CN118640936B - High-precision map precision verification method, device, equipment and readable storage medium - Google Patents
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Abstract
The application provides a high-precision map accuracy verification method, a device, equipment and a readable storage medium, and relates to the technical field of automatic driving, wherein the method comprises the following steps: partitioning the real geographic area into at least one area unit; configuring at least one sampling area for each area unit based on the environmental information of the area unit; selecting at least one set of truth points in the sampling area; acquiring the real coordinates of the true value points; and determining the precision of the high-precision map based on the real coordinates and the map coordinates corresponding to the true value points in the high-precision map. The scheme can ensure that the true value point selection is more representative, so that the high-precision map data precision can be comprehensively and accurately evaluated in the subsequent data comparison process.
Description
Technical Field
The application relates to the technical field of automatic driving, in particular to a high-precision map accuracy verification method, a device, equipment and a readable storage medium.
Background
With the development of automatic driving technology, a high-precision map is taken as an important tool for sensing the environment of an automatic driving vehicle, and the data precision of the high-precision map is directly related to the safety and stability of the driving of the vehicle. However, due to the influence of laser radar measurement errors, inertial navigation system errors, time synchronization errors, calibration errors, motion distortion errors, inertial navigation attitude positioning errors and other factors, the high-precision map inevitably generates errors in the production process.
In the prior art, the space coordinates of specific position points are collected mainly by means of a traditional global navigation satellite system, a real-time dynamic carrier phase difference instrument, a total station instrument and other measuring tools, and then the space coordinates are compared with corresponding coordinates in a high-precision map, so that the precision of the high-precision map is determined.
However, in this conventional method, the error evaluation of the high-precision map is not accurate.
Disclosure of Invention
The application provides a high-precision map accuracy verification method, device, equipment and readable storage medium, which are used for solving the problem that the existing high-precision map error assessment is inaccurate.
In a first aspect, an embodiment of the present application provides a method for verifying precision of a high-precision map, including:
Partitioning the real geographic area into at least one area unit;
configuring at least one sampling area for each area unit based on the environmental information of the area unit;
selecting at least one set of truth points in the sampling area;
Acquiring the real coordinates of the true value points;
and determining the precision of the high-precision map based on the real coordinates and the map coordinates corresponding to the true value points in the high-precision map.
In one possible design of the first aspect, the configuring at least one sampling area for each area unit based on the environmental information of the area unit includes:
determining whether a target environment meeting a set condition exists in the area unit based on the environment information of the area unit;
If the target environments exist, at least one sampling area is configured for each target environment.
In another possible design of the first aspect, the target environment includes: one or any combination of mountainous areas, tunnels, high speeds, urban areas.
In yet another possible design of the first aspect, the configuring at least one sampling area for each target environment includes:
and selecting a road section outlet or inlet of the target environment as the sampling area.
In yet another possible design of the first aspect, the set of truth points includes: at least one of a horizontal verification point, an elevation verification point, and a relative accuracy verification point.
In yet another possible design of the first aspect, the determining the precision of the high-precision map based on the real coordinates and the map coordinates corresponding to the true value points in the high-precision map includes:
If the group of truth points comprise the horizontal verification point, determining the absolute precision of the high-precision map based on the real coordinates of the horizontal verification point and the map coordinates corresponding to the horizontal verification point in the high-precision map;
If the set of truth points comprise the elevation verification points, determining the absolute precision of the high-precision map based on the real coordinates of the elevation verification points and the map coordinates corresponding to the elevation verification points in the high-precision map;
And if the group of true value points comprise the relative precision verification points, determining the relative precision of the high-precision map based on the real coordinates of at least two relative precision verification points and the corresponding map coordinates of each relative precision verification point in the high-precision map.
In yet another possible design of the first aspect, the selecting at least one set of truth points in the sampling area includes:
and if the set of truth points comprises the horizontal verification point, selecting at least one horizontal verification point based on at least one position of a sharp angle of a guide belt of an outlet or an inlet of the sampling area, a merging and separating point of the outlet or the inlet of the sampling area and a specific position point in the sampling area.
If the set of truth points comprises the elevation verification points, obtaining guideboards existing around the horizontal verification points, and selecting at least one elevation verification point based on the guideboards.
And if the group of true value points comprise the relative precision verification points, acquiring a road indication mark in the target environment, and determining at least two position points in a boundary line of the road indication mark as at least two relative precision verification points.
In yet another possible design of the first aspect, the determining the relative accuracy of the high-accuracy map based on the real coordinates of at least two relative accuracy verification points and the corresponding map coordinates of each relative accuracy verification point in the high-accuracy map includes:
Acquiring precision errors between each relative precision verification point and corresponding map coordinates of the relative precision verification point in the high-precision map;
Based on at least two of the precision errors, a relative precision of the high-precision map is determined.
In yet another possible design of the first aspect, the method further comprises:
Determining the total group number of true value points required by the real geographic area according to the sampling interval distance;
and determining the group number of the truth points of each area unit according to the total group number of the truth points and the number of the sampling areas of each area unit.
In a second aspect, an embodiment of the present application provides a high-precision map accuracy verification apparatus, including:
the regional division module is used for dividing the real geographic region into at least one regional unit;
The sampling configuration module is used for configuring at least one sampling area for each area unit based on the environment information of the area unit;
the point value selecting module is used for selecting at least one group of true value points in the sampling area;
The coordinate acquisition module is used for acquiring the real coordinates of the true value points;
And the precision verification module is used for determining the precision of the high-precision map based on the real coordinates and the map coordinates corresponding to the true value points in the high-precision map.
In a third aspect, an embodiment of the present application provides an electronic device, including: a processor, and a memory communicatively coupled to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method as described above.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium having stored therein computer-executable instructions for performing a method as described above when executed by a processor.
