CN113096395B - Road traffic safety evaluation system based on positioning and artificial intelligence recognition - Google Patents
Road traffic safety evaluation system based on positioning and artificial intelligence recognition Download PDFInfo
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- CN113096395B CN113096395B CN202110344218.9A CN202110344218A CN113096395B CN 113096395 B CN113096395 B CN 113096395B CN 202110344218 A CN202110344218 A CN 202110344218A CN 113096395 B CN113096395 B CN 113096395B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/588—Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors
Abstract
The invention discloses a road traffic safety evaluation system based on positioning and artificial intelligence recognition, which comprises the following steps: the system comprises a road data acquisition module, a parameter extraction module, an AI identification positioning module, a laser accurate positioning module, an AI classification matching module and an analysis and verification module; the road information acquisition module, the parameter extraction module, the AI identification and positioning module and the laser accurate positioning module are used for extracting and accurately positioning elements in the road information, and the AI classification matching module and the analysis and verification module are used for classifying and verifying the elements. The invention has the characteristics of high checking accuracy and high efficiency.
Description
Technical Field
The invention relates to the field of traffic safety evaluation, in particular to a road traffic safety evaluation system based on positioning and artificial intelligence recognition.
Background
With the advance of the infrastructure of China, the number of newly built and reconstructed roads is continuously increased every year, and the traffic safety evaluation is extremely important to the evaluation and the safety of road construction.
In the past, road transportation facility information was often collected by manual detection methods. This method is inefficient, labor intensive, and has a limited amount of information collected. The evaluation and the verification of the road are all carried out manually, the efficiency is low, the time consumption is long, the vehicle-mounted radar and the camera are used for collecting the road traffic facility information, the information is automatically verified with the current standard after being stored, the accuracy is high, and the efficiency is greatly improved.
Disclosure of Invention
The invention aims to provide a road traffic safety evaluation system based on positioning and artificial intelligence identification, and aims to solve the problems of low efficiency and incomplete information acquisition of traditional artificial detection and acquisition of road traffic facility information.
In order to solve the technical problem, the invention provides a technical scheme that: the utility model provides a road traffic safety evaluation system based on location and artificial intelligence discernment, this system sets up on the vehicle for carry out the security aassessment to the road after the construction, this system includes:
the road data acquisition module is used for measuring the position and the posture of the vehicle according to the GPS and inertial navigation and acquiring a road image and corresponding radar point cloud;
the parameter extraction module is used for performing point cloud splicing on each frame of radar point cloud according to the position and the posture of the vehicle, extracting the road line shape in the acquired road image and measuring the road line shape;
the AI identification positioning module is used for carrying out AI identification and element extraction on the collected road image and carrying out primary positioning based on graph measurement on elements in the road image; wherein the elements comprise traffic identification and traffic facilities;
the laser accurate positioning module extracts radar point clouds corresponding to the elements based on the laser point cloud characteristics according to the preliminary positioning information of the elements, and accurately positions the elements;
the AI classification matching module is used for carrying out AI identification and classification on the elements, matching the elements with the road pile numbers and storing a GIS database;
the analysis and check module is used for comparing and analyzing the road alignment and the elements stored in the GIS database with the design specifications and standards by combining the radar point cloud data through image recognition and semantic recognition, and listing the road sections and the elements which do not meet the requirements; the specific checking process of the analysis checking module comprises the following steps:
analyzing the road alignment based on the sight distance requirement in the standard requirement, and listing the sections with poor sight distance;
extracting breadth size, set clearance height, road side width, character height and color of the marks in the elements based on the setting requirements for the marks in the standard requirements, and listing the marks which do not meet the requirements;
based on the requirements for the marked lines in the standard requirements, the forms and positions of the marked lines in the elements are compared with the standard, and the positions of the marked lines which do not meet the requirements are listed;
and quantitatively analyzing the form, height and lateral clearance of the guardrails in the elements based on the setting requirements of the guardrails in the specification requirements, and listing the unqualified guardrails.
