Positioning precision detection method and device of Internet of Vehicles positioning system

Abstract

The invention discloses a positioning precision detection method and device of an Internet of Vehicles positioning system, and relates to the field of Internet of Vehicles, and the method comprises the steps: obtaining a road surface image covered by a vehicle when the vehicle passes through an area with an identification graph, and recognizing the identification graph right facing the center of the vehicle and the number of an identification grid according to the road surface image, calculating the position information of the identification grid directly facing the center of the vehicle; according to the position information of the identification grid directly facing the center of the vehicle and the vehicle position information positioned by the vehicle networking positioning system, calculating the positioning error of the current vehicle networking positioning system; and calculating the positioning precision of the Internet of Vehicles positioning system according to the measured positioning error values of multiple points of the Internet of Vehicles positioning system. The detection method is obtained through selected area identification grid image recognition calculation instead of ground base station positioning, positioning errors caused by influence of external factors such as signal shielding or extreme weather are reduced, and the accuracy of detection of the positioning precision of the detection vehicle networking positioning system is effectively improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle networking, and specifically relates to a method and device for detecting the positioning accuracy of a vehicle networking positioning system. Background Art

[0002] With the development of intelligent connected vehicles, their safety has gradually become a concern of society. In the operation and safety testing of intelligent connected vehicles, a vehicle networking positioning system is required to position the vehicle. Once the positioning is lost or a large deviation occurs, it will not only endanger the safety of the vehicle and personnel, but also affect the safety test results. Therefore, before the vehicle networking positioning system is put into use, it needs to be detected to determine whether its positioning accuracy meets the actual use requirements.

[0003] In the prior art, usually multiple ground base stations are used to perform fusion positioning on the vehicle to obtain the true position of the vehicle, and the true position of the vehicle is compared with the positioning position of the vehicle networking positioning system to detect the positioning accuracy of the vehicle networking.

[0004] However, in a complex environment, the signals of the ground base stations will be blocked by various obstacles and affected by weather factors, and there is a large deviation in the true position of the vehicle obtained by positioning, which seriously reduces the accuracy of detecting the positioning accuracy of the vehicle networking positioning system and does not meet the requirements of high real-time and high accuracy for the positioning of intelligent connected vehicles. Once the positioning is lost or a large deviation occurs, it will not only affect the detection results, but also may affect the safety of the vehicle and personnel. Summary of the Invention

[0005] The present application provides a method and device for detecting the positioning accuracy of a vehicle networking positioning system, which can improve the accuracy of detecting the positioning accuracy of the vehicle networking positioning system.

[0006] In a first aspect, an embodiment of the present application provides a method for detecting the positioning accuracy of a vehicle networking positioning system, the method including:

[0007] When the vehicle passes through an area with an identification map, obtain a road surface image covered by the vehicle, where the identification map is an identification image pre-drawn on the road surface, and the identification map is divided into multiple identification cells with the same area;

[0008] According to the road surface image, identify the identification map and the number of the identification cell directly opposite the vehicle center, and calculate the position information of the identification cell directly opposite the vehicle center;

[0009] According to the position information of the identification cell directly opposite the vehicle center and the vehicle position information located by the vehicle networking positioning system, calculate the positioning error of the vehicle networking positioning system at the current point;

[0010] Repeatedly measure the positioning error of the vehicle networking positioning system at multiple preset test points, and calculate the positioning accuracy of the vehicle networking positioning system according to the positioning error values measured multiple times.

[0011] Combined with the first aspect, in an implementation manner, according to the road surface image, identifying the identification map and the number of the identification grid directly opposite the vehicle center includes:

[0012] Performing image recognition on the road surface image to obtain the number of the identification map directly opposite the vehicle center;

[0013] Performing laser positioning on the identification grid directly opposite the vehicle center to obtain the number of the identification grid directly opposite the vehicle center.

[0014] Combined with the first aspect, in an implementation manner, calculating the position information of the identification grid directly opposite the vehicle center includes:

[0015] According to the number of the identification map directly opposite the vehicle center, obtaining the position information of the identification map directly opposite the vehicle center from the preset map data;

[0016] According to the number of the identification grid directly opposite the vehicle center, calculating the relative position information of the identification grid directly opposite the vehicle center in the identification map directly opposite the vehicle center;

[0017] According to the position information of the identification map directly opposite the vehicle center and the relative position information, calculating the position information of the identification grid directly opposite the vehicle center.

