WAVES - Waveform Analysis for Vehicles in Extreme Scenarios

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Lidars are sensors using light to measure distances to reflecting objects and to generate a 3D representation of the surroundings. They are critical sensors commonly installed on autonomous cars to detect obstacles and accurately locate vehicles on roads. The current trend is to use increasingly higher definition sensors, thus producing a large amount of data. In turn, this trend drives the cost upward for the entire vehicle. Moreover, these high-definition lidars generally rely on a spinning motor to scan the surroundings, which make them sensitive to premature failure due to vibrations. This weakness is a concern for long-term safety of autonomous vehicles. A different approach is to use sensors without moving parts named solid-state lidars. This research collaboration between LeddarTech and Laval University aims at improving perception capabilities of autonomous vehicles in Nordic conditions using these solid-state lidars. The first objective of the program is to use machine learning algorithms to better detect and identify moving obstacles on the road. The second objective is to integrate continuous intensity measurements into probabilistic mapping algorithms. Both of these objectives will be validated against winter conditions to ensure safety in low- visibility conditions. This research program will impact the transportation system by easing the integration of autonomous vehicle technology in Canadian driving conditions.