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CN-115965920-B - Road surface condition monitoring method, device, vehicle and storage medium

CN115965920BCN 115965920 BCN115965920 BCN 115965920BCN-115965920-B

Abstract

The application discloses a road surface condition monitoring method, a device, a vehicle and a storage medium, wherein the road surface condition monitoring method comprises the steps of obtaining a target road section identifier of a target road surface based on a preset first map, obtaining the target road section identifier based on the first map in advance, matching corresponding road surface clustering road sections according to the target road section identifier, extracting and clustering the characteristics of the road surface clustering road sections on the corresponding road surfaces based on different vehicles, and obtaining the current condition of the target road surface according to the road surface clustering road sections. And the road surface rectangular frames are obtained by feature extraction and clustering on the corresponding road surfaces based on different vehicles, so that the road surface conditions can be effectively monitored in the vehicle driving process, and the vehicle driving comfort and safety are improved.

Inventors

  • LI MENGLIN
  • LV WENJIE
  • HUANG JIANYU

Assignees

  • 广东鲲鹏空间信息技术有限公司

Dates

Publication Date
20260512
Application Date
20221202

Claims (13)

  1. 1. A method of monitoring road conditions, the method comprising the steps of: acquiring a target road section identifier of a target road surface based on a preset first map, wherein the target road section identifier is obtained by marking in advance based on the first map; Matching corresponding road surface clustering road sections according to the target road section identification, wherein the road surface clustering road sections are obtained by carrying out feature extraction and clustering on corresponding road surfaces based on different vehicles; obtaining the current condition of the target road surface according to the road surface clustering road sections; The road surface clustering road sections comprise road surface rectangular frames, and before the step of matching the corresponding road surface clustering road sections according to the target road section identifiers, the road surface clustering road sections further comprise: Acquiring road surface data acquired by different vehicles on corresponding road surfaces, and acquiring the driving trails of the different vehicles on the corresponding road surfaces; obtaining the road surface attribute of the corresponding road surface based on the road surface data, wherein the road surface attribute is the attribute of a bumpy road surface and is used for clustering different bumpy road surfaces; and carrying out feature extraction and clustering on the driving tracks based on the road surface attributes to obtain corresponding road surface rectangular frames.
  2. 2. The method for monitoring road surface conditions according to claim 1, wherein the step of extracting features of the wheel paths based on the road surface attributes and clustering the extracted features to obtain corresponding rectangular frames of the road surface comprises: breaking the travelling path into a straight line segment sequence; extracting features of the straight line segment sequence to obtain corresponding track geometric features; combining the track geometric features according to the road surface attributes to obtain corresponding clustering groups; Extracting the characteristics of the clustering groups to obtain corresponding clustering geometric characteristics; And merging the clustering geometrical features according to the geometrical relationship of the clustering geometrical features to obtain a corresponding pavement rectangular frame.
  3. 3. The method of claim 2, wherein the step of merging the trajectory geometry features according to the road surface properties to obtain corresponding cluster groupings comprises: Detecting whether the pavement attribute is matched with the clustering attribute of the current clustering group; if the pavement attribute is matched with the clustering attribute, combining the track geometric feature with the corresponding clustering group; And if the pavement attribute is not matched with the clustering attribute, generating a clustering group except the current clustering group.
  4. 4. The method for monitoring road surface condition according to claim 1, wherein the road surface clustering section further comprises a road surface area frame, and the step of extracting and clustering the characteristics of the wheel paths based on the road surface attribute to obtain a corresponding road surface rectangular frame further comprises: and combining the pavement rectangular frames according to the distances among different pavement rectangular frames to obtain the pavement area frame.
  5. 5. The method for monitoring road surface condition according to claim 1, wherein the road surface clustering section further comprises a road rectangular frame, and the step of extracting and clustering the characteristics of the wheel paths based on the road surface attribute to obtain a corresponding road surface rectangular frame further comprises: And combining the road rectangular frame with a preset second map to obtain the road rectangular frame.
  6. 6. The method of claim 1, wherein the road surface data includes horizontal axis angle data and vertical axis angle data, and the step of obtaining the road surface property of the corresponding road surface based on the road surface data includes: Analyzing the pavement data and determining a corresponding detection mode; and obtaining the pavement attribute of the corresponding pavement according to the detection mode.
  7. 7. The method of claim 6, wherein analyzing the road surface data to determine a corresponding detection pattern comprises: acquiring a preset first speed coefficient; Calculating the sum of the variances of the horizontal axis angle data and the variances of the vertical axis angle data to obtain a first sum of variances; and if the sum of the first variances is larger than the first speed coefficient, determining that the detection mode is rough road detection.
  8. 8. The method of claim 6, wherein the road surface data further includes pitch angle data, and wherein the step of analyzing the road surface data to determine the corresponding detection pattern includes: Calculating the sum of the variances of the horizontal axis angle data and the variances of the vertical axis angle data to obtain a second sum of variances; And if the pitching angle is larger than a preset first value and the sum of the second variances is larger than a preset second value, determining that the detection mode is ramp detection.
  9. 9. The method of claim 6, wherein analyzing the road surface data to determine a corresponding detection pattern comprises: Acquiring a preset second speed coefficient; acquiring a horizontal axis variance of the horizontal axis angle data and a vertical axis variance of the vertical axis angle data; and if the vertical axis variance is larger than the second speed coefficient and the horizontal axis variance is smaller than a preset third numerical value, determining that the detection mode is deceleration strip detection.
  10. 10. The method for monitoring road surface condition according to claim 6, wherein after the step of extracting features of the wheel paths based on the road surface properties and clustering to obtain the corresponding rectangular frame of the road surface, further comprises: acquiring and analyzing clustering road section data acquired by different vehicles on corresponding roads aiming at the road surface clustering road sections; and if the clustered road section data does not accord with the detection mode, deleting the clustered road section of the road surface.
  11. 11. A road surface condition monitoring device, characterized in that the road surface condition monitoring device comprises: The road surface identification module is used for acquiring a target road section identifier of a target road surface based on a preset first map, wherein the target road section identifier is obtained by marking in advance based on the first map; The road surface matching module is used for matching corresponding road surface clustering road sections according to the target road section identification, wherein the road surface clustering road sections are obtained by carrying out feature extraction and clustering on corresponding road surfaces based on different vehicles; The condition acquisition module is used for acquiring the current condition of the target road surface according to the road surface clustering road sections; The road surface clustering road section includes road surface rectangle frame, road surface situation monitoring devices still is used for: Acquiring road surface data acquired by different vehicles on corresponding road surfaces, and acquiring the driving trails of the different vehicles on the corresponding road surfaces; obtaining the road surface attribute of the corresponding road surface based on the road surface data, wherein the road surface attribute is the attribute of a bumpy road surface and is used for clustering different bumpy road surfaces; and carrying out feature extraction and clustering on the driving tracks based on the road surface attributes to obtain corresponding road surface rectangular frames.
  12. 12. A vehicle comprising a memory, a processor and a road condition monitoring program stored on the memory and operable on the processor, which when executed by the processor, implements the steps of the road condition monitoring method of any one of claims 1-10.
  13. 13. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a road surface condition monitoring program which, when executed by a processor, implements the steps of the road surface condition monitoring method according to any one of claims 1-10.

