The CADC dataset aims to promote research to improve self-driving in adverse weather conditions. This is the first public dataset to focus on real world driving data in snowy weather conditions. It features:

For this dataset, routes were chosen with various levels of traffic, a variety of vehicles and always with snowfall. Sequences were selected from data collected within the Region of Waterloo, Canada.

We collected data using the Autonomoose, a Lincoln MKZ Hybrid mounted with a full suite of LiDAR, inertial and vision sensors. Please refer to the figure below for the sensor configuration of the Autonomoose.

Scale’s data annotation platform combines human work and review with smart tools, statistical confidence checks and machine learning checks to ensure the quality of annotations. The resulting accuracy is consistently higher than what a human or synthetic labeling approach can achieve independently as measured against seven rigorous quality areas for each annotation.