According to the scheme, the road data acquisition module comprises a forward laser radar and a camera, the position and pose of the forward laser radar and the camera are firstly calibrated in the acquisition process, so that the camera pixels correspond to radar point clouds, then the position and the attitude of the vehicle are measured according to a GPS and inertial navigation, and one frame of road image and one frame of radar point cloud are acquired every 10-20 m of driving.
According to the scheme, the AI identification and positioning module specifically adopts a method based on visual priority to perform AI identification and element extraction on each acquired road image, and performs primary positioning based on graphic measurement on elements in the road image.
A road traffic safety evaluation method comprises the following steps:
s1, firstly, carrying out pose calibration on the forward laser radar and the camera to enable the camera pixels to correspond to radar point clouds, then carrying out vehicle position and attitude measurement according to GPS and inertial navigation, and collecting one frame of image and radar point clouds every 10-20 m when the vehicle runs;
s2, performing point cloud splicing on each frame of radar point cloud according to the position and the posture of the vehicle, extracting the road line shape in the acquired road image and measuring the road line shape;
s3, carrying out AI identification and element extraction on the collected road image, and carrying out primary positioning on elements in the road image based on graph measurement;
s4, AI identification and classification are carried out on the elements, and the elements are matched with the road stake numbers and stored in a GIS database;
s5, comparing and analyzing the road alignment and the elements stored in the GIS database with the design specifications and standards through image recognition and semantic recognition and by combining radar point cloud data, and listing the road sections and the elements which do not meet the requirements; the method specifically comprises the following steps:
analyzing the road alignment based on the sight distance requirement in the standard requirement, and listing the sections with poor sight distance;
extracting breadth size, set clearance height, road side width, character height and color of the marks in the elements based on the setting requirements for the marks in the standard requirements, and listing the marks which do not meet the requirements;
based on the requirements for the marked lines in the standard requirements, the forms and positions of the marked lines in the elements are compared with the standard, and the positions of the marked lines which do not meet the requirements are listed;
and quantitatively analyzing the form, height and lateral clearance of the guardrails in the elements based on the setting requirements of the guardrails in the specification requirements, and listing the unqualified guardrails.
The invention has the beneficial effects that: the road information processing system is provided with a road data acquisition module, a parameter extraction module, an AI recognition positioning module and a laser accurate positioning module, so that the accurate extraction and positioning of elements in the road information can be realized, and the intelligent classification and the normative verification of the elements are realized through an AI classification matching module and an analysis checking module; the method has the characteristics of high checking accuracy and high efficiency.
Drawings
FIG. 1 is a block diagram of a module framework according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings of the embodiments of the present disclosure. It is to be understood that the described embodiments are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the disclosure without any inventive step, are within the scope of protection of the disclosure.
