CN112162304A - Road network matching method, device, electronic equipment and medium - Google Patents
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- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
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- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
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Abstract
The present disclosure provides a road network matching method, including: recording a first position point and a second position point of the target vehicle; acquiring all route segments in a preset area with the first position point or the second position point as a central point; respectively calculating an included angle between a connecting line of the first position point and the second position point and each route segment, and determining a route segment corresponding to the minimum included angle; calculating the distance from the central point to the route segment corresponding to the minimum included angle; and judging whether the distance is within a preset effective distance threshold range, and if so, determining the route section as a road network route capable of being fitted. The method has the advantages of simple operation process and less required resources, thereby having higher operation efficiency, always maintaining higher calculation precision to meet the calculation requirement, being capable of supporting stream processing calculation subsequently and having good expansibility. The disclosure also provides a road network matching device, an electronic device and a readable storage medium.
Description
Technical Field
The present disclosure relates to the field of vehicle driving, and in particular, to a road network matching method, apparatus, electronic device, and medium.
Background
Roads are important basic geographic elements in maps, and with the increasing popularization of 5G and big data technologies, the rapid updating of road network data has important significance and value for implementing vehicle navigation, responding to emergencies, even networking vehicles and the like.
The use of GPS devices for vehicle trajectory research is becoming more and more widely practiced. The road network mapping is the basis for developing the research of the motor vehicle track and the emission, and the main purpose of the road network mapping is to match the vehicle positioning data collected in the GPS with the existing road network data through a specific algorithm so as to accurately display the real track of the motor vehicle running, thereby playing the roles of positioning, tracking and auxiliary analysis and judgment. Therefore, obtaining effective movement track data and performing accurate movement track road matching are the basis for developing related research, which implies a great research value.
Currently, the proximity problem of a moving track is one of the hot problems in research, and at present, different ideas are given by a plurality of different methods, such as a route proximity algorithm based on euclidean distance, a track similarity method (namely, lcs) based on the longest common subsequence, a grid method and the like, and different schemes are provided for the proximity problem of the moving track through different angles. However, the above methods all have limitations in use, for example, the proximity algorithm based on the euclidean distance requires equal numbers of sampling points between the trajectories, which is difficult to achieve in actual operation; the LCSS does not effectively judge some drift track points, so that the result is not accurate enough; the difficulty of the grid method is that the division of the grid size is difficult to define, and therefore, great uncertainty and artificial interference are brought to the final result.
According to the method, a professional data acquisition person needs to be hired, and a professional vehicle, a positioning device and the like are used for measuring the road and the vehicle running track, so that the problems of long information acquisition period, large post-calculation amount, high equipment maintenance cost and the like exist. Some methods for matching the motor vehicle road network by using means such as GPS/GNSS, etc. increase the accuracy, but result in the increase of geometric progression of calculated amount, thereby failing to realize real-time matching operation, greatly limiting the practicability and application range of the method in actual operation, and sacrificing the timeliness and efficiency.
Disclosure of Invention
Technical problem to be solved
In view of the above technical problems, the present disclosure provides a road network matching method, device, electronic device and medium, which are used to at least partially solve one of the above technical problems.
(II) technical scheme
A first aspect of the present disclosure provides a road network matching method, including: recording a first position point and a second position point of the target vehicle; acquiring all route segments in a preset area with the first position point or the second position point as a central point; respectively calculating an included angle between a connecting line of the first position point and the second position point and each route segment, and determining the route segment corresponding to the minimum included angle; calculating the distance from the central point to the route section corresponding to the minimum included angle; and judging whether the distance is within a preset effective distance threshold range, and if so, determining the route section as a road network route capable of being fitted.
Optionally, the recording the first location point and the second location point of the target vehicle comprises: and recording the longitude and latitude coordinates of the target vehicle at the first moment as a first position point, and recording the longitude and latitude coordinates of the target vehicle at the second moment as a second position point, wherein the first moment and the second moment are different moments.
Optionally, the obtaining all route segments in the preset area with the first position point or the second position point as the center point includes: carrying out gridding processing on road network line segments by using a central point to obtain a first grid with a preset size; carrying out grid expansion on the basis of the first grid to obtain N grids which are closest to the first grid in the expanded grids; and acquiring IDs corresponding to the first grid and the N grids, matching the IDs with the IDs of all route network segments, and acquiring all the route segments contained in the first grid and the N grids.
