A Dual-Radar Measurement Method for Vehicle Outer Contour Information

Abstract

The present invention provides a dual-radar measurement method for vehicle outer contour information, including: radar calibration, calibrating the length-measuring lidar and the width-and-height-measuring lidar, and initializing the parameters of the length-measuring lidar and the width-and-height-measuring lidar; performing length-measuring target detection and width-and-height-measuring target detection, and for vehicles meeting the requirements, calculating the current length, height, and width of the vehicle; continuously tracking the target, recording the length, height, and width of the vehicles meeting the requirements, and through continuous multi-frame tracking, calculating the final length, height, and width of the vehicle, which are the contour data of the vehicle; the dual-radar measurement method for vehicle outer contour information provided by the present invention can measure the vehicle outer contour with only two lidars; by filtering through background data, the data validity is greatly improved; and by using multi-frame data for target tracking, the data validity is further ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of radar detection, and particularly relates to a dual-radar measurement method for vehicle outer contour information. Background Art

[0002] In the prior art, the dynamic overloading and over-limit detection devices at each toll station can only detect the axle load mass, and cannot identify some geometric parameters of the vehicle, such as the size of the vehicle. These geometric parameters are of great significance for traffic safety and effective control of over-limit operation. Some toll stations directly use a tape measure to perform contact measurement on the vehicle. Such a measurement method has low measurement accuracy, poor measurement efficiency, and increases the workload and labor time of the operator. In some prior art studies on using lidar to detect dimensions, the data frame transmission situation in the actual detection environment, as well as the problems of target detection filtering and target tracking using data frames, are not considered.

[0003] For example, the Chinese invention patent with the publication number CN102679889B discloses a "measurement method and device for vehicle size". Among them, the measurement method for vehicle size includes: scanning a plane perpendicular to the vehicle driving direction through a lidar to measure the width and height of the vehicle; and scanning a plane parallel to the vehicle driving direction and perpendicular to the horizontal plane through a lidar to measure the length of the vehicle.

[0004] Chinese invention patent CN108592801A;

[0005] Chinese invention patent CN104655249A;

[0006] Chinese invention patent CN110728747A;

[0007] It can be seen that the prior art mainly measures the width and height of the vehicle by scanning a plane perpendicular to the vehicle driving direction through a lidar, and measures the length of the vehicle by scanning a plane parallel to the vehicle driving direction and perpendicular to the horizontal plane through a lidar. Its measurement model lacks support for data frames and cannot achieve target detection and tracking through data frames. Summary of the Invention

[0008] The purpose of the present invention is to provide a dual-radar measurement method for vehicle outer contour information, aiming to solve the problem that the target cannot be tracked using frame difference data when two lidars are used to measure the vehicle outer contour in the prior art.

[0009] To achieve the above technical purpose and reach the above technical effect, the present invention is realized through the following technical solutions:

[0010] The present invention provides a dual-radar measurement method for vehicle outer contour information, including the following steps:

[0011] Step S1: Radar calibration. Calibrate the length-measuring lidar and the width-height-measuring lidar, and initialize the height H1 above the ground, angular resolution ω1, radar point count P1, sampling period T1, and the angle α between the first scan line and the ground of the length-measuring lidar, as well as the height H2 above the ground, angular resolution ω2, radar point count P2, sampling period T2, and the angle β between the first scan line and the ground of the width-height-measuring lidar; The position of the length-measuring lidar relative to the ground is used as the first coordinate zero point, and the position of the width-height-measuring lidar relative to the ground is used as the second coordinate zero point; Record the initial length and height data {D 1(0)} of the length-measuring lidar, and the initial width and height data {W 1(0)} of the width-height-measuring lidar;

[0012] Step S2: Detection of length-measuring target. Use the initial length and height data {D 1(0)} as the background data for length and height. Obtain one frame of real-time length and height detection data {D 1(0+a*T1)} at each sampling period T1; Compare the real-time length and height detection data with the background data for length and height, and calculate the change rate μ of the length-measuring lidar according to the following formula 11 :

