Road Adhesion Coefficient Estimation Based on LiDAR Reflectance Intensity

Road surface condition is a crucial factor affecting vehicle safety. Accurate and rapid identification of road conditions can enhance the adaptability of active safety control systems to adapt to complex working conditions, while predicting the road adhesion coefficient in advance can help active safety control systems adjust control strategies promptly to ensure driving safety and stability. This article introduces an algorithm that combines the squeeze-and-excitation network (SENet) and convolutional neural network (CNN) to estimate the road adhesion coefficient based on the reflectance intensity features of light detection and ranging (LiDAR) point clouds. The method mainly comprises the following seven steps: 1) data acquisition; 2) point cloud segmentation; 3) selection of the point cloud region of interest (ROI); 4) filtering processing; 5) grayscale image generation; 6) classification based on the SE-CNN model; and 7) combination of the graph method to estimate the road adhesion coefficient. Data were collected on urban roads to construct the dataset. Experimental results indicate that different lighting conditions and speeds have minimal impact on the distribution of reflectance intensity in regional point clouds. The classification accuracy of this research method for dry asphalt, wet asphalt, dry concrete, and wet concrete reached 100%, 99.98%, 99.82%, and 99.23%, respectively. Compared to other machine learning methods, the proposed method exhibits higher accuracy and stability in predicting road surface adhesion coefficients.