Self-driving cars promise to improve mobility, build a more efficient

transportation system and even save lives.

But while the industry has been making great progress, to date the field has

struggled to build cars and artificial intelligence models that can handle all

weather conditions.

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.

Take a car trained to drive on the wide, sunny boulevards of Arizona or

California and place it on a snowy street like this, and things will go wrong:

Snow covering the road markings would make it hard for the car to stay in its

lane.

Accumulated snow significantly reduces the color contrast, making it harder

for the car’s system to recognize objects, like trees or parked cars covered

in snow—or even pedestrians.

Most problematically, snowfall makes it hard for a vehicle’s cameras and LiDAR

sensors to see, which significantly reduces safety:

A skilled human driver can handle the same road in all weathers—but today’s AV

models can’t generalise their experience in the same way. To do so, they need

much more data.

Until now, there has not yet been an open dataset of annotated LiDAR data and

images of driving in snowy weather that researchers can use to develop more

robust systems.

CADC: A new dataset for wintry conditions

In partnership with the University of Waterloo and the University of Toronto,

we’ve been applying our technology to this challenge.

Today, we’re open sourcing the world’s first AV dataset in wintry

conditions—the Canadian Adverse Driving Conditions dataset (CADC, or cad-see.)

Drawn from 20 km of driving in harsh, snowy conditions in southwestern Ontario

over the past two winters, CADC contains 7,000 frames of LiDAR and camera data

spread over 70 driving sequences—featuring a wide range of perception

challenges for AV models that can handle wintry conditions. Scale provided the

annotations that makes the data usable by researchers.

Researchers at the Universities of Waterloo and Toronto have also released a

detailed academic paper outlining the technical features of the data collected

and object detection labels provided, which you can view on arXiv

here.

Ultimately, this is about safety. Without robust data, self-driving models

can’t learn how to handle snowy conditions—preventing us from being able to

make AV systems a reality for the many millions of people who live in places

that receive wintry weather.

As Professor Krzyzstof Czarnecki at the University of Waterloo says, “We want

to engage the research community to generate new ideas and enable innovation.

This is how you can solve really hard problems, the problems that are just too

big for anyone to solve on their own.”

We hope this dataset will make it easier for researchers to achieve the

next-generation breakthroughs in AV systems that will take us one step closer

to self-driving vehicles that work safely in a wide range of different

environments.

As Professor Steven Waslander at the University of Toronto says, “We’re hoping

that both industry and academia go nuts with it. We want the world to be

working on driving everywhere.

“Bad weather is a condition that is going to happen.”

You can find the dataset on the

Waterloo website, with dataset

support tools available on

GitHub.