Optionally, the Rtree method is adopted for grid expansion.
Optionally, the first location point and the second location point of the target vehicle are recorded using an onboard GPS device of the target vehicle.
Optionally, if the distance is not within the preset distance threshold range, returning to record the operation of the first position point and the second position point of the target vehicle, and performing road network matching again.
Optionally, the calculating an included angle between a connection line of the first position point and the second position point and each route segment respectively includes: and calculating the cosine value or sine value or tangent value or cotangent value of an included angle between the connecting line of the first position point and the second position point and each route segment.
A second aspect of the present disclosure provides a road network matching apparatus, including: the recording module is used for recording a first position point and a second position point of the target vehicle; the acquisition module is used for acquiring all route segments in a preset area with the first position point or the second position point as a central point; the first calculation module is used for calculating an included angle between a connecting line of the first position point and the second position point and each route segment respectively and determining the route segment corresponding to the minimum included angle; the second calculation module is used for calculating the distance from the central point to the route section corresponding to the minimum included angle; and the judging module is used for judging whether the distance is within the range of the preset effective distance threshold value, and if so, determining the route section as the road network route which can be fitted.
A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the above-described method.
A fourth aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions for implementing the above-described method when executed.
(III) advantageous effects
The road network matching method, the road network matching device, the electronic equipment and the medium have the advantages that:
1. the method directly adopts the vehicle track point connecting line and the road section included angle for judgment, has simple operation process, and has higher operation speed and higher operation efficiency compared with the prior method.
2. The method has lower demand on computing resources, and can always maintain higher computing precision so as to meet the computing requirement.
3. The method can support stream processing calculation subsequently, and has good expansibility. Due to the strict requirement of stream processing calculation on data timeliness, the conventional method needs to consume computing resources greatly because the operation steps are not optimized enough, so that the related technical requirement of stream processing calculation cannot be met. By adopting the method, the demand on computing resources can be greatly reduced, so that the effective support can be realized on the subsequent flow processing calculation, and a flow processing scheme for motor vehicle positioning, emission monitoring and supervision is constructed on the basis.
Drawings
FIG. 1 is a flow chart schematically illustrating a road network matching method according to an embodiment of the present disclosure;
fig. 2 schematically illustrates a track point trend determination method diagram provided by the embodiment of the present disclosure;
fig. 3 schematically illustrates a driving trajectory fast matching result diagram of a target vehicle provided by the embodiment of the disclosure;
fig. 4 schematically shows a block diagram of a road network matching apparatus according to an embodiment of the present disclosure;
fig. 5 schematically shows a block diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The embodiment of the disclosure provides a road network matching method and a road network matching device using the same. And acquiring all route segments in a preset area with the first position point or the second position point as a central point. And respectively calculating the included angle between the connecting line of the first position point and the second position point and each route segment, and determining the route segment corresponding to the minimum included angle. And calculating the distance from the central point to the route section corresponding to the minimum included angle. And judging whether the distance is within a preset distance threshold range, and if so, determining the route section as a road network capable of being fitted.
According to the method and the device, the closeness of the track point and the road is judged, namely the included angle between the connecting line of two adjacent track points and the extension line of the similar road segment is adopted to judge the goodness of fit between the track point and the road, so that the method has higher operation efficiency, the requirement on calculation resources is lower, and higher calculation precision can be maintained all the time to meet the calculation requirement. In addition, the method can support stream processing calculation subsequently, and has good expansibility.
Fig. 1 schematically shows a flowchart of a road network matching method according to an embodiment of the present disclosure.
As shown in fig. 1, the method may include, for example, operations S101 to S105.
In operation S101, a first location point and a second location point of a target vehicle are recorded.
In the embodiment of the disclosure, for example, the vehicle-mounted GPS device of the target vehicle may be used for position acquisition, and the directly acquired GPS data may be subjected to data cleaning. Specifically, longitude and latitude coordinates of the target vehicle are recorded at a first moment as a first position point, and longitude and latitude coordinates of the target vehicle are recorded at a second moment as a second position point. The first time and the second time are different times. For example, the ith location point for target vehicle A is recorded as a set P of coordinates and time stampsi(LongAi，LatAi，TAi). Similarly, the j-th position point of the target vehicle A is recorded as Pj(LongAj，LatAj，TAj) Wherein LongAi、LatAiAnd TAiRespectively, longitude, latitude, and location time, where i ≠ j.