In a fifth aspect, embodiments of the present application provide a computer program product comprising a computer program which, when executed by a processor, implements the method described above.
According to the high-precision map precision verification method, device, equipment and readable storage medium, the real geographic area is partitioned, different types of sampling areas are correspondingly selected according to different track surrounding environments in the partitioned area units, the fact that true value point selection is more representative can be guaranteed, and overall and accurate evaluation of high-precision map data precision can be achieved when data comparison is carried out subsequently.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application;
fig. 1 is a schematic view of a scene of high-precision map accuracy verification provided by an embodiment of the present application;
fig. 2 is a flow chart of a high-precision map accuracy verification method according to an embodiment of the present application;
Fig. 3 is a flow chart of a method for determining a sampling area according to an embodiment of the present application;
FIG. 4 is a flowchart of an overall framework of a high-precision map accuracy verification method provided by an embodiment of the present application;
fig. 5 is a schematic structural diagram of a high-precision map accuracy verification device according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Specific embodiments of the present application have been shown by way of the above drawings and will be described in more detail below. The drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but rather to illustrate the inventive concepts to those skilled in the art by reference to the specific embodiments.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of 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, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
With the development of automatic driving technology, a high-precision map (simply referred to as a high-precision map) is taken as an important tool for sensing the environment of an automatic driving vehicle, and the data precision of the high-precision map is directly related to the safety and stability of the driving of the vehicle. However, due to the influence of radar measurement errors, inertial navigation system errors, time synchronization errors, calibration errors, motion distortion errors, inertial navigation attitude positioning errors and other factors, the high-precision map inevitably generates errors in the production process. Therefore, how to accurately evaluate the error distribution of the map data and ensure the accuracy of the map data becomes a key problem for monitoring the production quality of high-precision maps.
In the related art, the method for evaluating the precision of the high-precision map mainly relies on the traditional global navigation satellite system (Global Navigation SATELLITE SYSTEM, GNSS), real-time dynamic carrier phase difference (Real-TIME KINEMATIC), total station and other measuring tools, and the precision of the map data is evaluated by measuring the space coordinates of specific characteristic points on the map and comparing the space coordinates with corresponding coordinates in the map data. However, the methods often have the problems of insufficient scientific and comprehensive sampling point selection, insufficient precision error evaluation and the like, and are difficult to meet the requirements of high-precision map production.
Aiming at the problems, the application provides a high-precision map precision verification scheme, which is characterized in that a real geographic area is partitioned into area units, sampling areas are selected in each area unit, and verification points of different types are correspondingly selected according to different track surrounding environments to perform data comparison, so that comprehensive and accurate evaluation of absolute precision of high-precision map data is realized, and reliable technical support is provided for map production quality monitoring and map product use.
For example, fig. 1 is a schematic view of a scene of high-precision map precision verification provided by the embodiment of the present application, as shown in fig. 1, when the high-precision map is verified, real coordinates (for example, coordinates of lane merging points in fig. 1) of some verification points may be collected on site, then the real coordinates are compared with corresponding map coordinates in the high-precision map, and by comparing differences between the two coordinates, whether the precision of the high-precision map is reliable or not may be mastered, so as to implement evaluation of the precision of the high-precision map.
The technical scheme of the application is described in detail through specific embodiments. It should be noted that the following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.
Fig. 2 is a flow chart of a high-precision map accuracy verification method according to an embodiment of the present application, where the method may be applied to an electronic device (for example, a computer in fig. 1), and the method may specifically include the following steps:
step S210: the real geographic area is partitioned into at least one area unit.
In this embodiment, the real geographic area represents the actual spatial environment, and the corresponding geographic area is also in the high-precision map, but the geographic area in the high-precision map is usually formed by scaling the real geographic area based on the corresponding scale.
By way of example, taking a real geographic area as an area Q, a high-precision map corresponding to the area Q is formed after mapping the area Q. The high-precision map inevitably generates errors in the production process, and if the errors are too large, the safety of the automatic driving vehicle can be affected, namely, if the automatic driving vehicle drives in the area Q based on the high-precision map as a navigation basis, if the errors of the high-precision map are large, the automatic driving vehicle can possibly collide with dangerous situations such as obstacles and the like.
In this embodiment, the area occupied by each area unit may be directly divided, so as to partition the real geographic area, for example, reasonable size is configured for each area unit, so that differentiation of the real geographic area may be realized. Specifically, a certain point of the vehicle in the real geographic area is taken as an origin, every n kilometers (n is a positive integer) of the vehicle are driven in different directions, boundary points in multiple directions are obtained, and then the boundary points are connected, so that an area unit is obtained.
In other embodiments, the real geographic area may also be partitioned by environmental information, such as where it is desired to ensure that each area unit has a particular geomorphic environment (e.g., mountainous areas, tunnels, high speeds, urban areas, etc.). Among them, these specific landform environments are often used as influencing factors, which cause errors in the production process of the high-precision map (for example, some landform environments influence the signal reception of the acquisition vehicle, so that the data of the acquisition vehicle is inaccurate, thereby causing errors in the production process of the high-precision map).
By configuring specific relief environments in each area unit, the method is more representative, and whether the high-precision map has errors in the relief environments can be verified later.
Step S220: at least one sampling region is configured for each region unit based on the environmental information of the region unit.
In this embodiment, the sampling area refers to an area in which field sampling is required later in the area unit. The sampling area is smaller than the whole area unit range, so that subsequent field sampling is facilitated, and a true value point is selected.
The environmental information may refer to road track environmental information in the area unit, and when the road track environmental information is in the environments, mapping accuracy is easily affected, which leads to errors generated in the production process of the high-precision map.