Referring to fig. 1, a road traffic safety evaluation system based on location and artificial intelligence discernment, includes road parameter module and safety evaluation module, and the road parameter module includes road data acquisition module, parameter extraction module, AI discernment orientation module and the accurate orientation module of laser, and the safety evaluation module includes AI categorised matching module and analysis and verification module, wherein:
the road data acquisition module is used for measuring the position and the posture of the vehicle according to the GPS and inertial navigation and acquiring a road image and corresponding radar point cloud;
the parameter extraction module is used for performing point cloud splicing on each frame of radar point cloud according to the position and the posture of the vehicle, extracting the road line shape in the acquired road image and measuring the road line shape;
the AI identification positioning module is used for carrying out AI identification and element extraction on the collected road image and carrying out primary positioning based on graph measurement on elements in the road image; wherein the elements comprise traffic identification and traffic facilities;
the laser accurate positioning module extracts radar point clouds corresponding to the elements based on the laser point cloud characteristics according to the preliminary positioning information of the elements, and accurately positions the elements;
the AI classification matching module is used for carrying out AI identification and classification on the elements, matching the elements with the road pile numbers and storing a GIS database;
the analysis and check module is used for comparing and analyzing the road alignment and the elements stored in the GIS database with the design specifications and standards by combining the radar point cloud data through image recognition and semantic recognition, and listing the road sections and the elements which do not meet the requirements; the specific checking process of the analysis checking module comprises the following steps:
analyzing the road alignment based on the sight distance requirement in the standard requirement, and listing the sections with poor sight distance;
extracting breadth size, set clearance height, road side width, character height and color of the marks in the elements based on the setting requirements for the marks in the standard requirements, and listing the marks which do not meet the requirements;
based on the requirements for the marked lines in the standard requirements, the forms and positions of the marked lines in the elements are compared with the standard, and the positions of the marked lines which do not meet the requirements are listed;
based on the setting requirement of the guardrails in the specification requirement, carrying out quantitative analysis on the form, height and lateral clearance of the guardrails in the elements, and listing the unqualified guardrails;
wherein the specification request refers to the design Specification for urban road traffic facilities (GB 50688-2011).
Furthermore, the road data acquisition module comprises a forward laser radar and a camera, during the acquisition process, firstly, the position and pose of the forward laser radar and the camera are calibrated, so that the camera pixels correspond to radar point clouds, then, the position and the attitude of the vehicle are measured according to a GPS and inertial navigation, and one frame of road image and one frame of radar point cloud are acquired every 10-20 m of driving.
Further, the AI identification and positioning module specifically adopts a method based on visual preference to perform AI identification and element extraction on each acquired road image, and perform preliminary positioning based on graphic measurement on elements in the road image, wherein the road traffic facility type according to the AI identification can refer to road traffic safety facility basic information acquisition specification (GA/T1495-2018).
A road traffic safety evaluation method comprises the following steps:
s1, firstly, carrying out pose calibration on the forward laser radar and the camera to enable the camera pixels to correspond to radar point clouds, then carrying out vehicle position and attitude measurement according to GPS and inertial navigation, and collecting one frame of image and radar point clouds every 10-20 m when the vehicle runs;
s2, performing point cloud splicing on each frame of radar point cloud according to the position and the posture of the vehicle, extracting the road line shape in the acquired road image and measuring the road line shape;
s3, carrying out AI identification and element extraction on the collected road image, and carrying out primary positioning on elements in the road image based on graph measurement;
s4, AI identification and classification are carried out on the elements, and the elements are matched with the road stake numbers and stored in a GIS database;
s5, comparing and analyzing the road alignment and the elements stored in the GIS database with the design specifications and standards through image recognition and semantic recognition and by combining radar point cloud data, and listing the road sections and the elements which do not meet the requirements; the method specifically comprises the following steps:
analyzing the road alignment based on the sight distance requirement in the standard requirement, and listing the sections with poor sight distance;
extracting breadth size, set clearance height, road side width, character height and color of the marks in the elements based on the setting requirements for the marks in the standard requirements, and listing the marks which do not meet the requirements;
based on the requirements for the marked lines in the standard requirements, the forms and positions of the marked lines in the elements are compared with the standard, and the positions of the marked lines which do not meet the requirements are listed;
and quantitatively analyzing the form, height and lateral clearance of the guardrails in the elements based on the setting requirements of the guardrails in the specification requirements, and listing the unqualified guardrails.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (4)
