Enhancing autonomous vehicle safety in rain: a data-centric approach for clear vision

• URL: http://arxiv.org/abs/2412.20565v1
• Date: Sun, 29 Dec 2024 20:27:12 GMT
• Title: Enhancing autonomous vehicle safety in rain: a data-centric approach for clear vision
• Authors: Mark A. Seferian, Jidong J. Yang,
• Abstract summary: We developed a vision model that processes live vehicle camera feeds to eliminate rain-induced visual hindrances. We employed a classic encoder-decoder architecture with skip connections and concatenation operations. The results demonstrated notable improvements in steering accuracy, underscoring the model's potential to enhance navigation safety and reliability in rainy weather conditions.
• Score: 0.0
• License:
• Abstract: 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.

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