By way of example, the environmental information may include mountainous areas, high speeds, tunnels, urban areas, and the like.
In this embodiment, each area unit may include a plurality of such environmental information, for example, the area unit a includes both tunnels and mountainous areas. Each piece of environment information can be configured with a sampling area so as to ensure the selected integrity of the subsequent data (namely, the true value point), and further improve the accuracy of the subsequent verification.
Step S230: at least one set of truth points is selected in the sampling area.
Step S240: and obtaining the true coordinates of the true value points.
In this embodiment, the true coordinates of the true value points may be obtained by measuring the true value points by a measurer in the field, and the accuracy of the true coordinates is higher. The coordinates in the high-precision map are usually measured by corresponding equipment (such as a collection vehicle) through corresponding instruments, so that the accuracy is poor, and errors in the production of the high-precision map are easily caused.
In this embodiment, some representative location points may be selected as the truth points in the sampling area, for example, a specific location point at the entrance or exit of the tunnel may be selected, or a specific location point at the high-speed entrance or exit may be selected as the truth points.
In order to ensure that the data are more comprehensive, the number of groups of the true value points can be as much as possible, so that the accuracy of the verification result can be ensured when the accuracy of the high-precision map is verified later.
In addition, when the total group number of the true value points is fixed, the true value points of the corresponding group numbers need to be reasonably distributed for each area unit. Specifically, in some embodiments, the total number of truth points required for verifying the high-precision map corresponding to the real geographic area may be determined by combining the actual spatial area occupied by the real geographic area and the set sampling interval distance (for example, 150-200 km), then, whether each area unit includes a target environment meeting the condition is further determined by combining with each area unit, finally, the number of sampling areas of each area unit is further determined, and finally, based on the number of sampling areas of each area unit and the total number of truth points, a plurality of groups of truth points are uniformly configured for each area unit.
Specifically, taking the example of a sampling interval distance of 150 km, assuming that in a real geographic area, a set of truth points need to be selected at an interval of 150 km, and a total of 1250 sets of truth points need to be selected, the 1250 sets of truth points need to be uniformly distributed to each area unit in combination with the number of target environments contained in each area unit (in this way, the accuracy of each area unit in a high-precision map is conveniently verified later).
Taking the example that the real geographic area is divided into an area unit a, an area unit B and an area unit C, if the area unit a includes a mountain area, a tunnel and a high speed, the area unit B includes the tunnel and the high speed, and the area unit C includes only the high speed, then the area unit a needs to allocate more groups of true value points. Illustratively, the ratio of the true value point set numbers for region unit A, region unit B, and region unit C is 3:2:1.
According to the embodiment, the real geographic area is partitioned, so that the true value point group number required to be selected is distributed uniformly for each area unit according to the true value point total group number, and the accuracy of the high-precision map corresponding to each area unit is verified better.
Step S250: and determining the precision of the high-precision map based on the map coordinates corresponding to the real coordinates and the true value points in the high-precision map.
In this embodiment, the real coordinates and the map coordinates may include values in three directions (i.e., a horizontal axis coordinate value, a horizontal vertical axis coordinate value, and a vertical direction coordinate value), and the accuracy of the high-precision map may be determined by comparing the differences between the three values of the real coordinates and the map coordinates.
For example, taking the coordinates of the real coordinates as (x 1, y1, z 1) and the coordinates of the map coordinates as (x 2, y2, z 2) as examples, if the difference between the position distances of the two coordinate points is greater than 1 meter, the accuracy of the high-precision map is poor.
According to the embodiment of the application, the real geographic area is partitioned, and different types of sampling areas are correspondingly selected according to different track surrounding environments in the partitioned area units, so that the selection of the follow-up truth value points is more representative, and the comprehensive and accurate evaluation of the high-precision map data accuracy can be realized when the follow-up data comparison is carried out.
The following describes in detail how the sampling area is selected by some embodiments.
Fig. 3 is a flow chart of a method for determining a sampling area according to an embodiment of the present application, as shown in fig. 3, the method includes the following steps:
Step S310: based on the environment information of the area unit, it is determined whether or not there is a target environment satisfying the set condition in the area unit.
In this embodiment, various environments may be included in the area unit, and an environment that does not affect the precision of the high-precision map is referred to as a normal environment (e.g., an open road), an environment that does affect the precision of the high-precision map is referred to as an abnormal environment (e.g., a mountain area, a tunnel, etc.), and then the target environment may refer to these abnormal environments. That is, the setting condition may refer to: which affects the environment of high precision map accuracy.
When dividing the regional units, the real geographic region can be divided directly by configuring the occupied range of each regional unit. The area unit obtained by partitioning may include the normal environment and the abnormal environment, or may include only the normal environment. When only the normal environment is included, the sampling area can be determined only in the normal environment, so that the subsequent truth point selection can be performed. In this embodiment, when an abnormal environment (i.e., a target environment) exists in the area unit, the sampling area may be determined in the abnormal environment, so that the truth point selected later is more representative, and the accuracy of high-precision map verification is further improved.
For example, in some embodiments, the target environment may include: one or any combination of mountainous areas, tunnels, high speeds, urban areas.
In practical application, the high-precision map is required to be acquired by the acquisition vehicle, and when the acquisition vehicle acquires data in the target environments, signals are likely to be weak or cannot be received due to the influence of the environmental factors, so that errors may exist in the finally produced high-precision map. In addition, if the high-precision map is mainly used for automatic driving, the target environment at least comprises a lane, and the selection of the true value point can be realized based on the corresponding characteristic positions existing in the surrounding area of the lane.
