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.