[0013] μ 11 =(D 1(0+a*T1) –D 1(0) ) / D 1(0) ;

[0014] In the formula, D 1(0+a*T1) is the radar measurement value of the length-measuring lidar at the a-th frame of data (the a-th sampling period); D 1(0) is the initial length and height data of the length-measuring lidar;

[0015] When the length change rate μ 11 of the length-measuring lidar at the a-th frame of data is less than 2%, update the background data for length and height, and use the current real-time length and height detection data {D 1(0+a*T1)} as the new background data for length and height; When the length change rate μ 11 of the length-measuring lidar at the i-th frame of data is greater than or equal to 2%, it is determined that a vehicle has entered the detection range, and calculate the first abscissa group of the vehicle measured by the length-measuring lidar relative to the first coordinate zero point according to the following formula:

[0016] X 1i =(D 1(0+i*T1) -D 1(0+a*T1) )*cos(ω1*P1+α);

[0017] In the formula, X 1i is the first abscissa of the vehicle measured by the length-measuring lidar relative to the first coordinate zero point; D 1(0+i*T1) is the radar measurement value of the vehicle measured by the length-measuring lidar at the i-th frame of data (the i-th sampling period); D1(0+a*T1) The length and height background data of the vehicle measured by the length-measuring lidar;

[0018] The length L of the vehicle at the i-th frame of data i Is the difference between the maximum value and the minimum value in the first abscissa group X 1i :

[0019] L i = max{X 11 , X 12 , X 13 , …, X 1i}-min{X 11 , X 12 , X 13 , …, X 1i};

[0020] Calculate the first ordinate group of the vehicle measured by the length-measuring lidar relative to the first coordinate zero point according to the following formula:

[0021] Y 1i = H1 - (D 1(0+i*T1) - D 1(0+a*T1) ) * sin(ω1 * P1 + α),

[0022] In the formula, Y 1i Is the first ordinate of the vehicle measured by the length-measuring lidar relative to the first coordinate zero point; D 1(0+i*T1) Is the radar measurement value of the target measured by the length-measuring lidar at the i-th frame of data; D 1(0+a*T1) Is the length and height background data of the vehicle measured by the length-measuring lidar;

[0023] The first height M of the vehicle at the i-th frame of data i Is the maximum value in the first ordinate group Y 1i :

[0024] M i = max{Y 11 , Y 12 , Y 13 , …, Y 1i};

[0025] Step S3: Width and height target detection. Use the initial width and height data {W 1(0)} as the width and height background data, and obtain a frame of real-time width and height detection data {W 1(0+b*T2)} in each sampling period T2; Compare the real-time width and height detection data with the width and height background data, and calculate the change rate μ of the width and height lidar according to the following formula 22 :

[0026] μ 22 = (W 1(0+b*T2) – W 1(0)) / W 1(0) ;

[0027] wherein, W 1(0+b*T2) is the radar measurement value of the width and height measuring lidar at the b-th frame data (the b-th sampling period); W 1(0) is the initial width and height data of the width and height measuring lidar;

[0028] When the width change rate μ 22 of the width and height measuring lidar at the b-th frame data is less than 2%, update the width and height background data, and use the current real-time width and height detection data {W 1(0+b*T2)} as the new width and height background data; when the change rate μ 22 of the width and height measuring lidar at the j-th frame data is greater than or equal to 2%, it is determined that a vehicle has entered the detection range, and the second abscissa group of the vehicle measured by the width and height measuring lidar relative to the second coordinate zero point is calculated according to the following formula:

[0029] X 2j =(W 1(0+j*T2) -W 1(0+b*T2) )*cos(ω2*P2 + β);