In particular, taking a mountain area as an example, due to large relief, satellite signals may be blocked by objects such as buildings, trees or rocks, and the like, resulting in weak or unreceivable signals. For another example, there may be a large area of water around high speeds: reflections from the surface of the body of water may cause multipath effects to occur and, due to the high reflectivity of the body of water, the strength of the reflected signal may exceed that of the direct signal, making it difficult for the receiver to resolve. Also for example, urban high speeds are at locations other than tall or dense buildings, valleys, canyons, etc., where GPS signals may be blocked, forming a location blind area. Meanwhile, large-area signal towers, power stations, transformer substations, high-voltage cables, radars and the like can generate electromagnetic interference in the target environments, and the transmission and the reception of GPS signals are interfered.
Further, based on the above, in some embodiments, in determining the sampling area, a road segment exit or entrance of the target environment may be selected as the sampling area.
For example, if a mountain area exists in the area unit, a road section outlet or an entrance of the mountain area can be selected as a sampling area of the mountain area, and if a tunnel exists in the area unit, a road section outlet or an entrance of the tunnel can be selected as a sampling area of the tunnel; finally, if the high speed exists in the area unit, the exit or entrance of the road section with the high speed can be selected as a high-speed sampling area.
In this embodiment, whether the position of the exit or entrance of the road or tunnel is accurate or not has a great influence on the accuracy of the high-accuracy map, and when the position of the exit or entrance is inaccurate, a great safety risk may be caused to the automatic driving vehicle.
According to the embodiment, the target environment is set to be one or any combination of a mountain area, a tunnel and a high speed, and the entrance or the exit of a mountain area section, the entrance or the exit of the tunnel and the exit or the entrance of a high-speed section are taken as sampling areas, so that various different types of sampling areas can be determined, and the sufficiency and rationality of data in the process of selecting a true value point later are ensured.
Step S320: if the target environments exist, at least one sampling area is configured for each target environment.
In this embodiment, the target environment may configure a plurality of sampling areas, and by taking the target area as an example of a tunnel, one sampling area may be configured at the entrance of the tunnel, and one sampling area may also be configured at the exit of the tunnel, so that the number of truth point selections may be improved in the following steps, thereby further improving the accuracy of the following high-precision map verification.
According to the embodiment of the application, the sampling area is configured for the target environment preferentially, so that more representative true value points can be selected from the sampling area later, and whether the high-precision map has errors or not can be verified more accurately.
The selection of the truth points and the truth points is described in detail below with some embodiments.
In some embodiments, the set of truth points may include at least one of a horizontal verification point, an elevation verification point, a relative accuracy verification point. The horizontal verification point and the elevation verification point are used for determining the absolute precision of the high-precision map, and the relative precision verification point is used for determining the relative precision of the high-precision map.
The horizontal verification point may be a feature point on a road surface, for example, a feature point at a certain position on a road surface indication sign, and the elevation verification point may be a feature point at a certain position on a road guideboard, wherein the guideboard elevation may be 5 meters, and the guideboard elevation specifically refers to the height of the guideboard relative to the ground, and is set according to actual needs and road design. The elevation verification point may be a tunnel sign of the tunnel, which is typically disposed before and within the tunnel entrance for providing information of tunnel length, speed limit, prohibited overtaking, etc.
In some embodiments, the selection of the truth point may have the following two ways that may improve the subsequent accuracy verification effect:
selecting at least one horizontal verification point and at least one elevation verification point in a sampling area to serve as a group of truth points;
Mode (2) selects at least one horizontal verification point, at least one elevation verification point, and at least two relative accuracy verification points in the sampling area as a set of truth points.
In some embodiments, to improve accuracy of the post-accuracy verification, each set of truth points should include at least a horizontal verification point and an elevation verification point, and further, the relative accuracy verification points may be selected to be default according to actual situations.
In this embodiment, the distribution of the true value points in the area unit should be as uniform as possible to ensure the accuracy of the subsequent verification of the high-precision map. For example, a set of truth points may be collected in the area unit every 250-300 km.
Further, when the true value point is selected, the corresponding selection modes of different types of verification points (namely, horizontal verification points, elevation verification points and relative precision verification points) are different, and the method specifically comprises the following steps:
Mode one: if the set of truth points comprises horizontal verification points, at least one horizontal verification point is selected based on at least one position of a sharp angle of a guide belt of an outlet or an inlet of the sampling area, a merging and separating point of the outlet or the inlet of the sampling area and a specific position point of the sampling area.
Mode two: if the set of truth points comprises elevation verification points, obtaining guideboards existing around the horizontal verification points, and selecting at least one elevation verification point based on the guideboards.
Mode three: if the group of true value points comprise the relative precision verification points, acquiring a road indication mark in the target environment, and determining at least two position points in a boundary line of the road indication mark as at least two relative precision verification points.
In the selection of the verification point, a clear and high-density outlet or inlet (including an outlet or an inlet of a road section and a tunnel) should be selected. Furthermore, when the elevation verification point is selected, the guideboard with the flow guide belt and lower than 5 meters at the outlet or inlet is selected as much as possible, so that the rationality and the accuracy of the selection of the truth point are ensured.
Aiming at the mode one:
In this embodiment, the entrance/exit guide belt sharp angle means a triangular zone of a high-speed exit or entrance provided with a guide belt, in which a sharp angle portion is used to guide a vehicle to travel along a specified route. The sharp angle of the flow guide strip is typically in the form of a V-shaped or diagonal area, the dimensions of which are set according to the specific intersection topography. The position of the sharp angle of the guide belt can be selected as a horizontal verification point, and other adjacent or similar positions can be selected near the sharp angle of the guide belt to be used as the horizontal verification point.
In this embodiment, the merging and separating point of the outlet or inlet may refer to a merging or separating point where the vehicle needs to perform a merging or separating operation at a high speed. The merging and separating points can be selected as a horizontal verification point, and other adjacent or similar positions can be selected near the merging and separating points to be used as the horizontal verification point.
