在現代科技的快速發展中，自動駕駛技術已經成為汽車行業的一個核心領域。然而自動駕駛技術的應用在不同的天氣條件下，特別是在降雨天氣下，仍然面臨著重要的挑戰。天氣條件的不確定性和多變性使得自動駕駛系統難以適應，進而影響其安全性和效能。
因此近年來，許多研究致力於解決自動駕駛系統在不同天氣條件下的問題。其中，雨量大小成為一個值得關注的研究方向。在雨天行駛時，降雨的強弱會直接影響自動駕駛系統的感知和決策，因此準確地分類雨量大小對於提高自動駕駛系統的性能至關重要。
本研究旨在通過深入研究和分析，開發一種有效的雨量大小分類方法，以幫助自動駕駛系統提升在降雨天氣下的表現。我們將採用 CNN 作為分類器，結合帶有雨量標籤之雨滴數據集和雨水特徵分離網路之特徵提取方法，以實現對雨量大小的準確分類。期望可以提升自動駕駛系統在實際應用中的安全性和效能，同時為汽車行業的未來發展做出寶貴貢獻。

In the rapid development of modern technology, autonomous driving technology has
become a core area in the automotive industry. However, the application of autonomous
driving technology still faces significant challenges under different weather conditions,
especially in rainy weather. The uncertainty and variability of weather conditions make
it difficult for autonomous driving systems to adapt, thereby affecting their safety and
performance.
Therefore, in recent years, many studies have focused on addressing the issues of
autonomous driving systems under different weather conditions. Among them, the classification of rainfall intensity has become a noteworthy research direction. When driving in
rainy weather, the intensity of rainfall directly affects the perception and decision-making
of autonomous driving systems. Therefore, accurate classification of rainfall intensity is
crucial for improving the performance of autonomous driving systems.
This study aims to develop an effective method for classifying rainfall intensity through
in-depth research and analysis, to help autonomous driving systems enhance their performance in rainy weather conditions. We will use CNN as the classifier, combined with
a feature extraction method that separates rainwater features, using datasets labeled with
rainfall amounts. It is expected that this approach can improve the safety and performance
of autonomous driving systems in real-world applications, while making valuable contributions to the future development of the automotive industry.

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