Real-Time Road Adhesion Prediction for Advanced Driver-Assistance Systems Using Vehicle Lidar Point Clouds

Real-time prediction of road adhesion conditions ahead of the vehicle is critical for emergency braking,stability control, and trajectory planning in advanced driver-assistance systems (ADAS). To overcome the limitationsof conventional vehicle-dynamics-based methods, which mainly rely on tire–road contact responses and lack preview perception capability, this article proposes a real-time road adhesion prediction method based on onboard lidarpoint clouds. First, point cloud data were collected from four typical road surface conditions, including dry asphalt,wet asphalt, dry concrete, and wet concrete. Random sample consensus (RANSAC)-based ground segmentation andreflectivity filtering were then applied to remove nonroad points and highly reflective outliers. Subsequently, a speedadaptive dynamic region of interest (ROI) was constructed by incorporating a vehicle safety distance model, enablingpreview perception of the target road surface ahead. On this basis, the processed point clouds were converted intomultichannel pseudoimages, and a convolutional neural network (CNN)-vision transformer classification model wasdeveloped to enhance local reflectance texture feature extraction and global context modeling. Finally, based onthe empirical mapping relationship between road surface type and friction coefficient, the road surface recognitionresults were converted into road adhesion prediction results. Experimental results show that the proposed methodachieves a better tradeoff between recognition accuracy and real-time performance. Compared with conventionalvehicle-dynamics-based estimation methods, the proposed method significantly reduces the mean absolute error andmean absolute percentage error in both homogeneous road and transition road scenarios, verifying its effectivenessfor real-time forward road adhesion prediction in ADAS.