[0018] Combined with the first aspect, in an implementation manner, calculating the positioning error of the vehicle networking positioning system at the current point according to the position information of the identification grid directly opposite the vehicle center and the vehicle position information located by the vehicle networking positioning system includes:

[0019] According to the position information of the identification grid directly opposite the vehicle center, calculating the first coordinate of the identification grid directly opposite the vehicle center in the global coordinate system;

[0020] According to the vehicle position information located by the vehicle networking positioning system, calculating the second coordinate of the vehicle position located by the vehicle networking positioning system in the global coordinate system;

[0021] Calculating the Euclidean distance between the first coordinate and the second coordinate, and the Euclidean distance is the positioning error of the vehicle networking positioning system at the current point.

[0022] Combined with the first aspect, in an implementation manner, repeatedly measuring the positioning error of the vehicle networking positioning system at multiple preset test points includes:

[0023] For each preset test point, repeatedly measure the positioning error of the vehicle networking positioning system;

[0024] Statistically analyze the positioning errors of the vehicle networking positioning system obtained from multiple measurements to obtain the average positioning error at each test point.

[0025] Combined with the first aspect, in one implementation, calculate the positioning accuracy of the vehicle networking positioning system according to the positioning error values obtained from multiple measurements, including:

[0026] Calculate the probability distribution of the positioning error according to the positioning error values obtained from multiple measurements;

[0027] Calculate the circular error probable (CEP) of the vehicle networking positioning system according to the probability distribution of the positioning error, and the CEP is the positioning accuracy of the vehicle networking positioning system.

[0028] Combined with the first aspect, in one implementation, calculating the positioning accuracy of the vehicle networking positioning system further includes:

[0029] Obtain the measured speed of the vehicle through the vehicle networking positioning system;

[0030] Calculate the true speed of the vehicle according to the time when the vehicle passes through each identification grid and the Euclidean distance between the identification grids;

[0031] Calculate the speed measurement error of the vehicle networking positioning system according to the measured speed obtained by the vehicle networking positioning system and the true speed of the vehicle, where the speed measurement error is the absolute value of the difference between the measured speed of the vehicle and the true speed of the vehicle.

[0032] Combined with the first aspect, in one implementation, after calculating the positioning accuracy of the vehicle networking positioning system, it further includes:

[0033] If the positioning accuracy of the vehicle networking positioning system is lower than the preset positioning accuracy threshold, calibrate the vehicle networking positioning system according to the positioning errors of the vehicle networking positioning system measured at each preset test point.

[0034] In a second aspect, an embodiment of the present application provides a positioning accuracy detection device for a vehicle networking positioning system, and the device includes:

[0035] An image acquisition module, configured to acquire a road surface image covered by the vehicle when the vehicle passes through an area with an identification map;

[0036] An image recognition module, configured to identify the identification map and the number of the identification grid directly opposite the vehicle center according to the road surface image, and calculate the position information of the identification grid directly opposite the vehicle center;

[0037] A calculation module, configured to calculate the positioning error of the current vehicle networking positioning system according to the position information of the identification grid directly opposite the vehicle center and the vehicle position information located by the vehicle networking positioning system; and further configured to repeatedly measure the positioning error of the vehicle networking positioning system at multiple preset test points, and calculate the positioning accuracy of the vehicle networking positioning system according to the positioning errors of the vehicle networking positioning system obtained from multiple measurements.

[0038] Combined with the second aspect, in an implementation manner, the device further includes:

[0039] A calibration module, configured to calibrate the vehicle networking positioning system according to the positioning errors of the vehicle networking positioning system measured at each preset test point when the positioning accuracy of the vehicle networking positioning system is lower than a preset positioning accuracy threshold.

