Road Surface Friction Estimation Based on LiDAR Reflectivity for Intelligent Vehicle

The road surface friction coefficient is a key factor in the decision-making and control strategies of autonomous driving systems. This study presents a groundbreaking method for estimating the road surface friction coefficient using light detection and ranging (LiDAR) point cloud data, enhancing autonomous vehicles’ prospective and high-precision perception. Data from eight road types formed a robust dataset. Cloth simulation filtering (CSF) and the random sample consensus (RANSAC) algorithm extracted road point clouds accurately. Gaussian filtering then removed reflectivity outliers. Given the correlation among reflectivity, distance, and incident angle, the road surface was segmented for comprehensive feature extraction. A designed deep neural network (DNN) model, trained rigorously with the dataset, achieved road recognition. Using statistical knowledge of road materials and peak friction coefficients determined the road’s friction coefficient. Validation showed the algorithm identifies road types with over 99.62% accuracy, at 55 ms per cycle. This ensures real-time, high-precision estimation of the peak friction coefficient, a major boost for autonomous driving systems.