[0030] wherein, X 2j is the second abscissa of the vehicle measured by the width and height measuring lidar relative to the second coordinate zero point; W 1(0+j*T2) is the radar measurement value of the width and height measuring lidar at the j-th frame data; W 1(0+b*T2) is the width and height background data of the width and height measuring lidar;

[0031] The length W j of the vehicle at the j-th frame data is the difference between the maximum value and the minimum value in the second abscissa group X 2j :

[0032] W j = max{X 21 , X 22 , X 23 , …, X 2j}- min{X 21 , X 22 , X 23 , …, X 2j};

[0033] The second ordinate group of the vehicle measured by the width and height measuring lidar relative to the second coordinate zero point is calculated according to the following formula:

[0034] Y 2j = H2 - (W 1(0+j*T2) -W 1(0+b*T2) )*sin(ω2*P2 + β);

[0035] wherein, Y 2jis the second vertical coordinate of the vehicle measured by the length-measuring lidar relative to the second coordinate zero point; W 1(0+j*T2) is the radar measurement value of the length-measuring lidar at the j-th frame of data; W 1(0+b*T2) Width and height background data of the width and height measuring lidar;

[0036] The second height K of the vehicle at the j-th frame of data j is the maximum value in the second vertical coordinate group Y 2j :

[0037] K j = max{Y 21 , Y 22 , Y 23 , …, Y 2j};

[0038] Step S4: Width and height target tracking. Set the width threshold δ, and judge the width W of the vehicle at the j-th frame of data j ; If the width W of the vehicle at the j-th frame of data j is less than the width threshold δ, discard the target and return to step S2; If the width W of the vehicle at the j-th frame of data j is greater than or equal to the width threshold ε, start tracking from the j-th frame of data. When the change rate μ of the width and height measuring lidar 22 is continuously greater than or equal to 2% for r consecutive frames of data, continuously record r frames of data until the change rate μ of the width and height measuring lidar 22 is less than 2%, indicating that the vehicle has left, the recording ends, continuously record r frames of data, the vehicle width W = max{W j , W j+1 , …, W j+r}, the vehicle second height K = max{K j , K j+1 , …, K j+r}; The vehicle length L = max{L i , L i+1 , …, L i+r}, the vehicle first height M = max{M i , M i+1 , …, M i+r};

[0039] Step S5: Vehicle information matching. Set the matching threshold ζ. If the difference |M - K| between the vehicle first height M and the vehicle second height K is greater than ζ, the matching fails and return to step S2; If the difference |M - K| between the vehicle first height M and the vehicle second height K is less than or equal to ζ, the matching is successful, the vehicle second height K is used as the vehicle final height, the vehicle length L is used as the vehicle final length, and the vehicle width W is used as the vehicle final width, and the process ends.

[0040] Advantages of the present invention:

[0041] The dual-radar measurement method for vehicle outline information provided by the present invention uses two lidar sensors to measure the vehicle outline; filtering through background data greatly improves the data validity; and using multi-frame data for target tracking further ensures the data validity. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic structural diagram of the arrangement of the length-measuring lidar and the width-and-height-measuring lidar according to the present invention;

[0043] Figure 2 It is a schematic diagram of the lidar calibration structure of the length-measuring lidar according to the present invention;

[0044] Figure 3 It is a schematic diagram of the first coordinate according to the present invention;

[0045] Figure 4 It is a schematic diagram of the second coordinate according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below through specific embodiments with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0047] In an embodiment of the present invention, a dual-radar measurement method for vehicle outline information is provided. As Figure 1 shown, a length-measuring lidar 200 and a width-and-height-measuring lidar 300 are arranged on a gantry 100. The length-measuring lidar 200 is used to measure the length of a vehicle 400 along the driving direction of the vehicle 400, and the width-and-height-measuring lidar 300 is used to measure the width and height of the vehicle 400 in a direction perpendicular to the driving direction of the vehicle 400; specifically, the method includes the following steps:

