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For autonomous vehicles to viably replace human drivers they must contendwith inclement weather. Falling rain and snow introduce noise in LiDAR returnsresulting in both false positive and false negative object detections. In thisarticle we introduce the Winter Adverse Driving dataSet (WADS) collected in thesnow belt region of Michigan's Upper Peninsula. WADS is the first multi-modaldataset featuring dense point-wise labeled sequential LiDAR scans collected insevere winter weather; weather that would cause an experienced driver to altertheir driving behavior. We have labelled and will make available over 7 GB or3.6 billion labelled LiDAR points out of over 26 TB of total LiDAR and cameradata collected. We also present the Dynamic Statistical Outlier Removal (DSOR)filter, a statistical PCL-based filter capable or removing snow with a higherrecall than the state of the art snow de-noising filter while being 28\%faster. Further, the DSOR filter is shown to have a lower time complexitycompared to the state of the art resulting in an improved scalability.  Our labeled dataset and DSOR filter will be made available athttps://bitbucket.org/autonomymtu/dsor_filter

DSOR: A Scalable Statistical Filter for Removing Falling Snow from LiDAR Point Clouds in Severe Winter Weather