[0040] The beneficial effects brought by the technical solution provided by the embodiments of the present application include:

[0041] In the present application, when the vehicle passes through an area with an identification map, the road surface image covered by the vehicle is acquired, and then the identification map and the number of the identification map in the road surface image are recognized, and the position information of the identification grid directly opposite the vehicle center is calculated according to the number. Then, the position information of the identification grid directly opposite the vehicle center is compared with the vehicle position information located by the vehicle networking positioning system, and the positioning accuracy of the vehicle networking positioning system is calculated through the error between the two. Since the position information of the identification grid (the real position of the vehicle) is obtained by image recognition calculation rather than by ground base station positioning, the positioning error caused by signal shielding is reduced, and the accuracy of detecting the positioning accuracy of the vehicle networking positioning system is effectively improved. Description of the Drawings

[0042] Figure 1 It is a schematic flow chart of the method for detecting the positioning accuracy of the vehicle networking positioning system according to the embodiment of the present application;

[0043] Figure 2 It is a schematic structural diagram of the device for detecting the positioning accuracy of the vehicle networking positioning system according to the embodiment of the present application. Detailed Embodiments

[0044] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0045] To make the purpose, technical solution and advantages of the present application clearer, the embodiments of the present application will be further described in detail below with reference to the accompanying drawings.

[0046] In a first aspect, please refer to Figure 1 , Figure 1 which is a schematic flowchart of a method for detecting the positioning accuracy of a vehicle networking positioning system according to an embodiment of the present application. The method for detecting the positioning accuracy of the vehicle networking positioning system provided in this embodiment includes the following steps:

[0047] Step S1: When the vehicle passes through an area with an identification map, obtain a road surface image covered by the vehicle. Among them, the identification map is an identification image pre-drawn on the road surface, and the identification map is divided into multiple identification grids with the same area.

[0048] Step S2: According to the road surface image, identify the identification map and the number of the identification grid directly opposite the vehicle center, and calculate the position information of the identification grid directly opposite the vehicle center.

[0049] Step S3: According to the position information of the identification grid directly opposite the vehicle center and the vehicle position information located by the vehicle networking positioning system, calculate the positioning error of the vehicle networking positioning system at the current point.

[0050] Step S4: Repeatedly measure the positioning error of the vehicle networking positioning system at multiple preset test points, and calculate the positioning accuracy of the vehicle networking positioning system according to the positioning error values measured multiple times.

[0051] In this method, when the vehicle passes through an area with an identification map, a road surface image covered by the vehicle is obtained. Subsequently, the identification map and the number of the identification map in the road surface image are identified, and the position information of the identification grid directly opposite the vehicle center is calculated according to the number. Then, the position information of the identification grid directly opposite the vehicle center is compared with the vehicle position information located by the vehicle networking positioning system, and the positioning accuracy of the vehicle networking positioning system is calculated through the error between the two. Since the position information of the identification grid (the true position of the vehicle) is obtained through image recognition calculation rather than through ground base station positioning, the positioning error caused by signal shielding is reduced, and the accuracy of detecting the positioning accuracy of the vehicle networking positioning system is effectively improved.

[0052] In some embodiments, in the above step S1, the road surface image covered by the vehicle can be captured by an in-vehicle camera.

[0053] In some embodiments, in the above step S1, according to the specific layout plan of the vehicle networking positioning system in a test area such as a demonstration area or a test field, an identification map is drawn on the road surface at key points. Among them, the key points include signal junction areas and signal interference areas. Since the signal is unstable in the signal junction areas and signal interference areas, it is necessary to test the vehicle networking positioning system in these areas to determine whether the positioning accuracy of the vehicle networking positioning system in the entire test area is consistent and continuous.

[0054] In a specific embodiment, the identification map is a 1m * 1m rectangular frame, and each identification map is attached with an ID number. Each identification map is divided into 10,000 identification cells of 1cm * 1cm, and each identification cell is numbered. In each identification map, the number of the identification cell is expressed as (Xn, Yn), where the value range of n is from 0 to 99. The identification cells are arranged in a fixed order along a preset grid, that is, in different identification maps, the identification cells with the same number are in the same relative position. The position information of the selected position identification cell can be repeatedly compared and calibrated by surveying or high-precision positioning equipment under good environmental conditions, and can be regarded as the positioning true value.

[0055] In some embodiments, in the above step S2, according to the road surface image, identifying the identification map and the number of the identification cell directly opposite the vehicle center includes the following steps:

[0056] First, perform image recognition on the road surface image to obtain the number of the identification map directly opposite the vehicle center, and then perform laser positioning on the identification cell directly opposite the vehicle center to obtain the number of the identification cell directly opposite the vehicle center.

[0057] Specifically, an infrared laser can be used to perform laser positioning on the identification cell directly opposite the vehicle center.

