Deep Learning-Based Detection Systems for Autonomous Vehicles in Challenging Weather Conditions

Weather detection systems (WDS) are essential in enhancing decision-making for autonomous vehicles, specifically under challenging and adverse weather conditions. Autonomous systems can effectively classify outdoor weather scenarios using deep learning (DL) techniques, allowing seamless adaptation to dynamic environmental changes. This study introduces a robust DL-driven framework to classify diverse weather conditions and aid autonomous vehicle navigation in typical and extreme scenarios. The proposed framework utilizes advanced transfer learning methods alongside a high-performance Nvidia GPU to evaluate the efficiency of three convolutional neural networks (CNNs): MobileNetV2, DenseNet121, and VGG-16. The experiments were conducted using two comprehensiveweather imaging datasets, DAWN2020 and MCWRD2018, combined to classify six distinct weather categories: cloudy, rainy, snowy, sandy, sunny, and sunrise. Experimental outcomes showed outstanding performance for all models, with the MobileNetV2-based systemperforming the highest detection accuracy, precision, and sensitivity of 97.92%, 97.88%, and 97.95%, respectively. Furthermore, the framework achieved a rapid inference time, with an average processing speed of 7 milliseconds per inference using the GPU. Comparative analysis with existing models of the effectiveness of the presented approach showcases advancements in classification accuracy by a margin of 0.3% to 19.8%. These outcomes provide the framework's practicality classification to facilitate reliable decision-making for autonomous vehicles in diverse conditions