Autonomous vehicles face significant challenges in navigating adverse weather, particularly rain, due to the visual impairment of camera-based systems. In this study, we leveraged contemporary deep learning techniques to mitigate these challenges, aiming to develop a vision model that processes live vehicle camera feeds to eliminate rain-induced visual hindrances, yielding visuals closely resembling clear, rain-free scenes. Using the Car Learning to Act (CARLA) simulation environment, we generated a comprehensive dataset of clear and rainy images for model training and testing. In our model, we employed a classic encoder-decoder architecture with skip connections and concatenation operations. It was trained using novel batching schemes designed to effectively distinguish high-frequency rain patterns from low-frequency scene features across successive image frames. To evaluate the model performance, we integrated it with a steering module that processes front-view images as input. The results demonstrated notable improvements in steering accuracy, underscoring the model's potential to enhance navigation safety and reliability in rainy weather conditions.

本研究解决了自主车辆在雨天导航时遇到的视觉障碍问题。通过利用现代深度学习技术，我们开发了一种视觉模型，对实时车辆摄像头图像进行处理，以消除雨水导致的视觉干扰，提供接近晴朗场景的视觉效果。研究结果显示，该模型显著提高了转向精度，有望增强雨天条件下的导航安全性与可靠性。

增强雨天自主车辆安全性的 数据驱动方法以实现清晰视觉

增强雨天自主车辆安全性的数据驱动方法以实现清晰视觉