Weather Recognition Algorithms for Autonomous Driving and Navigation and Positioning

The Automatic Driving System (ADS) heavily relies on multi-sensor perception and satellite navigation. Weather changes can affect the accuracy of machine vision and satellite navigation, leading to sensor performance degradation, errors in road condition assessment, and unclear decision-making, thereby increasing the safety risks of ADS. Different weather conditions can cause varying delays in the ionosphere and troposphere, affecting the propagation path and speed of navigation signals and introducing positioning errors. To obtain real-time weather information around the vehicle and assist ADS in navigating more safely and efficiently, we propose a weather phenomenon recognition algorithm based on residual and group convolutions—RGNet. This algorithm introduces a residual structure to mitigate issues of gradient instability and non-linear transformation imbalance during backpropagation and reduces overfitting. Group convolutions are utilized to decrease computational complexity and improve recognition efficiency. A medium-sized dataset is established to ensure the diversity and balanced distribution of weather image data. Experimental results demonstrate that, compared to traditional weather recognition methods, the RGNet network model achieves higher recognition accuracy and faster recognition speed while significantly reducing the number of network layers and parameters.