Description

Road surface condition monitoring method, device, vehicle and storage medium Technical Field The present application relates to the field of vehicle driving technologies, and in particular, to a method and apparatus for monitoring road surface conditions, a vehicle, and a storage medium. Background In recent years, with the promotion of economical strength, the living standard of people is greatly promoted, and more convenient traffic also greatly promotes the living and working efficiency of people. With the rapid development of the automobile industry, vehicle driving comfort and safety have become important issues. However, in daily driving, there are cases where the road surface is bumpy, for example, many roads may have relatively large pits due to various reasons such as road surface excavation, or frequent deceleration strips are provided on a certain section of road surface, or steep slopes are present. The condition of road surface jolt not only affects the comfort level of users, but also can scratch the chassis of the vehicle. Disclosure of Invention The application mainly aims to provide a road surface condition monitoring method, a road surface condition monitoring device, a vehicle and a storage medium, and aims to effectively monitor the road surface condition during driving of the vehicle and improve the driving comfort and safety of the vehicle. In order to achieve the above object, the present application provides a road surface condition monitoring method comprising: acquiring a target road section identifier of a target road surface based on a preset first map, wherein the target road section identifier is obtained by marking in advance based on the first map; Matching corresponding road surface clustering road sections according to the target road section identification, wherein the road surface clustering road sections are obtained by carrying out feature extraction and clustering on corresponding road surfaces based on different vehicles; and obtaining the current condition of the target road surface according to the road surface clustering road sections. Optionally, the road surface clustering road section includes a road surface rectangular frame, and before the step of matching the corresponding road surface clustering road section according to the target road section identifier, the method further includes: Acquiring road surface data acquired by different vehicles on corresponding road surfaces, and acquiring the driving trails of the different vehicles on the corresponding road surfaces; obtaining the pavement attribute of the corresponding pavement based on the pavement data; and carrying out feature extraction and clustering on the driving tracks based on the road surface attributes to obtain corresponding road surface rectangular frames. Optionally, the step of extracting features of the driving tracks and clustering the driving tracks based on the road surface attribute to obtain a corresponding rectangular road surface frame includes: breaking the travelling path into a straight line segment sequence; extracting features of the straight line segment sequence to obtain corresponding track geometric features; combining the track geometric features according to the road surface attributes to obtain corresponding clustering groups; Extracting the characteristics of the clustering groups to obtain corresponding clustering geometric characteristics; And merging the clustering geometrical features according to the geometrical relationship of the clustering geometrical features to obtain a corresponding pavement rectangular frame. Optionally, the step of merging the track geometric features according to the road surface attribute to obtain a corresponding cluster group includes: Detecting whether the pavement attribute is matched with the clustering attribute of the current clustering group; if the pavement attribute is matched with the clustering attribute, combining the track geometric feature with the corresponding clustering group; And if the pavement attribute is not matched with the clustering attribute, generating a clustering group except the current clustering group. Optionally, the road surface clustering road section further includes a road surface area frame, and after the step of extracting and clustering the characteristics of the driving tracks based on the road surface attribute to obtain a corresponding road surface rectangular frame, the method further includes: and combining the pavement rectangular frames according to the distances among different pavement rectangular frames to obtain the pavement area frame. Optionally, the road surface clustering road section further includes a road rectangular frame, and after the step of extracting and clustering the characteristics of the driving tracks based on the road surface attribute to obtain a corresponding road surface rectangular frame, the method further includes: And combining the road rectangular frame with a pr