In this embodiment, if the sampling area is a tunnel, the feature location point of the sampling area may be a front feature point or a rear feature point of the tunnel. The front characteristic point or the rear characteristic point of the tunnel refers to a specific position before and after the entrance and the exit of the tunnel, and is usually used for setting an alarm sign or a speed limit sign.
Aiming at the second mode:
In this embodiment, the guideboard may refer to a sign vertically set on a road section, which is used to indicate a road section where the user is currently located, a road section which is selectable in front, and the like. The height of the guideboard refers to the height of the guideboard relative to the ground, and the guideboard is set according to actual needs and road design. If the sampling area is a tunnel, the guideboard may be referred to as a tunnel label, which is typically disposed in front of and within the tunnel entrance for providing information on tunnel length, speed limit, prohibited overtaking, etc.
Aiming at a mode III:
since the relative accuracy verification point is used to verify the relative accuracy of the high-accuracy map (i.e., whether there is an error in the distance between two points), at least two or more relative accuracy verification points need to be selected. In general, road indication marks such as ground lines, guideboards, longitudinal length indication lines, guide arrows, and features in tunnels are present during the running of the vehicle, and a relative accuracy verification point can be selected from the road indication marks.
Illustratively, the ground line is of a length-width dimension, so that if one relative accuracy verification point is selected in front of, behind or in the left-right direction of the ground line, the actual distance between the two relative accuracy verification points can be determined, thereby being used for verifying whether the distance between the two relative accuracy verification points in the high-accuracy map is in error with the actual distance.
The highway ground line comprises a lane line, a vehicle distance confirmation line and the like, and the length and the width of the highway ground line play an important role in ensuring driving safety and providing visual guidance. Illustratively, the lane lines are each 6 meters long with a white dashed line, the distance between the two lines being 9 meters, the distance between the two white lines plus the middle being 21 meters in total.
Similarly, the guideboard also has a length and a width, and the guideboard on the expressway is used for providing information such as direction indication, distance prompt and the like, and the length and the width of the guideboard are set according to actual needs. The design of the guideboard ensures that a driver can clearly recognize when driving at a high speed and react in advance. In addition, in some cases, a longitudinal 100 meter length mark is set on the expressway to indicate that the driver keeps a safe distance from the front vehicle. The guide arrow also has a length and a width, and is used for indicating the running direction of the vehicle, and the length of the guide arrow is set according to actual needs and the road width. Features which may exist in the tunnel include emergency stop areas, fire-fighting equipment, monitoring cameras and the like, and the length and the width of the features are set according to actual needs.
When the truth points are selected in the first to third modes, environmental factors are considered, for example, environmental factors in a sampling area are relatively bad, and may affect the accuracy of the collection of the truth point coordinates, so that the coordinates of the truth points collected in the bad environments need to be marked, and when the high-precision map is verified later, the coordinates of the truth points collected in the bad environments can be considered as extreme points and distinguished from the coordinates of the truth points collected in the normal environment.
If the error between the true value point coordinates collected under some normal environments and the corresponding coordinates in the high-precision map is large, it may be stated that the problem of failure of the collection vehicle may occur in the process of making the high-precision map, so that the problem of the original data used for making the high-precision map may occur. In addition, the error range of the coordinates of the true points acquired under some severe environments and the corresponding coordinates in the high-precision map can be properly widened so as to exclude the influence of environmental factors.
After the truth point is determined, the true space coordinates of the selected truth point can be obtained by precisely measuring the true point by using measuring tools such as GNSS, RTK, total station and the like, and the true space coordinates are used as the true coordinates.
By way of example, the following list three verification point selection examples:
Example one: taking the area unit A as an example, the mountain expressway in the area unit A is taken as a sampling area, and the horizontal verification point can select a clear road section entrance of the mountain expressway with higher density, so that the road surface is flat, no shielding object exists, and the horizontal accuracy verification is suitable. The elevation verification point can select a guideboard which is less than 5 meters far from the entrance, and the guideboard is positioned in the open area, so that the elevation precision of the guideboard can be conveniently measured. In addition, because the mountain area has complex topography, a relative accuracy verification point (defaults) can select an obvious characteristic point in front of the tunnel, such as a warning sign in front of the tunnel portal.
The mountain area high speed is used as a sampling area because the mountain area is traversed, the topography is large, surrounding trees are dense, and the satellite signal receiving is affected to a certain extent, so that the mountain area high speed is selected as the sampling area, and the mountain area high speed is more representative.
Example two: taking the area unit B as an example, a suburban expressway between the cities C1 and C2 is selected as a sampling area, and the road section is located in the suburban area, and has partial buildings and trees around, but has little influence on satellite signal reception as a whole. The horizontal verification point can be used for selecting a straight road section outlet of the suburban expressway, the road section has wide view field, moderate vehicle flow and clear point cloud data. The elevation verification point can select a guideboard with a diversion belt and less than 5 meters near the outlet, and the guideboard is positioned in an open area, so that elevation accuracy verification is facilitated. The relative accuracy verification point (default) can be selected as a curve near the exit, and the lane line on the curve is clear and can be used as the relative accuracy verification point.
Example three: taking the area unit C as an example, urban common roads of the city C3 in the area unit C are selected as sampling areas, the road sections are located in urban central areas, surrounding high buildings stand, and vehicles and pedestrians are numerous, so that satellite signal reception is affected to a certain extent, and electromagnetic interference and signal shielding problems are likely to exist. The horizontal verification point can be used for selecting an intersection entrance of an urban common road, traffic is busy at the intersection entrance, but point cloud data are still clear, and the horizontal verification point can be used for horizontal accuracy verification. The elevation verification point can select a guideboard with traffic indication information near the intersection, and the guideboard is positioned in an open area and has moderate height, so that elevation accuracy verification is convenient to carry out. The city common road may be defaulted here because of the lack of obvious relative accuracy verification points.