In operation S102, all route segments within a preset region where the first position point or the second position point is the center point are obtained.
In the embodiment of the present disclosure, the first position point may be used as a center point, and the second position point may also be used as a center point, which may be specifically set according to a requirement of an environment where the target vehicle is actually located, and the present disclosure is not limited specifically. After the center point is selected, the following operations are carried out:
firstly, with the central point as a center, the road network contained in the acquired road network data is subjected to grid processing to obtain a first grid with a preset size. For example, in latitude and longitude coordinates, the grid size may be set to 0.004476 °
Then, based on the first grid, the grids are expanded, the N grids which are closest to the first grid in the expanded grids are obtained, and IDs corresponding to the first grid and the N grids are obtained, wherein the IDs refer to grid numbers and are used for distinguishing different grids, and the ID numbers of different grids correspond to specific geographic positions. The grid expansion can be performed by adopting an Rtree method, and N grids and IDs (identity) which are most adjacent to the first grid in the expanded grids are obtained by adopting a Nearest neighbor Neighbors method.
And finally, matching the IDs with the IDs of all the route network segments to obtain the first grid and all the route segments contained in the N grids. Because each road network line segment in the road network data has a corresponding ID, the road network line segment is stored in a dictionary mode generally, and therefore, the road network data can be directly storedAnd acquiring the dictionaries corresponding to the IDs to obtain the IDs corresponding to all the road network line segments. All the acquired route segments can be recorded as L ═ L1，l2…ln}。
In operation S103, an included angle between each route segment and a connection line between the first location point and the second location point is calculated, and a route segment corresponding to the minimum included angle is determined.
In the embodiment of the present disclosure, as shown in fig. 2, the first position point (corresponding track point a) and the second position point (corresponding track point B) may be connected to form a straight line AB, and an included angle α between the straight line AB and the route segment L is calculated. The cosine value cos α of the included angle between the straight line AB and the route segment L, the sine value sin α of the included angle between the line AB and the route segment L, the tangent value, the cosine value, and the like of the included angle can be calculated, and the disclosure is not limited specifically. For example, when calculating the cosine value cos α of the included angle between the straight line AB and the route segment L, min (1-cos α) of the cosine values of the included angles between all the route segments and the straight line AB is calculated, and the corresponding road network can be marked as 1closest. When calculating the sine value sin alpha of the included angle between the straight line AB and the route segment L, min (sin alpha) in the sine values of the included angles between all the route segments and the straight line AB needs to be calculated, and the corresponding road network can be recorded as Lclosest. Wherein, the value of alpha is [0, pi/2]。
In operation S104, a distance from the center point to the route segment corresponding to the minimum included angle is calculated.
In the embodiment of the present disclosure, a geop and distance () method may be adopted to obtain the center point (first position point Pi) to the closest route segment lclosestDistance D ofclosest。
In operation S105, it is determined whether the distance is within the preset distance threshold range, and if so, operation S106 is performed.
In the embodiment of the present disclosure, the preset effective distance threshold may be set according to an actual requirement, for example, may be set to 0.005 °. If the distance DclosestIf the distance is less than the preset effective distance threshold value, the corresponding route segment l is divided intoclosestDetermined as fittable to road network lines if distance DclosestIf the distance is greater than or equal to the preset effective distance threshold, the first position point and the second position point recorded at present are set, the operation returns to operation S101,operations S101 to S105 are re-executed.
And S106, determining the route segment as a road network which can be fitted.
To clarify the first disclosure, a specific example is further described below.
Specifically, the 1 st position point of the target vehicle a is recorded as a set P of coordinates and time stamps1(116.4253, 39.9584, 2314 s); similarly, the 2 nd position point for the target vehicle A is recorded as P2(116.4358，39.9684，2319s)。
And (3) taking the 1 st position point as a center, carrying out gridding processing on the road network, wherein the grid size is 0.004476 degrees under longitude and latitude coordinates.
