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。

WATonoBus：一款全天候自动驾驶班车

WATonoBus: An All Weather Autonomous Shuttle

In this paper, we investigate the application of end-to-end and multi-module
frameworks for G2P conversion for the Persian language. The results demonstrate
that our proposed multi-module G2P system outperforms our end-to-end systems in
terms of accuracy and speed. The system consists of a pronunciation dictionary
as our look-up table, along with separate models to handle homographs, OOVs and
ezafe in Persian created using GRU and Transformer architectures. The system is
sequence-level rather than word-level, which allows it to effectively capture
the unwritten relations between words (cross-word information) necessary for
homograph disambiguation and ezafe recognition without the need for any
pre-processing. After evaluation, our system achieved a 94.48% word-level
accuracy, outperforming the previous G2P systems for Persian.

本研究探讨了针对波斯语的 G2P 转换的端到端和多模块框架的应用，结果表明，我们的多模块 G2P 系统在准确性和速度方面优于我们的端到端系统。该系统采用发音字典作为我们的查找表，并使用 GRU 和 Transformer 架构创建用于处理 Persian 的同音异型体，OOVs 和 ezafe 的独立模型。经过评估，我们的系统在单词级准确率上达到了 94.48％，优于以前的波斯语 G2P 系统。