[0058] In some embodiments, in the above step S2, calculating the position information of the identification cell directly opposite the vehicle center includes the following steps:

[0059] S21: According to the number of the identification map directly opposite the vehicle center, obtain the position information of the identification map directly opposite the vehicle center from the preset map data.

[0060] S22: According to the number of the identification cell directly opposite the vehicle center, calculate the relative position information of the identification cell directly opposite the vehicle center in the identification map directly opposite the vehicle center.

[0061] S23: According to the position information of the identification map directly opposite the vehicle center and the above relative position information, calculate the position information of the identification cell directly opposite the vehicle center.

[0062] It should be noted that in the above steps S22 and S23, by calculating the relative position information of the identification cell directly opposite the vehicle center in the identification map directly opposite the vehicle center, and then calculating the position information of the identification cell directly opposite the vehicle center according to the position information of the identification map directly opposite the vehicle center and the relative position information, this step is actually establishing a local-to-global position conversion. Through this step, the position information of the identification cell directly opposite the vehicle center can be obtained without pre-recording the position of each identification cell, greatly reducing the storage amount of map data and enhancing the response speed and accuracy of the positioning accuracy of the detection vehicle networking positioning system.

[0063] In some embodiments, in the above step S21, the position information of the identification diagram specifically refers to the coordinates (X10, Y10, Z10) of the lower left vertex of the lower left vertex identification cell of the identification diagram in the global coordinate system.

[0064] In some embodiments, in the above step S23, according to the position information of the identification diagram directly opposite the vehicle center and the above relative position information, calculate the position information of the identification cell directly opposite the vehicle center. The specific calculation formula is as follows:

[0065] X1N = X10 + Xn Formula (1)

[0066] Y1N = Y10 + Yn Formula (2)

[0067] Z1N = Z10 Formula (3)

[0068] In Formulas (1), (2), and (3), (X1N, Y1N, Z1N) are the coordinates of the identification cell directly opposite the vehicle center in the global coordinate system, (X10, Y10, Z10) are the coordinates of the lower left vertex of the lower left vertex identification cell of the identification diagram in the global coordinate system, Xn is the distance between the identification cell directly opposite the vehicle center and the lower left vertex of the lower left vertex identification cell of the identification diagram on the X-axis, Yn is the distance between the identification cell directly opposite the vehicle center and the lower left vertex of the lower left vertex identification cell of the identification diagram on the Y-axis, and the value ranges of Xn and Yn are from 0 to 99. If Xn is 23 and Yn is 59, it means that this identification cell is the 23rd identification cell on the X-axis and the 59th identification cell on the Y-axis counted from the lower left vertex of the identification diagram.

[0069] In Formula (3), it is assumed that all identification cells in the identification diagram are on the same plane parallel to the XY plane, that is, the Z-axis coordinates are the same, so Z1N = Z10.

[0070] In some embodiments, in the above step S3, according to the position information of the identification cell directly opposite the vehicle center and the vehicle position information located by the vehicle networking positioning system, calculate the positioning error of the vehicle networking positioning system at the current point, including the following steps:

[0071] S31: According to the position information of the identification cell directly opposite the vehicle center, calculate the first coordinate of the identification cell directly opposite the vehicle center in the global coordinate system.

[0072] S32: According to the vehicle position information located by the vehicle networking positioning system, calculate the second coordinate of the vehicle position located by the vehicle networking positioning system in the global coordinate system.

[0073] S33: Calculate the Euclidean distance between the first coordinate and the second coordinate, and this Euclidean distance is the positioning error of the vehicle networking positioning system at the current point.

[0074] In the above steps S31 - S33, by comparing the position (the first coordinate) of the identification grid directly opposite the vehicle center in the global coordinate system with the vehicle position (the second coordinate) provided by the vehicle networking positioning system, a quantitative value of the positioning error can be obtained intuitively. This direct comparison method helps to more accurately evaluate the performance of the vehicle networking positioning system.

[0075] In some embodiments, in the above step S33, the formula for calculating the Euclidean distance between the first coordinate and the second coordinate is as follows:

[0076]

[0077] In formula (4), (X1N, Y1N, Z1N) is the first coordinate, (XN, YN, ZN) is the second coordinate, and W1 is the Euclidean distance between the first coordinate and the second coordinate.