[0048] Step S1: Lidar calibration. Calibrate the length-measuring lidar 200 and the width-and-height-measuring lidar 300, and initialize the ground clearance H1, angular resolution ω1, radar point count P1, sampling period T1 of the length-measuring lidar 200, and the angle α between the first scan line and the ground (as Figure 2 , Figure 3 shown), the ground clearance H2, angular resolution ω2, radar point count P2, sampling period T2 of the width-and-height-measuring lidar 300, and the angle β between the first scan line and the ground (as Figure 4 shown); the position of the length-measuring lidar 200 relative to the ground is used as the zero point of the first coordinate, and the position of the width-and-height-measuring lidar 300 relative to the ground is used as the zero point of the second coordinate; record the initial length and height data {D 1(0)}, and the width and height initial data {W of the width and height measurement lidar 300 1(0)};

[0049] Step S2: Length measurement target detection. Use the length and height initial data {D 1(0)} as the length and height background data, and obtain one frame of real-time length and height detection data {D 1(0+a*T1)} in each sampling period T1; Compare the real-time length and height detection data with the length and height background data, and calculate the change rate μ of the length measurement lidar according to the following formula 11 :

[0050] μ 11 =(D 1(0+a*T1) –D 1(0) ) / D 1(0) ;

[0051] In the formula, D 1(0+a*T1) is the radar measurement value of the length measurement lidar at the a-th frame of data (the a-th sampling period); D 1(0) is the length and height initial data of the length measurement lidar;

[0052] When the length change rate μ 11 of the length measurement lidar at the a-th frame of data is less than 2%, update the length and height background data, and use the current real-time length and height detection data {D 1(0+a*T1)} as the new length and height background data; When the length change rate μ 11 of the length measurement lidar at the i-th frame of data is greater than or equal to 2%, it is determined that a vehicle has entered the detection range, and calculate the first abscissa group of the vehicle measured by the length measurement lidar relative to the first coordinate zero point according to the following formula:

[0053] X 1i =(D 1(0+i*T1) -D 1(0+a*T1) )*cos(ω1*P1+α);

[0054] In the formula, X 1i is the first abscissa of the vehicle measured by the length measurement lidar relative to the first coordinate zero point; D 1(0+i*T1) is the radar measurement value of the vehicle measured by the length measurement lidar at the i-th frame of data (the i-th sampling period); D 1(0+a*T1) is the length and height background data of the vehicle measured by the length measurement lidar;

[0055] The length L i of the vehicle at the i-th frame of data 1i is the difference between the maximum value and the minimum value in the first abscissa group X

[0056] L i =max{X 11 ,X 12 ,X 13 ,…,X1i}-min{X 11 ,X 12 ,X 13 ,…,X 1i};

[0057] Calculate the first ordinate group of the vehicle measured by the length-measuring lidar relative to the first coordinate zero point according to the following formula:

[0058] Y 1i = H1 - (D 1(0+i*T1) - D 1(0+a*T1) ) * sin(ω1 * P1 + α),

[0059] In the formula, Y 1i is the first ordinate of the vehicle measured by the length-measuring lidar relative to the first coordinate zero point; D 1(0+i*T1) is the radar measurement value of the target measured by the length-measuring lidar at the i-th frame of data; D 1(0+a*T1) is the length and height background data of the vehicle measured by the length-measuring lidar;

[0060] The first height M of the vehicle at the i-th frame of data i is the maximum value in the first ordinate group Y 1i :

[0061] M i = max{Y 11 , Y 12 , Y 13 , …, Y 1i};

[0062] Step S3: Width and height target detection. Take the width and height initial data {W 1(0)} as the width and height background data, and obtain one frame of real-time width and height detection data {W 1(0+b*T2)} at each sampling period T2; Compare the real-time width and height detection data with the width and height background data, and calculate the change rate μ 22 of the width and height lidar according to the following formula:

[0063] μ 22 = (W 1(0+b*T2) – W 1(0) ) / W 1(0) ;

