Enhancing Autonomous Driving in Adverse Weather: Road Surface Classification and Image Restoration Evaluation

The integration of artificial intelligence into transportation has pushed for the development of autonomous vehicles, promising improved safety and convenience. Despite numerous advancements, however, autonomous vehicles remain unreliable in adverse weather conditions including rain, fog, snow, and darkness. Current methods of scene mapping rely on LiDAR technology, which tends to fail when exposed to weather that distorts its signals. This issue emphasizes the need for robust image-based systems for suboptimal driving conditions. Two models, CLIP and ResNet, were selected, both of which offer unique approaches toward image-based weather classification compared to conventional models such as convolutional neural networks (CNNs). Using a self-compiled custom dataset of 4800 road images in varying conditions, each model was trained and tested. ResNet-50 was the most effective model, reaching an accuracy of 0.95 on the testing data set. The predictions of road surface weather from the models were compared to empirically determined data to estimate the coefficient of friction, which can be used to maximize safety. Furthermore, an image restoration model—a model that removes weather effects such as raindrops—was analyzed and its performance was measured quantitatively. Algorithmically measuring the confidence of object detection numerically showed the improvement from the original images to the restored images, serving as a reliable evaluation technique. This concept could be used as an optimizer for these models to maximize their performance. Overall, this research reveals the potency of previously unused techniques for the development of autonomous driving and serves as a foundation for future developments involving adverse weather.