[0078] In some embodiments, in the above step S4, the positioning error of the vehicle networking positioning system is repeatedly measured at multiple preset test points, including the following steps:

[0079] First, for each preset test point, the positioning error of the vehicle networking positioning system is measured multiple times. Subsequently, statistical analysis is performed on the positioning errors of the vehicle networking positioning system obtained from multiple measurements to obtain the average positioning error of each test point.

[0080] By measuring multiple times, the random error that may be brought by a single measurement can be reduced, making the statistical results more stable and reliable. Multiple measurements can smooth out the error fluctuations caused by some accidental factors, thereby more accurately reflecting the true performance of the vehicle networking positioning system.

[0081] In some embodiments, in the above step S4, according to the positioning error values of the vehicle networking positioning system obtained from multiple measurements, the positioning accuracy of the vehicle networking positioning system is calculated, including the following steps:

[0082] S41: According to the positioning error values measured multiple times, calculate the probability distribution of the positioning error.

[0083] S42: According to the probability distribution of the positioning error, calculate the circular error probability of the vehicle networking positioning system. The calculated circular error probability is the positioning accuracy of the vehicle networking positioning system.

[0084] Circular Error Probable (CEP) represents the radius of the circle within which the positioning error is distributed at a certain probability (usually 50%). This can intuitively reflect the uncertainty of the positioning error, that is, the degree of dispersion of the vehicle positions located by the vehicle network positioning system around the true position with a certain probability. For different application scenarios, the requirements for the positioning accuracy of the vehicle network positioning system may vary. Using CEP can more precisely analyze the distribution of the positioning error, thus meeting the positioning accuracy requirements of specific application scenarios.

[0085] In some embodiments, in the above step S42, if the positioning error at more than 50% of a certain test point is less than 10 cm, the positioning accuracy of the vehicle network positioning system at this point is 10 cm CEP. By detecting all the marked map areas in the entire demonstration area or test field, the overall positioning accuracy of the vehicle network positioning system can be comprehensively evaluated. For example, if the minimum CEP is 10 cm and the maximum CEP is 1 m during the overall test process, it can be considered that the overall positioning accuracy of the vehicle network positioning system is not lower than 1 m CEP.

[0086] In some embodiments, in the above step S4, calculating the positioning accuracy of the vehicle networking positioning system further includes the following steps:

[0087] First, obtain the measured speed of the vehicle through the vehicle networking positioning system. Subsequently, calculate the true speed of the vehicle according to the time when the vehicle passes through each identification grid and the Euclidean distance between the identification grids. Finally, calculate the speed measurement error of the vehicle networking positioning system based on the measured speed obtained by the vehicle networking positioning system and the true speed of the vehicle, where the speed measurement error is the absolute value of the difference between the measured speed of the vehicle and the true speed of the vehicle.

[0088] By comparing the measured speed of the vehicle with the true speed, the speed measurement error can be accurately calculated, thereby precisely evaluating the performance of the vehicle networking positioning system in terms of speed measurement and meeting the requirements of dynamic testing.

[0089] In some embodiments, the formula for calculating the measured speed of the vehicle is as follows:

[0090]

[0091] In formula (5), V(N) is the measured speed of the vehicle, (X0, Y0, Z0) is the measurement coordinates of the positioning system of the previous identification grid passed by the vehicle, (XN, YN, ZN) is the measurement coordinates of the positioning system of the current identification grid passed by the vehicle, T0 is the time when the vehicle passes through the previous identification grid, and TN is the time when the vehicle passes through the current identification grid.

[0092] In some embodiments, the formula for calculating the true speed of the vehicle is as follows:

[0093]

[0094] In formula (6), V(1N) is the true speed of the vehicle, (X10, Y10, Z10) is the true coordinates of the previous identified grid passed by the vehicle, (X1N, Y1N, Z1N) is the true coordinates of the currently passed identified grid by the vehicle, T0 is the time when the vehicle passes the previous identified grid, and TN is the time when the vehicle passes the current identified grid.

[0095] In some embodiments, after calculating the positioning accuracy of the vehicle networking positioning system, it further includes:

[0096] If the positioning accuracy of the vehicle networking positioning system is lower than the preset positioning accuracy threshold, calibrate the vehicle networking positioning system according to the positioning errors of the vehicle networking positioning system measured at each preset test point.

[0097] Through calibration, the positioning errors of the vehicle networking positioning system at each test point can be corrected. The calibrated vehicle networking positioning system can more accurately reflect the actual position of the vehicle and improve the overall performance of the system.

