基于点云反射特性的前方道路附着系数估计方法研究

doi:10.19562/j.chinasae.qcgc.2024.10.011

Abstract

Abstract:

The road friction coefficient is a significant factor that impacts the decision-making control strategy of the autonomous driving system. To achieve prospective and high-precision perception of the road friction coefficient， a novel estimation method for road friction coefficient based on the LiDAR equipped in vehicles is proposed in this paper. Firstly， a road dataset is constructed by collecting data from dry asphalt， concrete， wet asphalt， icy， and snowy road surface. Then， road point cloud is extracted using cloth simulation filtering and RANSAC algorithms， and abnormal noise points are removed based on Gaussian filtering. The road surface is divided into different regions according to the variation of point cloud reflectivity with distance and incident angle， and features are extracted accordingly. A road recognition model is constructed based on the deep neural network and trained by the collected dataset. Finally， the friction coefficient of the road ahead is determined based on the statistical experience of road material and peak friction coefficient. The test results show that the proposed algorithm achieves road type recognition accuracy of over 99.3%， with an average running cycle of 55ms， enabling real-time and high-precision estimation of the road peak friction coefficient.

Key words: road friction coefficient, LiDAR point cloud, cloth simulation filtering, RANSAC, deep neural network, Gaussian filtering, road type recognition

Hongyu Hu,Minghong Tang,Fei Gao,Mingxi Bao,Zhenhai Gao. Research on the Estimation Method of Road Friction Coefficient Ahead Based on Point Cloud Reflection Properties[J].Automotive Engineering, 2024, 46(10): 1842-1852.

Figures/Tables 23

References 28

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路面类型	训练集数据量	测试集数据量
干燥柏油路面	36 457	9 914
积雪路面	38 962	9 741
混凝土路面	55 710	13 927
结冰路面	43 960	10 990
湿滑柏油路面	40 350	10 087

Research on the Estimation Method of Road Friction Coefficient Ahead Based on Point Cloud Reflection Properties

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