[0064] In the formula, W 1(0+b*T2) is the radar measurement value of the width and height lidar at the b-th frame of data (the b-th sampling period); W 1(0) is the width and height initial data of the width and height lidar;

[0065] When the width change rate μ 22 of the width and height lidar at the b-th frame of data is less than 2%, update the width and height background data, and take the current real-time width and height detection data {W1(0+b*T2)} As the new width and height background data; when the change rate μ of the width and height measurement lidar in the j-th frame of data 22 is greater than or equal to 2%, it is determined that a vehicle has entered the detection range, and the second abscissa group of the vehicle measured by the width and height measurement lidar relative to the second coordinate zero point is calculated according to the following formula:

[0066] X 2j = (W 1(0+j*T2) - W 1(0+b*T2) ) * cos(ω2 * P2 + β);

[0067] In the formula, X 2j is the second abscissa of the vehicle measured by the width and height measurement lidar relative to the second coordinate zero point; W 1(0+j*T2) is the radar measurement value of the width and height measurement lidar in the j-th frame of data; W 1(0+b*T2) the width and height background data of the width and height measurement lidar;

[0068] The length W of the vehicle at the j-th frame of data j is the difference between the maximum value and the minimum value in the second abscissa group X 2j :

[0069] W j = max{X 21 , X 22 , X 23 , …, X 2j}- min{X 21 , X 22 , X 23 , …, X 2j};

[0070] The second ordinate group of the vehicle measured by the width and height measurement lidar relative to the second coordinate zero point is calculated according to the following formula:

[0071] Y 2j = H2 - (W 1(0+j*T2) - W 1(0+b*T2) ) * sin(ω2 * P2 + β);

[0072] In the formula, Y 2j is the second ordinate of the vehicle measured by the length measurement lidar relative to the second coordinate zero point; W 1(0+j*T2) is the radar measurement value of the length measurement lidar in the j-th frame of data; W 1(0+b*T2) the width and height background data of the width and height measurement lidar;

[0073] The second height K of the vehicle at the j-th frame of data j is the maximum value in the second ordinate group Y 2j :

[0074] K j = max{Y21 , Y 22 , Y 23 , …, Y 2j};

[0075] Step S4: Width and height target tracking. Set the width threshold δ, and judge the width W of the vehicle at the j - th frame of data j . If the width W of the vehicle at the j - th frame of data j is less than the width threshold δ, then discard the target and return to Step S2; if the width W of the vehicle at the j - th frame of data j is greater than or equal to the width threshold ε, then start tracking from the j - th frame of data. When the change rate μ 22 of the width and height lidar is continuously greater than or equal to 2% for r consecutive frames of data, record r consecutive frames of data until the change rate μ 22 of the width and height lidar is less than 2%, indicating that the vehicle has left, and the recording ends. Record r consecutive frames of data, and the vehicle width W = max{W j , W j+1 , …, W j+r}, the second height K of the vehicle = max{K j , K j+1 , …, K j+r}; the vehicle length L = max{L i , L i+1 , …, L i+r}, the first height M of the vehicle = max{M i , M i+1 , …, M i+r};

[0076] Step S5: Vehicle information matching. Set the matching threshold ζ. If the difference |M - K| between the first height M and the second height K of the vehicle is greater than ζ, then the matching fails and return to Step S2; if the difference |M - K| between the first height M and the second height K of the vehicle is less than or equal to ζ, then the matching is successful. The second height K of the vehicle is used as the final height of the vehicle, the vehicle length L is used as the final length of the vehicle, and the vehicle width W is used as the final width of the vehicle, and the process ends.