Based on the grid, the grid is expanded by an Rtree method, and near eight grid matrixes and IDs of the grid matrixes are obtained by using a Nearest Neighbors method. Obtaining all route segments in the nine grid matrixes by dictionary matching of the grid matrix ID and the route segment ID, and setting a set consisting of all roads as L ═ L1，l2…l9}。
Calculating the line segment P1P2And each network L in the L setnThe cosine value Cos alpha is calculated to obtain min (1-Cos alpha), and the corresponding road network is lclosest；
Obtain the position point Pi to the nearest road section l using the geopandas distance () methodclosestDistance D ofclosest＝0.000152°。
Due to the distance DclosestWithin the effective distance threshold range (0.005 °), so that the road segment is a road network which can be fitted, the fitting is successful, and the result of the quick matching on the driving track of the target vehicle a is shown in fig. 3.
In summary, since the method only needs to obtain the first location point and the second location point of the target vehicle and perform the proximity determination on the route segment in the connection line network data of the first location point and the second location point, the calculation process is simple, the required resources are few, and therefore, the method has higher calculation efficiency, can always maintain higher calculation precision to meet the calculation requirement, can support the flow processing calculation subsequently, and has good expansibility.
Fig. 4 schematically shows a block diagram of a road network matching apparatus according to an embodiment of the present disclosure. The device can execute the road network matching method.
As shown in fig. 4, the road network matching device 400 according to the embodiment of the present disclosure may include, for example, a recording module 410, an obtaining module 420, a first calculating module 430, a second calculating module 440, and a determining module 450.
The recording module 410 is configured to record a first location point and a second location point of a target vehicle.
An obtaining module 420, configured to obtain all route segments in a preset area with the first location point or the second location point as a central point;
the first calculating module 430 is configured to calculate an included angle between a connection line of the first position point and the second position point and each route segment, and determine a route segment corresponding to the minimum included angle.
The second calculating module 440 is configured to calculate a distance from the central point to the route segment corresponding to the minimum included angle.
The determining module 450 is configured to determine whether the distance is within a preset effective distance threshold range, and if so, determine the road segment as a road network that can be fitted.
It should be noted that the embodiments of the apparatus portion and the method portion are similar to each other, and the achieved technical effects are also similar to each other, which are not described herein again.
Any of the modules according to embodiments of the present disclosure, or at least part of the functionality of any of them, may be implemented in one module. Any one or more of the modules according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules according to the embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging the circuit, or in any one of three implementations, or in any suitable combination of any of the software, hardware, and firmware. Alternatively, one or more of the modules according to embodiments of the disclosure may be implemented at least partly as computer program modules which, when executed, may perform corresponding functions.
For example, any plurality of the recording module 410, the obtaining module 420, the first calculating module 430, the second calculating module 440, and the determining module 450 may be combined into one module to be implemented, or any one of the modules may be divided into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the recording module 410, the obtaining module 420, the first calculating module 430, the second calculating module 440, and the determining module 450 may be at least partially implemented as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or implemented by any one of three implementation manners of software, hardware, and firmware, or an appropriate combination of any several of them. Alternatively, at least one of the recording module 410, the obtaining module 420, the first calculating module 430, the second calculating module 440 and the determining module 450 may be at least partially implemented as a computer program module, and when the computer program module is executed, the corresponding function may be executed.
Fig. 5 schematically shows a block diagram of an electronic device according to an embodiment of the disclosure. The electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 5, the electronic device 500 includes a processor 510, a computer-readable storage medium 520. The electronic device 500 may perform a method according to an embodiment of the present disclosure.
In particular, processor 510 may include, for example, a general purpose microprocessor, an instruction set processor and/or related chip set and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), and/or the like. The processor 510 may also include on-board memory for caching purposes. Processor 510 may be a single processing unit or a plurality of processing units for performing different actions of a method flow according to embodiments of the disclosure.
Computer-readable storage media 520, for example, may be non-volatile computer-readable storage media, specific examples including, but not limited to: magnetic storage systems, such as magnetic tape or Hard Disk Drives (HDDs); optical storage systems, such as compact discs (CD-ROMs); memory such as Random Access Memory (RAM) or flash memory, etc.
The computer-readable storage medium 520 may include a computer program 521, which computer program 521 may include code/computer-executable instructions that, when executed by the processor 510, cause the processor 510 to perform a method according to an embodiment of the disclosure, or any variation thereof.
The computer program 521 may be configured with, for example, computer program code comprising computer program modules. For example, in an example embodiment, code in computer program 521 may include one or more program modules, including for example 521A, modules 521B, … …. It should be noted that the division and number of modules are not fixed, and those skilled in the art may use suitable program modules or program module combinations according to actual situations, and when these program modules are executed by the processor 510, the processor 510 may execute the method according to the embodiment of the present disclosure or any variation thereof.
