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.

自动驾驶车辆的全天候操作存在重大挑战，包括感知、决策、路径规划和控制模块。我们通过提出一种多模块和模块化系统架构来解决这个问题，考虑了不良天气条件对感知层、决策层和安全监控的影响，并通过在 WATonoBus 平台上进行近一年的日常服务，证明了我们的方法能够应对恶劣的天气条件，并从操作中观察到有价值的边缘案例。