[0077] References to "some embodiments", "an embodiment", or "embodiments" etc. in this specification refer to the specific features, structures, or properties described in connection with the embodiments being included in at least one embodiment. Thus, the appearances of the phrases "in some embodiments", "in an embodiment", or "in embodiments" etc. throughout the specification do not necessarily refer to the same embodiment. In addition, the specific features, structures, or properties may be combined in any suitable manner in one or more embodiments. Therefore, the specific features, structures, or properties shown or described in connection with one embodiment may be combined with the features, structures, or properties of one or more other embodiments wholly or partly without limitation, as long as the combination is not illogical or non - working. Additionally, the elements in the drawings of this application are only for illustrative purposes and are not drawn to scale.

[0078] Thus, several aspects of at least one embodiment of the present invention have been described, and it can be understood that various changes, modifications, and improvements can be easily made by those skilled in the art. Such changes, modifications, and improvements are intended to be within the spirit and scope of the present invention.

Claims

1. A dual-radar measurement method for vehicle outline information, characterized in that , it includes the following steps: Step S1: Radar calibration. Calibrate the length-measuring lidar and the width-height-measuring lidar, and initialize the height H1 from the ground, angular resolution ω1, radar point count P1, sampling period T1, and the angle α between the first scan line and the ground of the length-measuring lidar, as well as the height H2 from the ground, angular resolution ω2, radar point count P2, sampling period T2, and the angle β between the first scan line and the ground of the width-height-measuring lidar; The position of the length-measuring lidar relative to the ground is used as the first coordinate zero point, and the position of the width-height-measuring lidar relative to the ground is used as the second coordinate zero point; Record the initial length and height data {D 1(0)} of the length-measuring lidar, and the initial width and height data {W 1(0)} of the width-height-measuring lidar; Step S2: Length measurement target detection. Use the initial length and height data {D 1(0)} as the length and height background data, and obtain one frame of real-time length and height detection data {D 1(0+a*T1)} in each sampling period T1; compare the real-time length and height detection data with the length and height background data, and calculate the change rate μ of the length measurement lidar according to the following formula 11 : μ 11 = (D 1(0+a*T1) – D 1(0) ) / D 1(0) ; where D 1(0+a*T1) is the radar measurement value of the length-measuring lidar at the a-th frame of data; D 1(0) is the initial data of the height and length of the length-measuring lidar. When the length change rate μ of the length measurement lidar in the a-th frame of data 11 is less than 2%, update the height and length background data, and use the current real-time height and length detection data {D 1(0+a*T1)} as the new height and length background data; when the length change rate μ of the length measurement lidar in the i-th frame of data 11 is greater than or equal to 2%, it is determined that a vehicle has entered the detection range, and the first abscissa group of the vehicle measured by the length measurement lidar relative to the first coordinate zero point is calculated according to the following formula: X 1i = (D 1(0+i*T1) - D 1(0+a*T1) ) * cos(ω1 * P1 + α); where X 1i is the first abscissa of the vehicle measured by the length-measuring lidar relative to the first coordinate zero point; D 1(0+i*T1) is the radar measurement value of the vehicle measured by the length-measuring lidar at the i-th frame of data; D 1(0+a*T1) is the height and length background data of the vehicle measured by the length-measuring lidar; The length L of the vehicle at the i-th frame of data i is the difference between the maximum value and the minimum value in 1i the first abscissa group X: L i = max{X 11 , X 12 , X 13 , …, X 1i} - min{X 11 , X 12 , X 13 , …, X 1i}; Calculate the first ordinate group of the vehicle measured by the length-measuring lidar relative to the first coordinate zero point according to the following formula: Y 1i = H1 - (D 1(0+i*T1) - D 1(0+a*T1) ) * sin(ω1 * P1 + α), where Y 1i is the first vertical coordinate of the vehicle measured by the length-measuring lidar relative to the first coordinate zero point; D 1(0+i*T1) is the radar measurement value of the target