Machines That See Roads Better Than Humans

Pittsburgh, Pennsylvania. A town forged in steel is now a leader in a new movement: autonomous vehicles. One of the companies leading the charge is Roadbotics, a Pittsburgh-based company that creates advanced road monitoring technology.

Under the leadership of Chief Executive Officer Mark DeSantis, the Roadbotics team has developed a comprehensive road monitoring system. This system uses semi-autonomous vehicles to collect road data and machine learning to analyze road conditions with 10-foot accuracy. A cloud-based Geographic Information System (GIS) dashboard is used to make each road’s assessment easily searchable and accessible.

* Video clip from full interview below.

Features for Predictive Road Maintenance

How can we tell when a road is in dire need of maintenance? By that time, we’ve usually ran over a pothole. But, there are other ways to monitor roads beyond blowing a tire or breaking an axle in your car. Deep learning algorithms can evaluate cracks, fractures, and other road damage to predict whether a road needs small fixes or a major overhaul.

* Video clip from full interview below.

It’s all about fixing the little issues before they become big problems. Leave the machines to take care of the monotonous data collection and evaluation processes. Utility companies can then act on those insights to fix their customers’ roads in an optimal fashion.

“If you can see features that are predictive of a road that’s just about to go bad,” Mark proposes, “then you can extend the useful life of that asset indefinitely.”

We built millions of miles of roads by hand. Now, to save our roads, we may need to enlist the help of machines to show us where to fix the bumps and bruises.


FORWARD-LOOKING STATEMENTS:

Certain blog and other posts on this website may contain forward-looking statements, including statements relating to expectations in the market for our products and applications of our products. These forward-looking statements are subject to risks and uncertainties that could cause actual results to differ materially from those expressed in the forward-looking statements, including development challenges or delays, changes in markets, demand, global economic conditions and other risks and uncertainties listed in Western Digital Corporation’s most recent quarterly and annual reports filed with the Securities and Exchange Commission, to which your attention is directed. Readers are cautioned not to place undue reliance on these forward-looking statements and we undertake no obligation to update these forward-looking statements to reflect subsequent events or circumstances.

 

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