Automatic collection method of three-dimensional pavement information spectrum

Abstract

The invention discloses an automatic collection method of a three-dimensional road surface information spectrum, which is applied to a vehicle comprising a suspension mechanism and an information collection mechanism and comprises the following steps: 1, collecting vertical road surface information data; 2, obtaining an output variable of the kth cycle according to the quarter vehicle suspension two-degree-of-freedom kinetic model; 3, calculating the pavement relative roughness and the power spectral density of the kth cycle; 4, calculating an estimated pavement roughness result of the kth cycle; 5, obtaining the pavement relative roughness of the (k + 1) th cycle based on the discrete Kalman filtering prediction model; 6, calculating a pavement adhesion coefficient; and 7, coupling the road surface relative roughness and the road surface adhesion coefficient by using the timestamp of the positioning coordinate to obtain a road surface information spectrum of the kth cycle. According to the invention, road surface information can be collected by using the existing suspension structure and the sensor thereof, so that the road surface information spectrum can be quickly and accurately established at low cost.

Description

technical field [0001] The invention relates to the field of road surface information collection and acquisition, and is specifically described as a three-dimensional automatic acquisition method for a three-dimensional road surface information spectrum. Background technique [0002] In the 21st century, the human transportation system has been gradually perfected. As one of the most important components of the ground transportation system, roads already exist all over the world and serve as the link between regions. Existing roads are mainly divided into asphalt pavement, concrete pavement and non-paved road. Due to different road construction conditions, long-term natural weathering and erosion, and normal traffic flow, there are no two roads with the same surface roughness and adhesion coefficient in the world. In order to facilitate the identification of good and bad road conditions, the current existing methods divide the road surface into A, B, C... grades according ...

AI Technical Summary

Problems solved by technology

When the graded road surface is damaged or the roughness of the road surface and the adhesion coefficient change dynamically due to precipitation and other conditions, it is impossible to carry out municipal maintenance or release information such as the slippery road surface in time according to the actual situation, resulting in many unnecessary losses

[0004] At present, there are many dynamic management systems for road surface information, but most of them only collect vertical or longitudinal information for road classification, and need to use expensive special equipment and vehicles for information collection. This is undoubtedly the road information in many areas. Collection brings constraints on conditions

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