Even a little crummy weather might pose big problems for self-driving systems.
New research from Michigan State University suggests light rain and drizzle can confound the algorithms that autonomous systems use to detect pedestrians, bicyclists and other road users. That doesn't bode well for the self-driving future in Canada, where there are four seasons across the country and Vancouver's infamous rain to deal with.
The findings raise the prospect that until these algorithms can better handle a variety of weather conditions, self-driving vehicles may be limited to American Sun Belt states, or fleets of vehicles might need to be grounded when weather conditions are subpar.
"When we run these algorithms, we see very noticeable, tangible degradation in detection," said Hayder Radha, an MSU professor of electrical and computer engineering who oversaw the study. "Even low-intensity rain can really create some serious problems, and as you increase the intensity, the performance of what we consider state-of-the-art mechanisms can almost become paralyzed."
The researchers are finalizing their report, but Radha previewed the findings in a conversation with Automotive News.
Although radar and lidar are often used to detect obstacles, Radha said the research focuses on measuring the competence of computer vision systems because cameras are most often the primary sensor automakers and tech companies use to classify pedestrians and other road users.
But the problem is not the cameras, Radha stressed; it's the algorithms distilling information from them.
"Once you throw in a few drops of rain, they get confused," he said. "It's like putting eyedrops in your eye and expecting to see right away."