In addition, in some embodiments, for example, the accuracy of data sources during high-precision map verification is guaranteed, and the data accuracy of the true value points can be verified by self, so that inaccurate true value point data is eliminated, and the objectivity and fairness of subsequent high-precision map evaluation are guaranteed.
According to the embodiment of the application, the regional units are divided, and the truth points are uniformly extracted by taking the regional units as units, so that the comprehensiveness and representativeness of the truth points are ensured. Meanwhile, according to the track surrounding environment and road characteristics, sampling areas of different types are selected, so that the sufficiency and rationality of sampling are ensured. In addition, through detailed truth point selection criteria, including clear road section exit or entrance with higher density, diversion belts and guideboards at road section entrance and exit, and various road characteristic points, such as front/rear characteristic points of tunnels, land line length and width, guideboard length and width, the accuracy and effectiveness of the truth point are ensured. Finally, the influence of environmental factors on GPS signals, such as mountain areas, water areas, electromagnetic interference and the like, is fully considered when the truth point is selected, so that the reliability of the truth point is improved.
How to verify the accuracy of the high-precision map is described in detail below by some embodiments.
It has been mentioned above that a set of truth points may include at least one of a horizontal verification point, an elevation verification point, a relative accuracy verification point. Wherein, horizontal verification point and elevation verification point can be used to verify the absolute precision of high-precision map, and relative precision verification point can be used to verify the relative precision of high-precision map. Three example cases are specifically provided as follows:
Case ①: if the group of truth points comprise horizontal verification points, determining the absolute precision of the high-precision map based on the real coordinates of the horizontal verification points and the corresponding map coordinates of the horizontal verification points in the high-precision map;
Case ②: if the set of truth points comprise elevation verification points, determining the absolute precision of the high-precision map based on the real coordinates of the elevation verification points and the map coordinates corresponding to the elevation verification points in the high-precision map;
Case ③: if the group of true value points comprises the relative precision verification points, determining the relative precision of the high-precision map based on the real coordinates of at least two relative precision verification points and the corresponding map coordinates of each relative precision verification point in the high-precision map.
For case ① and case ②, the following formulas may be set:
yield= (number of points/sample size less than threshold value) ×100%
In the above-mentioned method, the step of,、、True coordinates of the true value point p1 on the X axis, the Y axis and the Z axis of the point cloud are respectively,、、The coordinates of the true value point p1 on the X axis, the Y axis and the Z axis of the corresponding points on the high-precision map are respectively set values (for example, 1 meter), the points smaller than the threshold value are the number of the true value points with absolute precision D smaller than the threshold value, and the sample size is the total amount of the selected true value points.
For the case ③, when determining the relative accuracy of the high-precision map, it is necessary to acquire accuracy errors between each relative accuracy verification point and corresponding map coordinates of the relative accuracy verification point in the high-precision map, and then determine the relative accuracy of the high-precision map based on at least two accuracy errors.
For example, taking the first relative accuracy verification point and the second relative accuracy verification point as examples, the absolute accuracy of the first relative accuracy verification point and the absolute accuracy of the second relative accuracy verification point can be calculated through the absolute accuracy calculation formula, and then the relative accuracy of the high-accuracy map can be further determined based on the two absolute accuracy.
By way of example, the relative accuracy of a high-precision map may be calculated by the following formula:
yield= (number of points/sample size less than threshold value) ×100%
In the above-mentioned method, the step of,、Verifying the real coordinates of the point on the X axis and the Y axis of the point cloud respectively for the first relative precision;、 verifying the true coordinates of the point on the X axis and the Y axis of the point cloud for the second relative precision respectively; 、 map coordinates of the first relative accuracy verification point on the corresponding X axis and Y axis on the high-accuracy map are respectively; 、 the corresponding map coordinates of the second relative accuracy verification point on the X axis and the Y axis on the high-accuracy map are respectively, the threshold value is a set value (for example, 10 meters), the points smaller than the threshold value are the number of true value points with the relative accuracy smaller than the threshold value, and the sample size is the total amount of the selected true value points.
The relative accuracy verification point is usually a position point on a horizontal plane in a real scene, so that the real coordinate of the relative accuracy verification point on the z axis of the point cloud is omitted in the relative accuracy calculation formula. It should be noted that, in some special cases, if the relative accuracy verification point has a real coordinate on the z-axis of the point cloud, a new relative accuracy calculation formula may be reconstructed by referring to the form of the relative accuracy calculation formula, so as to calculate the relative accuracy.
The result set is calculated according to the formula, the qualification can be judged by meeting certain quality requirements, and the quality requirements of the absolute precision are defined as 98% to less than or equal to 1m.
According to the embodiment of the application, the real coordinates of the horizontal verification point, the elevation verification point and the relative precision verification point are utilized, so that the absolute precision and the relative precision of the high-precision map can be verified, and the verification can be more sufficiently and accurately.
Fig. 4 is an overall frame flow chart of the high-precision map accuracy verification method provided by the embodiment of the application, and as shown in fig. 4, the overall frame flow chart is divided into 4 major steps:
step (1): and selecting points.
Step (II): and (5) obtaining a true value.
Step (III): and (5) data comparison.
Step (IV): and (5) precision evaluation.
