Autonomous Navigation Challenges, Part 2

In our last post we looked at the current state of the art in autonomous vehicle navigation. Another way in which the problem of navigation in unmapped or incorrectly mapped areas will be overcome is through artificial intelligence. We looked at the potential of this technology in our 4/10/17 post entitled, “Machine Learning and Work Zones”. Michael Reser published an article May 8th in Electronic Design entitled, “How AI Will Help Pave the Way to Autonomous Driving”.

Mr. Reser’s main point is that given the unfathomable quantity of data that must be digested and acted upon by autonomous vehicles (AVs) the technology will progress much faster and more accurately through machine learning. “Translating it all into a real-world challenge for AI-backed autonomous-driving systems, the expected outcome of such massive data processing is nothing short of getting the right answer in the shortest possible time to determine a proper action to avoid a traffic incident.”

“To put it differently, (a) large set of data in combination with realistic scenarios and nonlinear parameter sets enables systems and applications to fail safely and learn faster.”

He goes on to list the many challenges that must also be addressed including how to tie images from multiple sensors with varying resolution quality into one accurate picture. Another was how to validate and tie different data sources together in time. They must have a consistent way of labeling those sources in time.

Mr. Reser goes on to say they are not there yet, but he sees the process as inevitable.

“For true enablement of Level 4 and Level 5 automated driving, the system should be functional in all weather and driving conditions, which is obviously a given requirement. Still, it’s a much bigger challenge than sometimes mentioned and admitted”.

Like most AV challenges, this one has serious implications for work zones. It will be interesting to watch as this process unfolds.

One View of the Current State of the Art in Autonomous Navigation

Much has been written about autonomous vehicles and their methods of navigation. But most of that writing is little more than science fiction. The systems described are usually just concepts that engineers are working toward. What is the current state of the art?

Dyllan Furness posted May 9th about emerging technology in Digital Trends magazine His article, titled “Get lost: MIT’s self-driving car takes on unmarked roads” examined the current capabilities of autonomous vehicles. He found that current AVs are only able to drive on well-mapped city streets. This deficiency would affect autonomous vehicles ability to navigate a work zone as well. As he wrote in his opening lines, “If you find yourself on a country road in a self-driving car, chances are you’re both pretty lost. Today’s most advanced autonomous driving systems rely on maps that have been carefully detailed and characterized in advanced. That means the millions of miles of unpaved roads in the United States are effectively off-limits for autonomous vehicles.”

MIT is working to change that by developing a method of navigating using simple GPS, Google map data and a variety of sensors. ““We were realizing how limited today’s self-driving cars are in terms of where they can actually drive,” Teddy Ort, an MIT CSAIL graduate student who worked on the project, told Digital Trends. “Companies like Google only test in big cities where they’ve labeled the exact positions of things like lanes and stop signs. These same cars wouldn’t have success on roads that are unpaved, unlit, or unreliably marked. This is a problem.””

Certainly, work zones fall into this problem area. And MIT’s new system could address our issues, as well. In particular, by using Google map data this system would also pick up near real-time work zone data like we described in our 9/25/17 post. Then the sensors could identify traffic control devices and follow them safely through the work zone.

It is good to see that at least one organization understands the limits of current technology and is looking for a better, safer way for autonomous vehicles to find their way through rural roads and work zones.