Guiding Autonomous Vehicles in Challenging Weather Using Machine Learning Techniques

The emergence of autonomous cars has transformed transport, providing enhanced safety, efficiency, and convenience. Yet, it is still difficult to incorporate these cars into regular traffic, especially under unfavorable weather conditions like rain, snow, fog, and storms, which greatly affect the performance of their perception and sensing systems. This paper surveys the current state of machine learning, sensor fusion, and intelligent transportation systems (ITS) to improve autonomous vehicle operation in adverse environments. Different methodologies such as Support Vector Machines (SVM), Deep Neural Networks (DNN), and Convolutional Neural Networks (CNN) are analyzed for their efficiency in traffic congestion forecasting and vehicular control. In addition to this, RSUs' use in traffic observation, smart cloud-based transport infrastructure, and automobile location tracking are examined. Outcomes show that sophisticated machine learning algorithms and sensor fusion are both effective in the reduction of negative impacts of unfavorable weather on the navigation of self-driving cars. This review highlights the importance of additional research in the integration of strong machine learning models and smart transportation solutions to make autonomous vehicle deployment safer and more reliable under varying environmental conditions.