In late 2018, Krzysztof Czarnecki, a professor at Canada’s University of Waterloo, built a self-driving car and trained it to navigate surrounding neighborhoods with an annotated driving data set from researchers in Germany, that was not recorded in inclement weather.
From an article in Wired by Will Knight.
The vehicle worked well enough to begin with, recognizing Canadian cars and pedestrians just as well as German ones. But then Czarnecki took the autonomous car for a spin in heavy Ontarian snow. It quickly became a calamity on wheels, with the safety driver forced to grab the wheel repeatedly to avert disaster.
Some may call this an edge case – I call it reality. Shouldn’t one design your system for the worst case scenario?
The incident highlights a gap in the development of self-driving cars: maneuvering in bad weather. To address the problem, Czarnecki and Steven Waslander, a professor at the University of Toronto, compiled a data set of images from snowy and rainy Canadian roads. It includes footage of foggy camera views, blizzard conditions, and cars sliding around, captured over two winters. The individual frames are annotated so that a machine can interpret what the scene conveys. Autonomous driving systems typically use annotated images to inform algorithms that track a car’s position and plan its route.
The Canadian data should help researchers develop and test algorithms against challenging conditions. But the team also hopes it will prompt carmakers and startups to think more about bad-weather driving. “It’s in the interest of everyone to think about this,” Czarnecki says.
A few companies, including Alphabet’s Waymo and Argo, backed by Ford and Volkswagen, are testing self-driving cars in winter conditions. But as a whole, the industry is far more focused on demonstrating and deploying vehicles in fair-weather locations such as California, Arizona, Texas, and Florida.
As even optimists about self-driving cars temper forecasts on their arrival, testing in sunnier climates is seen as a way to move the technology out of first gear. But the warm-weather bias could limit where autonomous vehicles can be deployed, or cause problems if it is rolled out in colder climates too quickly.
“It’s a very noticeable blind spot,” says Alexandr Wang, CEO of Scale AI, which annotated Czarnecki’s data and works with other autonomous-driving companies. “Deploying autonomous vehicles in bad conditions is not really tackled, or really talked about.”
Inclement weather conditions are challenging for autonomous vehicles for several reasons. Snow and rain can obscure and confuse sensors, hide markings on the road, and make a car perform differently. Beyond this, bad weather represents a difficult test for artificial intelligence algorithms. Programs trained to pick out cars and pedestrians in bright sunshine will struggle to make sense of vehicles topped with piles of snow and people bundled up under layers of clothing.
“Your AI will be erratic,” Czarnecki says of the typical self-driving car faced with snow. “It’s going to see things that aren’t there and also miss things.”
Matthew Johnson-Roberson, a professor at the University of Michigan who is developing a delivery robot optimized for difficult inclement weather conditions, believes tackling bad weather may offer a way to gain a competitive edge. Troubling conditions are a major source of accidents, he says, so they arguably should be a priority.
For the complete article click here.
Note – If you liked this post click here to stay informed of all of the 3D laser scanning, geomatics, UAS, autonomous vehicle, Lidar News and more. If you have an informative 3D video that you would like us to promote, please forward to email@example.com and if you would like to join the Younger Geospatial Professional movement click here.