1. The utility model provides a road traffic safety evaluation system based on location and artificial intelligence discernment which characterized in that: the system is arranged on a vehicle and used for carrying out safety assessment on a constructed road, and comprises:
the road data acquisition module is used for measuring the position and the posture of the vehicle according to the GPS and inertial navigation and acquiring a road image and corresponding radar point cloud;
the parameter extraction module is used for performing point cloud splicing on each frame of radar point cloud according to the position and the posture of the vehicle, extracting the road line shape in the acquired road image and measuring the road line shape;
the AI identification positioning module is used for carrying out AI identification and element extraction on the collected road image and carrying out primary positioning based on graph measurement on elements in the road image; wherein the elements comprise traffic identification and traffic facilities;
the laser accurate positioning module extracts radar point clouds corresponding to the elements based on the laser point cloud characteristics according to the preliminary positioning information of the elements, and accurately positions the elements;
the AI classification matching module is used for carrying out AI identification and classification on the elements, matching the elements with the road pile numbers and storing a GIS database;
the analysis and check module is used for comparing and analyzing the road alignment and the elements stored in the GIS database with the design specifications and standards by combining the radar point cloud data through image recognition and semantic recognition, and listing the road sections and the elements which do not meet the requirements; the specific checking process of the analysis checking module comprises the following steps:
analyzing the road alignment based on the sight distance requirement in the standard requirement, and listing the sections with poor sight distance;
extracting breadth size, set clearance height, road side width, character height and color of the marks in the elements based on the setting requirements for the marks in the standard requirements, and listing the marks which do not meet the requirements;
based on the requirements for the marked lines in the standard requirements, the forms and positions of the marked lines in the elements are compared with the standard, and the positions of the marked lines which do not meet the requirements are listed;
and quantitatively analyzing the form, height and lateral clearance of the guardrails in the elements based on the setting requirements of the guardrails in the specification requirements, and listing the unqualified guardrails.
2. The road traffic safety evaluation system based on positioning and artificial intelligence recognition of claim 1, characterized in that: the road data acquisition module comprises a forward laser radar and a camera, during acquisition, the forward laser radar and the camera are subjected to pose calibration at first, so that camera pixels correspond to radar point clouds, then vehicle position and attitude measurement is carried out according to GPS and inertial navigation, and one frame of road image and one frame of radar point cloud are acquired every 10-20 m of running.
3. The road traffic safety evaluation system based on positioning and artificial intelligence recognition of claim 1, characterized in that: the AI identification positioning module specifically adopts a method based on visual priority to perform AI identification and element extraction on each acquired road image, and performs primary positioning based on graphic measurement on elements in the road image.
4. The road traffic safety evaluation method implemented by the road traffic safety evaluation system based on positioning and artificial intelligence recognition according to any one of claims 1 to 3, characterized in that: the method comprises the following steps:
s1, firstly, carrying out pose calibration on the forward laser radar and the camera to enable the camera pixels to correspond to radar point clouds, then carrying out vehicle position and attitude measurement according to GPS and inertial navigation, and collecting one frame of image and radar point clouds every 10-20 m when the vehicle runs;
s2, performing point cloud splicing on each frame of radar point cloud according to the position and the posture of the vehicle, extracting the road line shape in the acquired road image and measuring the road line shape;
s3, carrying out AI identification and element extraction on the collected road image, and carrying out primary positioning on elements in the road image based on graph measurement;
s4, AI identification and classification are carried out on the elements, and the elements are matched with the road stake numbers and stored in a GIS database;
s5, comparing and analyzing the road alignment and the elements stored in the GIS database with the design specifications and standards through image recognition and semantic recognition and by combining radar point cloud data, and listing the road sections and the elements which do not meet the requirements; the method specifically comprises the following steps:
analyzing the road alignment based on the sight distance requirement in the standard requirement, and listing the sections with poor sight distance;
extracting breadth size, set clearance height, road side width, character height and color of the marks in the elements based on the setting requirements for the marks in the standard requirements, and listing the marks which do not meet the requirements;
based on the requirements for the marked lines in the standard requirements, the forms and positions of the marked lines in the elements are compared with the standard, and the positions of the marked lines which do not meet the requirements are listed;
and quantitatively analyzing the form, height and lateral clearance of the guardrails in the elements based on the setting requirements of the guardrails in the specification requirements, and listing the unqualified guardrails.
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