  Autonomous vehicle all-weather operation poses significant challenges,
encompassing modules from perception and decision-making to path planning and
control. The complexity arises from the need to address adverse weather
conditions like rain, snow, and fog across the autonomy stack. Conventional
model-based and single-module approaches often lack holistic integration with
upstream or downstream tasks. We tackle this problem by proposing a
multi-module and modular system architecture with considerations for adverse
weather across the perception level, through features such as snow covered curb
detection, to decision-making and safety monitoring. Through daily weekday
service on the WATonoBus platform for almost a year, we demonstrate that our
proposed approach is capable of addressing adverse weather conditions and
provide valuable learning from edge cases observed during operation.
