Road surface wet and slippery state identification method

Abstract

The invention relates to a road surface wet and slippery state identification method, which comprises the following steps: 1) establishing a road surface wet and slippery state identification BP artificial neural network comprising an input layer, a hidden layer and an output layer; 2) measuring air humidity, road surface temperature, temperature difference between the road surface temperature and air temperature and road surface wet and slippery state of different months, different regions and different times and defining an input vector and an output vector; 3) utilizing the measured values in the step 2) to train the artificial neural network in an off-line manner for obtaining IW, b, IW and b; 4) embedding IW, IW, b and b obtained by training in the step 3) in a vehicle-mounted ECU and calculating the output vector according to the actually input mouth, region, time, humidity and the temperature difference between the road surface temperature and the air temperature; and 5) judging the road surface wet and slippery state according to the output vector calculated in the step 4) and the output vector defined in the step 2) and providing the corresponding road surface wet and slippery state for a driver through a display device. The method can accurately identify the road surface wet and slippery state, facilitate the calculation of road surface maximum adhesion coefficient of the vehicle-mounted ECU and provide a powerful guarantee for travel safety.

Description

technical field [0001] The invention relates to the technical field of vehicle driving safety, in particular to a method for identifying a wet and slippery road surface state. Background technique [0002] The braking force and driving force necessary to ensure the safe and smooth driving of the vehicle (such as ABS, ASR, EBD, ESP, etc.) depend to a large extent on the road surface adhesion coefficient. The road surface adhesion coefficient is related to the wet and slippery state of the road surface. The wet and slippery road surface includes dry road surface, wet road surface, snowy road surface and icy road surface. Such as figure 1 As shown, the dry road surface adhesion coefficient can reach 1.0, the wet road surface adhesion coefficient is less than 0.8, the snow surface adhesion coefficient is below 0.2, and the ice surface adhesion coefficient is less than 0.1. It can be seen that in order to obtain greater braking force and driving force, the vehicle must first id...

AI Technical Summary

Problems solved by technology

But for the "Infraredimaging system" system, the road surface is relatively rough, which will scatter the infrared rays emitted by the system and reduce the infrared reflection intensity. At the same time, the external environment temperature also affects the reflected infrared intensity.

[0006] The above systems mainly identify the ice layer on the road surface, but do not identify the wet state of the road surface, and have not considered the influence of month, region, time and humidity on the identification of the ice layer on the road surface, so the accuracy of the identification result is not high

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