In this embodiment, the truth points are uniformly selected from each area unit through the step (1), and the truth points are guaranteed to include feature points in various different environments, then the selected truth points are accurately measured through the step (second) by using measurement tools such as GNSS, RTK and total station, the spatial coordinates of the selected truth points are obtained as real coordinates, and then the measured real coordinates are compared with the map coordinates of the corresponding points in the high-precision map data through the step (third), so that an error value between the real coordinates and the map coordinates is calculated. And finally, evaluating the precision of the high-precision map data according to the magnitude and distribution of the error values and generating a corresponding evaluation report. Through the steps, the method can realize comprehensive and accurate evaluation of absolute precision and relative precision of high-precision map data, and provides reliable support for map production quality monitoring and map product use.
The following are examples of the apparatus of the present application that may be used to perform the method embodiments of the present application. For details not disclosed in the embodiments of the apparatus of the present application, please refer to the embodiments of the method of the present application.
Fig. 5 is a schematic structural diagram of a high-precision map precision verification device according to an embodiment of the present application, and as shown in fig. 5, the high-precision map precision verification device 500 includes a region dividing module 510, a sampling configuration module 520, a point value selecting module 530, a coordinate obtaining module 540, and a precision verification module 550.
Wherein the region division module 510 is configured to divide the real geographic region into at least one region unit. The sampling configuration module 520 is configured to configure at least one sampling area for each area unit based on the environmental information of the area unit. The point value selection module 530 is configured to select at least one set of truth points in the sampling area. The coordinate acquisition module 540 is configured to acquire real coordinates of the true value point. The accuracy verification module 550 is configured to determine the accuracy of the high-accuracy map based on the real coordinates and the map coordinates corresponding to the true value points in the high-accuracy map.
Optionally, the sampling configuration module may specifically be configured to: determining whether a target environment meeting a set condition exists in the area unit based on the environment information of the area unit; if the target environments exist, at least one sampling area is configured for each target environment.
Optionally, the target environment includes: one or any combination of mountainous areas, tunnels, high speeds, urban areas.
Optionally, the sampling configuration module may specifically be configured to: and selecting a road section outlet or inlet of the target environment as a sampling area.
Optionally, the set of truth points includes: at least one of a horizontal verification point, an elevation verification point, and a relative accuracy verification point.
Optionally, the accuracy verification module may specifically be configured to: if the group of truth points comprise horizontal verification points, determining the absolute precision of the high-precision map based on the real coordinates of the horizontal verification points and the corresponding map coordinates of the horizontal verification points in the high-precision map; if the set of truth points comprise elevation verification points, determining the absolute precision of the high-precision map based on the real coordinates of the elevation verification points and the map coordinates corresponding to the elevation verification points in the high-precision map; if the group of true value points comprises the relative precision verification points, determining the relative precision of the high-precision map based on the real coordinates of at least two relative precision verification points and the corresponding map coordinates of each relative precision verification point in the high-precision map.
Optionally, the point value selecting module may specifically be configured to: if the set of truth points comprises horizontal verification points, at least one horizontal verification point is selected based on at least one position of a sharp angle of a guide belt of an outlet or an inlet of the sampling area, a merging and separating point of the outlet or the inlet of the sampling area and a specific position point in the sampling area. If the set of truth points comprises elevation verification points, obtaining guideboards existing around the horizontal verification points, and selecting at least one elevation verification point based on the guideboards. If the group of true value points comprise the relative precision verification points, acquiring a road indication mark in the target environment, and determining at least two position points in a boundary line of the road indication mark as at least two relative precision verification points.
Optionally, the accuracy verification module may specifically be configured to: acquiring precision errors between each relative precision verification point and corresponding map coordinates of the relative precision verification point in the high-precision map; based on the at least two precision errors, a relative precision of the high-precision map is determined.
Optionally, the system further comprises a group number determining module, which is used for determining the total group number of true value points required by the real geographic area according to the sampling interval distance; and determining the group number of the truth points of each area unit according to the total group number of the truth points and the number of the sampling areas of each area unit.
The device provided by the embodiment of the application can be used for executing the method in the embodiment shown above, and the implementation principle and technical effects are similar, and are not repeated here.
It should be noted that, it should be understood that the division of the modules of the above apparatus is merely a division of a logic function, and may be fully or partially integrated into a physical entity or may be physically separated. And these modules may all be implemented in software in the form of calls by the processing element; or can be realized in hardware; the method can also be realized in a form of calling software by a processing element, and the method can be realized in a form of hardware by a part of modules. For example, the area dividing module may be a processing element that is set up separately, may be implemented in a chip of the above-described apparatus, or may be stored in a memory of the above-described apparatus in the form of program codes, and the functions of the area dividing module may be called and executed by a processing element of the above-described apparatus. The implementation of the other modules is similar. In addition, all or part of the modules can be integrated together or can be independently implemented. The processing element here may be an integrated circuit with signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in a software form.
For example, the modules above may be one or more integrated circuits configured to implement the methods above, such as: one or more Application SPECIFIC INTEGRATED Circuits (ASIC), or one or more microprocessors (DIGITAL SIGNAL processors, DSP), or one or more field programmable gate arrays (field programmable GATE ARRAY, FPGA), etc. For another example, when a module above is implemented in the form of processing element scheduler code, the processing element may be a general purpose processor, such as a central processing unit (central processing unit, CPU) or other processor that may invoke the program code. For another example, the modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 6, the electronic device 600 includes: at least one processor 601, memory 602, bus 603, and communication interface 604. The processor, the communication interface and the memory are in communication with each other through the bus. The communication interface is used for communicating with other devices. The communication interface comprises a communication interface for data transmission, a display interface or an operation interface for human-computer interaction, and the like. A processor for executing computer-executable instructions, and in particular for performing the relevant steps of the methods described in the above embodiments.
