Road Estimation Method for Intelligent Vehicles Based on Laser Intensity Distribution Feature Analysis

Abstract:

In order to improve the driving safety and adaptability of intelligent vehicles to application scenarios under extreme conditions such as high speed and large lateral slipping state， the technology of tire-road peak adhesion coefficient （TRPAC） estimation （hereinafter referred to as road estimation） has aroused more and more attention in the field of active safety control. A road estimation method based on lidar is proposed. Based on theory of maximum likelihood， laser intensity distribution model parameters of several typical kinds of structured road surfaces are solved， based on which， the database of typical road surfaces is established. By using Kullback-Leibler Divergence （KLD） to represent the similarity of intensity distributions， road surfaces can be classified according to the established database， and the estimated value of TRPAC can then be mapped. The experiment results show that the proposed method can estimate TRPAC with an accuracy of more than 90%， that the sudden change of road condition can be sensitively detected， and that the proposed method has robustness to different illumination conditions in the day and at night.

Key words:intelligent vehicles;tire-road adhesion coefficient;road estimation;lidar;Kullback-Leibler divergence