measured by the length-measuring lidar at the i-th frame of data; D 1(0+a*T1) is the height and length background data of the vehicle measured by the length-measuring lidar; The first height M of the vehicle at the i-th frame of data i is the maximum value in the first ordinate group Y 1i as follows: M i = max{Y 11 , Y 12 , Y 13 , …, Y 1i}; Step S3: Width and height target detection. Use the initial width and height data {W 1(0)} as the width and height background data, and obtain one frame of real-time width and height detection data {W 1(0+b*T2)} in each sampling period T2; compare the real-time width and height detection data with the width and height background data, and calculate the change rate μ 22 of the width and height lidar according to the following formula: μ 22 = (W 1(0+b*T2) – W 1(0) ) / W 1(0) ; Where, W 1(0+b*T2) is the radar measurement value of the width and height measurement lidar at the b-th frame of data; W 1(0) is the initial width and height data of the width and height measurement lidar; When the width change rate μ of the width and height measurement lidar in the b-th frame of data 22 is less than 2%, update the width and height background data, and use the current real-time width and height detection data {W 1(0+b*T2)} as the new width and height background data; When the change rate μ of the width and height measurement lidar for the j-th frame of data 22 is greater than or equal to 2%, it is determined that a vehicle has entered the detection range, and the second abscissa group of the vehicle measured by the width and height measurement lidar relative to the second coordinate zero point is calculated according to the following formula: X 2j = (W 1(0+j*T2) - W 1(0+b*T2) ) * cos(ω2 * P2 + β); where X 2j is the second abscissa of the vehicle measured by the width and height measuring lidar relative to the second coordinate zero point; W 1(0+j*T2) is the radar measurement value of the width and height measuring lidar at the j-th frame of data; W 1(0+b*T2) is the width and height background data of the width and height measuring lidar The length W of the vehicle at the j-th frame of data j is the second abscissa group X 2j The difference between the maximum value and the minimum value in: W j = max{X 21 , X 22 , X 23 , …, X 2j} - min{X 21 , X 22 , X 23 , …, X 2j}; Calculate the second ordinate group of the vehicle measured by the width-and-height-measuring lidar relative to the second coordinate zero point according to the following formula: Y 2j = H2 - (W 1(0+j*T2) - W 1(0+b*T2) ) * sin(ω2 * P2 + β); where Y 2j is the second ordinate of the vehicle measured by the length-measuring lidar relative to the second coordinate zero point; W 1(0+j*T2) is the radar measurement value of the length-measuring lidar at the j-th frame of data; W 1(0+b*T2) is the width-height background data of the width-height measuring lidar The second height K of the vehicle at the j-th frame of data j is the maximum value in the second ordinate set Y 2j as follows: K j = max{Y 21 , Y 22 , Y 23 , …, Y 2j}; Step S4: Width and height target tracking. Set a width threshold δ, and judge the width W of the vehicle at the j-th frame of data. j If the width W of the vehicle at the j-th frame of data j is less than the width threshold δ, then discard this target and return to step S2. The width W of the vehicle at the j-th frame data j If it is greater than or equal to the width threshold ε, start tracking from the j-th frame data, and the change rate μ of the width and height measuring lidar 22 When it is continuously greater than or equal to 2% for r consecutive frames of data, continuously record r frames of data until the change rate μ of the width and height measuring lidar 22 Is less than 2%, indicating that the vehicle has left and the recording ends. Continuously record r frames of data, and the vehicle width W = max{W j , W j+1 , …, W j+r}, the second height K of the vehicle = max{K j , K j+1 , …, K j+r}; the vehicle length L = max{L i , L i+1 , …, L i+r , the first height M of the vehicle = max{M i , M i+1 , …, M i+r}; Step S5: Vehicle information matching. Set the matching threshold ζ. If the difference |M - K| between the first height M of the vehicle and the second height K of the vehicle is greater than ζ, the matching fails, and return to step S2; if the difference |M - K| between the first height M of the vehicle and the second height K of the vehicle is less than or equal to ζ, the matching is successful. The second height K of the vehicle is used as the final height of the vehicle, the vehicle length L is used as the final length of the vehicle, and the vehicle width W is used as the final width of the vehicle, and the process ends.

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