The present disclosure also provides a computer-readable storage medium, which may be included in the device/system described in the above embodiments, or may exist separately without being assembled into the device/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It will be understood by those skilled in the art that while the present disclosure has been shown and described with reference to certain exemplary embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present disclosure as defined by the appended claims and their equivalents. Accordingly, the scope of the present disclosure should not be limited to the above-described embodiments, but should be defined not only by the appended claims, but also by equivalents thereof.
The above-mentioned embodiments are intended to illustrate the objects, aspects and advantages of the present disclosure in further detail, and it should be understood that the above-mentioned embodiments are only illustrative of the present disclosure and are not intended to limit the present disclosure, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present disclosure should be included in the scope of the present disclosure.
Claims (10)
1. A road network matching method comprises the following steps:
recording a first position point and a second position point of the target vehicle;
acquiring all route segments in a preset area with the first position point or the second position point as a central point;
respectively calculating an included angle between a connecting line of the first position point and the second position point and each route segment, and determining a route segment corresponding to the minimum included angle;
calculating the distance from the central point to the route segment corresponding to the minimum included angle;
and judging whether the distance is within a preset effective distance threshold range, and if so, determining the route section as a road network route capable of being fitted.
2. The road network matching method according to claim 1, wherein said recording the first location point and the second location point of the target vehicle comprises:
recording longitude and latitude coordinates of the target vehicle at a first moment as the first position point, and recording longitude and latitude coordinates of the target vehicle at a second moment as the second position point, wherein the first moment and the second moment are different moments.
3. The road network matching method according to claim 1, wherein said obtaining all route segments in a preset area with the first position point or the second position point as a central point comprises:
carrying out gridding processing on road network line segments by using the central points to obtain a first grid with a preset size;
carrying out grid expansion on the basis of the first grid to obtain N grids which are closest to the first grid in the expanded grids;
and acquiring IDs corresponding to the first grid and the N grids, matching the IDs with the IDs of all route network segments, and acquiring all the route segments contained in the first grid and the N grids.
4. The road network matching method according to claim 3, wherein Rtree method is adopted for grid expansion.
5. The road network matching method according to claim 1, wherein a first location point and a second location point of a target vehicle are recorded using an on-board GPS device of the target vehicle.
6. The road network matching method according to claim 1, wherein if said distance is not within said preset distance threshold, returning to the operation of said first and second location points of said recording target vehicle, and performing road network matching again.
7. The road network matching method according to claim 1, wherein said calculating an angle between each route segment and a connection line between said first location point and said second location point respectively comprises:
and calculating the cosine value or sine value or tangent value or cosine value of an included angle between the connecting line of the first position point and the second position point and each route segment.
8. A road network matching device, comprising:
the recording module is used for recording a first position point and a second position point of the target vehicle;
the acquisition module is used for acquiring all route segments in a preset area with the first position point or the second position point as a central point;
the first calculation module is used for calculating an included angle between a connecting line of the first position point and the second position point and each route segment respectively and determining a route segment corresponding to the minimum included angle;
the second calculation module is used for calculating the distance from the central point to the route segment corresponding to the minimum included angle;
and the judging module is used for judging whether the distance is within a preset effective distance threshold range, and if so, determining the route section as a road network route capable of being fitted.
9. An electronic device, comprising:
one or more processors;
a memory for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-7.
10. A computer-readable storage medium storing computer-executable instructions for implementing the method of any one of claims 1 to 7 when executed.
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| CN113970333A (en) * | 2021-09-26 | 2022-01-25 | 深圳市跨越新科技有限公司 | Adaptive candidate road searching method, system, terminal device and storage medium |
| CN114722948A (en) * | 2022-04-13 | 2022-07-08 | 北京中软政通信息技术有限公司 | Vehicle driving data fusion method, device, equipment and medium |
| CN117419732A (en) * | 2023-10-10 | 2024-01-19 | 中国船舶集团有限公司第七〇九研究所 | Road network-based perception target positioning deviation rectifying method, equipment and storage medium |
| CN113970333B (en) * | 2021-09-26 | 2024-05-28 | 深圳市跨越新科技有限公司 | Self-adaptive candidate road searching method, system, terminal equipment and storage medium |
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