The processor may be a central processing unit (cpu), or an Application SPECIFIC INTEGRATED Circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included in the electronic device may be the same type of processor, such as one or more CPUs; but may also be different types of processors such as one or more CPUs and one or more ASICs. The memory is used for storing computer execution instructions. The memory may comprise high speed RAM memory or may also comprise non-volatile memory, such as at least one disk memory.
The present embodiment also provides a computer-readable storage medium having stored therein computer instructions which, when executed by at least one processor of an electronic device, perform the methods provided by the various embodiments described above.
The present embodiments also provide a computer program product comprising computer instructions stored on a readable storage electronic device that can be read from a readable storage medium by at least one processor, the at least one processor executing the computer instructions causing the electronic device to implement the methods provided by the various embodiments described above.
The above-described readable storage medium may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk. A readable storage medium can be any available medium that can be accessed by a general purpose or special purpose computer.
An exemplary readable storage medium is coupled to the processor such the processor can read information from, and write information to, the readable storage medium. In the alternative, the readable storage medium may be integral to the processor. The processor and the readable storage medium may reside in an Application SPECIFIC INTEGRATED Circuits (ASIC). The processor and the readable storage medium may reside as discrete components in a device.
The division of units is merely a logical function division, and there may be another division manner in actual implementation, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In the present application, "at least one" means one or more, and "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a alone, a and B together, and B alone, wherein a, B may be singular or plural. The character "/" generally indicates that the front and rear associated objects are an "or" relationship; in the formula, the character "/" indicates that the front and rear associated objects are a "division" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b, or c may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or plural.
It will be appreciated that the various numerical numbers referred to in the embodiments of the present application are merely for ease of description and are not intended to limit the scope of the embodiments of the present application. In the embodiment of the present application, the sequence number of each process does not mean the sequence of the execution sequence, and the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application in any way.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application.
Claims (12)
1. The high-precision map accuracy verification method is characterized by comprising the following steps of:
Partitioning the real geographic area into at least one area unit;
configuring at least one sampling area for each area unit based on the environmental information of the area unit;
selecting at least one set of truth points in the sampling area;
Acquiring the real coordinates of the true value points;
Determining the precision of the high-precision map based on the real coordinates and the map coordinates corresponding to the true value points in the high-precision map;
The configuring at least one sampling area for each area unit based on the environmental information of the area unit includes:
determining whether a target environment meeting a set condition exists in the area unit based on the environment information of the area unit;
If the target environments exist, at least one sampling area is configured for each target environment.
2. The method of claim 1, wherein the target environment comprises: one or any combination of mountainous areas, tunnels, high speeds, urban areas.
3. The method of claim 1, wherein configuring at least one sampling area for each target environment comprises:
and selecting a road section outlet or inlet of the target environment as the sampling area.
4. A method according to any one of claims 1-3, wherein the set of truth points comprises: at least one of a horizontal verification point, an elevation verification point, and a relative accuracy verification point.
5. The method of claim 4, wherein the determining the precision of the high-precision map based on the real coordinates and the map coordinates corresponding to the true value points in the high-precision map comprises:
If the group of truth points comprise the horizontal verification point, determining the absolute precision of the high-precision map based on the real coordinates of the horizontal verification point and the map coordinates corresponding to the horizontal verification point in the high-precision map;
If the set of truth points comprise the elevation verification points, determining the absolute precision of the high-precision map based on the real coordinates of the elevation verification points and the map coordinates corresponding to the elevation verification points in the high-precision map;
And if the group of true value points comprise the relative precision verification points, determining the relative precision of the high-precision map based on the real coordinates of at least two relative precision verification points and the corresponding map coordinates of each relative precision verification point in the high-precision map.
6. The method of claim 4, wherein selecting at least one set of truth points in the sampling area comprises:
If the set of truth points comprise the horizontal verification points, selecting at least one horizontal verification point based on at least one position of a sharp angle of a guide belt of an outlet or an inlet of the sampling area, a merging and separating point of the outlet or the inlet of the sampling area and a specific position point in the sampling area;
If the set of truth points comprise the elevation verification points, obtaining guideboards existing around the horizontal verification points, and selecting at least one elevation verification point based on the guideboards;
and if the group of true value points comprise the relative precision verification points, acquiring a road indication mark in a target environment, and determining at least two position points in a boundary line of the road indication mark as at least two relative precision verification points.
7. The method of claim 5, wherein the determining the relative accuracy of the high-accuracy map based on the true coordinates of at least two relative accuracy verification points and the corresponding map coordinates of each relative accuracy verification point in the high-accuracy map comprises:
Acquiring precision errors between each relative precision verification point and corresponding map coordinates of the relative precision verification point in the high-precision map;
Based on at least two of the precision errors, a relative precision of the high-precision map is determined.
8. The method according to claim 1, wherein the method further comprises:
Determining the total group number of true value points required by the real geographic area according to the sampling interval distance;
and determining the group number of the truth points of each area unit according to the total group number of the truth points and the number of the sampling areas of each area unit.
9. A high-precision map accuracy verification device, characterized by comprising:
the regional division module is used for dividing the real geographic region into at least one regional unit;
The sampling configuration module is used for configuring at least one sampling area for each area unit based on the environment information of the area unit;
the point value selecting module is used for selecting at least one group of true value points in the sampling area;
The coordinate acquisition module is used for acquiring the real coordinates of the true value points;
The precision verification module is used for determining the precision of the high-precision map based on the real coordinates and the map coordinates corresponding to the true value points in the high-precision map;
The sampling configuration module is specifically configured to determine whether a target environment meeting a set condition exists in the area unit based on the environmental information of the area unit; if the target environments exist, at least one sampling area is configured for each target environment.
10. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1-8.
11. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1 to 8.
12. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements the method of any of claims 1-8.
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