New data set helps train cars to drive autonomously in winter weather

While the most sophisticated driverless cars on public roads can handle haboobs and rainstorms like champs, certain types of precipitation remain a challenge for them -- like snow. That's because snow covers cameras critical to those cars' self-awareness and tricks sensors into perceiving obstacles that aren't there, and because snow obscures road signs and other structures that normally serve as navigational landmarks. In an effort to spur on the development of cars capable of driving in wintry weather, startup Scale AI this week open-sourced Canadian Adverse Driving Conditions (CADC), a data set containing over 56,000 images in conditions including snow created with the University of Waterloo and the University of Toronto. While several corpora with snowy sensor samples have been released to date, including Linköping University's Automotive Multi-Sensor Dataset (AMUSE) and the Mapillary Vistas data set, Scale AI claims that CADC is the first to focus specifically on "real-world" driving in snowy weather. "Snow is hard to drive in -- as many drivers are well aware. But wintry conditions are especially hard for self-driving cars because of the way snow affects the critical hardware and AI algorithms that power them," wrote Scale AI CEO Alexandr Wang in a blog post.