Fahed Hassanat and his team at Ottawa-based Sensor Cortek get behind the wheel of cars covered from bumper to rooftop in sensors in order to solve one of Canada's biggest roadblocks to autonomous vehicle adoption: snow. Hassanat is shown in this undated handout. THE CANADIAN PRESS/HO-Sensor Cortek
Snow can be hard to distinguish for sensors, which are often obscured and confused by bad weather, to detect, making it even more difficult to train self-driving vehicle software and algorithms."You have the snow, you have the rain, you have the fog, you have the dust," said Hassanat. Lidar and cameras are based on having line of sight, making them vulnerable to any obstruction between the sensor and the object it needs to detect, whereas radar doesn't need to "see" an object to detect it.
"The information that is coming from your sensors or your data is completely unreliable, simply because the software is unable to process occluded images or cluttered image with a lot of noise." The end goal for Sensor Cortek is creating a deep neural network and AI-based perception systems that can make autonomous vehicles -- and anything else needing sensors of the same nature -- "see" better in all visibility conditions and ultimately, operate safety.