Advanced fractal two-scale method for 905 nm laser scattering calculation of water-covered asphalt pavement and coverage perception

Sudden changes in pavement coverage can easily result in a vehicle's loss of control. Accurately characterizing laser scattering properties is essential for intelligent vehicle sensors like lidars to perceive pavement coverage information. In response to significant issues such as low computational efficiency, poor accuracy of existing scattering models, and the lack of methods for perceiving pavement water coverage information, this paper introduces an advanced laser scattering calculation method capable of describing water coverage conditions based on the fractal two-scale method (FTSM). The hemispherical spatial scattering intensity distribution of water-covered asphalt pavement is calculated. The influence of water depth on laser scattering characteristics and lidar signals of asphalt pavement is investigated. On this basis, a method for identifying the water coverage state of asphalt pavement using lidar is proposed. Experimental verification shows that the root mean square error (RMSE) between the calculated results of the advanced FTSM (AFTSM) model and the experimental results is only 8.69 × 10^-4 sr^-1. Compared with the classical numerical simulation model based on Kirchhoff approximation and the geometric optics model, it demonstrates a substantial improvement in calculation accuracy. Moreover, the recognition success rate of the asphalt pavement water coverage perception method proposed in this paper reaches over 95%. This work could expand the information acquisition dimension of a vehicle-borne lidar, provide a theoretical basis and data support for the development of road photoelectric perception technology, and is significant for promoting the development of intelligent driving environment perception technology.