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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.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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