[0098] In a second aspect, please refer to Figure 2 , Figure 2 which is a schematic structural diagram of the positioning accuracy detection device of the vehicle networking positioning system according to the embodiment of the present application. The positioning accuracy detection device of the vehicle networking positioning system provided in this embodiment includes an image acquisition module, an image recognition module, and a calculation module.

[0099] The image acquisition module is used to acquire the road surface image covered by the vehicle when the vehicle passes through the area with the identification map.

[0100] The image recognition module is used to identify the identification map and the number of the identified grid directly opposite the vehicle center according to the road surface image, and calculate the position information of the identified grid directly opposite the vehicle center.

[0101] The calculation module is used to calculate the positioning error of the vehicle networking positioning system at the current point according to the position information of the identified grid directly opposite the vehicle center and the vehicle position information located by the vehicle networking positioning system; it is also used to repeatedly measure the positioning error of the vehicle networking positioning system at multiple preset test points, and calculate the positioning accuracy of the vehicle networking positioning system according to the positioning error values obtained from multiple measurements.

[0102] This device obtains the road surface image covered by the vehicle through the image acquisition module, identifies the identification map and the numbers of the identification grids in the road surface image through the image recognition module, calculates the position information of the identification grid directly opposite to the vehicle center, calculates the positioning error of the vehicle networking positioning system through the calculation module, and calculates the positioning accuracy of the vehicle networking positioning system based on the positioning errors of the vehicle networking positioning system obtained through multiple measurements. Since the position information of the identification grid (the actual position of the vehicle) is obtained through image recognition calculation rather than through ground base station positioning, the positioning error caused by signal shielding is reduced, and the accuracy of detecting the positioning accuracy of the vehicle networking positioning system is effectively improved.

[0103] In some embodiments, the positioning accuracy detection device of the above vehicle networking positioning system further includes a calibration module.

[0104] The calibration module is used to calibrate the vehicle networking positioning system according to the positioning errors of the vehicle networking positioning system measured at each preset test point when the positioning accuracy of the vehicle networking positioning system is lower than the preset positioning accuracy threshold.

[0105] It should be noted that the serial numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0106] The terms "including" and "having" and any variations thereof in the description, claims and drawings of the present application are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally further include steps or units not listed, or may optionally further include other steps or units inherent to these processes, methods, products or devices. The descriptions with terms such as "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit that "first", "second" and "third" are of different types.

[0107] In the description of the embodiments of the present application, words such as "exemplary", "for example" or "for illustration" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary", "for example" or "for illustration" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary", "for example" or "for illustration" is intended to present relevant concepts in a specific manner.

[0108] In the description of the embodiments of the present application, unless otherwise specified, " / " means "or". For example, A / B may mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B may mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of the present application, "a plurality of" means two or more than two.

[0109] In some processes described in the embodiments of the present application, there are a plurality of operations or steps that appear in a specific order. However, it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or may be executed in parallel. The serial numbers of the operations are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in order or in parallel, and these operations or steps may be combined.

[0110] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium as described above (such as ROM / RAM, magnetic disk, optical disc), and includes several instructions for causing a terminal device to execute the methods described in the various embodiments of the present application.

[0111] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, is equally included in the patent protection scope of the present application.

Claims

1. A method for detecting the positioning accuracy of a vehicle networking positioning system, characterized in that The method includes: When the vehicle passes through an area with an identification map, obtain the road surface image covered by the vehicle, where the identification map is an identification image pre-drawn on the road surface and is divided into multiple identification cells of the same area; According to the road surface image, identify the identification map and the number of the identification cell directly opposite the vehicle center, and calculate the position information of the identification cell directly opposite the vehicle center; According to the position information of the identification cell directly opposite the vehicle center and the vehicle position information located by the vehicle networking positioning system, calculate the positioning error of the vehicle networking positioning system at the current point; Repeat the measurement of the positioning error of the vehicle networking positioning system at multiple preset test points, and calculate the positioning accuracy of the vehicle networking positioning system according to the positioning error values measured multiple times.

2. The positioning accuracy detection method of the vehicle networking positioning system according to claim 1, characterized in that According to the road surface image, identifying the identification map and the number of the identification cell directly opposite the vehicle center includes: Perform image recognition on the road surface image to obtain the number of the identification map directly opposite the vehicle center; Perform laser positioning on the identification cell directly opposite the vehicle center to obtain the number of the identification cell directly opposite the vehicle center.

3. The positioning accuracy detection method of the vehicle networking positioning system according to claim 1, characterized in that, Calculating the position information of the identification cell directly opposite the vehicle center includes: According to the number of the identification map directly opposite the vehicle center, obtain the position information of the identification map directly opposite the vehicle center from the preset map data; According to the number of the identification cell directly opposite the vehicle center, calculate the relative position information of the identification cell directly opposite the vehicle center in the identification map directly opposite the vehicle center; According to the position information of the identification map directly opposite the vehicle center and the relative position information, calculate the position information of the identification cell directly opposite the vehicle center.

4. The positioning accuracy detection method of the vehicle networking positioning system according to claim 1, characterized in that The calculating the positioning error of the vehicle networking positioning system at the current point according to the position information of the identification cell directly opposite the vehicle center and the vehicle position information located by the vehicle networking positioning system includes: According to the position information of the identification cell directly opposite the vehicle center, calculate the first coordinate of the identification cell directly opposite the vehicle center in the global coordinate system; According to the vehicle position information located by the vehicle networking positioning system, calculate the second coordinate of the vehicle position located by the vehicle networking positioning system in the global coordinate system; Calculate the Euclidean distance between the first coordinate and the second coordinate, and the Euclidean distance is the positioning error of the vehicle networking positioning system at the current point.

5. The positioning accuracy detection method of the vehicle networking positioning system according to claim 1, characterized in that, The repeating the measurement of the positioning error of the vehicle networking positioning system at multiple preset test points includes: For each preset test point, measure the positioning error of the vehicle networking positioning system multiple times; Perform statistical analysis on the positioning errors of the vehicle networking positioning system measured multiple times to obtain the average positioning error of each test point.

6. The positioning accuracy detection method of the vehicle networking positioning system according to claim 1, characterized in that Calculating the positioning accuracy of the vehicle networking positioning system according to the positioning error values measured multiple times includes: Calculate the probability distribution of the positioning error according to the positioning error values measured multiple times; According to the probability distribution of the positioning error, calculate the circular error probability of the vehicle networking positioning system, and the circular error probability is the positioning accuracy of the vehicle networking positioning system.

7. The positioning accuracy detection method of the vehicle networking positioning system according to claim 1, characterized in that Calculating the positioning accuracy of the vehicle networking positioning system further includes: Obtain the measured speed of the vehicle through the vehicle networking positioning system; Calculate the true speed of the vehicle according to the time when the vehicle passes through each identification cell and the Euclidean distance between each identification cell. According to the measured speed obtained by the vehicle networking positioning system and the true speed of the vehicle, calculate the speed measurement error of the vehicle networking positioning system, where the speed measurement error is the absolute value of the difference between the measured speed of the vehicle and the true speed of the vehicle.

8. The positioning accuracy detection method of the vehicle networking positioning system according to claim 1, characterized in that After calculating the positioning accuracy of the vehicle networking positioning system, it further includes: If the positioning accuracy of the vehicle networking positioning system is lower than the preset positioning accuracy threshold, calibrate the vehicle networking positioning system according to the positioning error of the vehicle networking positioning system measured at each preset test point.

9. A positioning accuracy detection device for a vehicle networking positioning system based on the method according to any one of claims 1-8, characterized in that, The device includes: An image acquisition module, configured to acquire a road surface image covered by the vehicle when the vehicle passes through an area with a logo map. An image recognition module, configured to identify the logo map and the number of the logo grid directly opposite the vehicle center according to the road surface image, and calculate the position information of the logo grid directly opposite the vehicle center. A calculation module, configured to calculate the positioning error of the vehicle networking positioning system at the current point according to the position information of the logo grid directly opposite the vehicle center and the vehicle position information positioned by the vehicle networking positioning system; and further configured to repeatedly measure the positioning error of the vehicle networking positioning system at multiple preset test points, and calculate the positioning accuracy of the vehicle networking positioning system according to the positioning error values measured multiple times.

10. The positioning accuracy detection device of the vehicle networking positioning system according to claim 9, characterized in that, The device further includes: A calibration module, configured to calibrate the vehicle networking positioning system according to the positioning error of the vehicle networking positioning system measured at each preset test point when the positioning accuracy of the vehicle networking positioning system is lower than the preset positioning accuracy threshold.

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