CN116383675A - Road network matching method and device for vehicle position data - Google Patents
Road network matching method and device for vehicle position data Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
- H03H21/00—Adaptive networks
- H03H21/0012—Digital adaptive filters
- H03H21/0043—Adaptive algorithms
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The application belongs to the field of computers, and aims to solve the problem of inaccurate road network matching, and the embodiment of the application discloses a road network matching method and device for vehicle position data, wherein the road network matching method for the vehicle position data comprises the following steps: acquiring a three-dimensional space position of a vehicle and a corresponding vehicle speed; determining an alternative road section of the vehicle at the three-dimensional space position; calculating likelihood values of vehicles on each alternative road section by using an adaptive filter based on the three-dimensional space position and Euclidean distance of the alternative road section, wherein the vehicle speed is used as a parameter of the adaptive filter; and determining the road section where the vehicle is located from the alternative road sections according to the likelihood value. Road network matching of vehicle position data is more accurate.
Description
Technical Field
The application relates to the technical field of computers, in particular to a road network matching method and device for vehicle position data.
Background
Determining a running track of a vehicle in a road network, generally periodically positioning the vehicle by adopting a vehicle-mounted global positioning device, obtaining information such as longitude and latitude, head direction and the like of the vehicle, calculating Euclidean distance between a positioning point and an alternative road section according to information such as distance between the longitude and latitude position and the alternative road section, and included angle between the head direction and the road, and matching the positioning point with the road section closest to the positioning point.
When the same position is provided with multiple layers of roads, it is difficult to distinguish which layer the vehicle is positioned on according to longitude and latitude information, for example, a large-scale overpass with a complex structure is used, and erroneous judgment of the road to which the vehicle belongs easily occurs in the driving process of the overpass. Meanwhile, the positioning error of the vehicle changes along with the speed, and the error is amplified when the vehicle speed is high. Particularly, due to the influence of satellite angles, the altitude error in global positioning is larger than the longitude and latitude error, and road section matching errors are easy to occur when a vehicle runs on a complex overpass or viaduct.
Disclosure of Invention
The embodiment of the application provides a road network matching method and device for vehicle position data, which can improve the accuracy of road network matching of the vehicle position data.
In a first aspect, an embodiment of the present application provides a road network matching method for vehicle location data, including:
acquiring a three-dimensional space position of a vehicle and a corresponding vehicle speed;
determining an alternative road section of the vehicle at the three-dimensional space position;
calculating likelihood values of vehicles on each alternative road section by using an adaptive filter based on the three-dimensional space position and Euclidean distance of the alternative road section, wherein the vehicle speed is used as a parameter of the adaptive filter;
and determining the road section where the vehicle is located from the alternative road sections according to the likelihood value.
In a second aspect, an embodiment of the present application provides a road network matching device for vehicle location data, including:
the positioning module is used for acquiring the three-dimensional space position of the vehicle and the corresponding vehicle speed;
a map module for determining an alternative road segment of the vehicle in the three-dimensional space position;
a calculation module for calculating likelihood values of vehicles located on the alternative road segments by using an adaptive filter based on the three-dimensional space position and the Euclidean distance of the alternative road segments, wherein the vehicle speed is used as a parameter of the adaptive filter;
and the matching module is used for determining the road section where the vehicle is located from the alternative road sections according to the likelihood value.
In a third aspect, embodiments of the present application provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as described in any of the preceding claims.
In a fourth aspect, embodiments of the present application provide an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the method of any one of the above when executing the computer program.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 shows a flow chart of a road network matching method of vehicle position data according to an embodiment of the present application;
fig. 2 is a schematic view of a scenario in which a road network matching method for vehicle position data according to an embodiment of the present application is specifically applied;
fig. 3 shows a schematic structural diagram of a road network matching device of vehicle position data according to an embodiment of the present application;
fig. 4 shows a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to better understand the technical solutions of the present application, the following description will clearly and completely describe the technical solutions in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
The terms first, second and the like in the description and in the claims of the present application and in the above-described figures, are used for distinguishing between different objects and not for describing a particular sequential order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
Referring to fig. 1, an embodiment of the present application provides a road network matching method for vehicle location data, including:
acquiring a three-dimensional space position of a vehicle and a corresponding vehicle speed;
determining an alternative road section of the vehicle in the three-dimensional space position;
calculating likelihood values of vehicles on each alternative road section by using an adaptive filter based on the three-dimensional space position and Euclidean distance of the alternative road section, wherein the vehicle speed is used as a parameter of the adaptive filter;
and determining the road section where the vehicle is located from the alternative road sections according to the likelihood value.
According to the road network matching method for the vehicle position data, the three-dimensional space position of the vehicle is obtained, longitude and latitude information of the vehicle is used, altitude information of the vehicle is used, speed information of the vehicle is also obtained, euclidean distance between the vehicle and an alternative road section can be determined based on the three-dimensional space position of the vehicle, likelihood values of the vehicle on each alternative road section are calculated by using self-adaptive chlorine, the road section of the vehicle is determined according to the likelihood values, when the likelihood values are calculated, the speed of the vehicle is used as a parameter of a self-adaptive filter, and accuracy of matching of vehicle positioning data road sections in special scenes such as complex overpasses, overpasses and ramp roads with higher vehicle speeds can be improved.
In this embodiment, the three-dimensional space position of the vehicle and the corresponding vehicle speed may be obtained through a positioning device, and the positioning device may be a satellite positioning device such as a GPS, a beidou, and the like. The three-dimensional space position of the vehicle and the corresponding vehicle speed can be calculated byThe set time interval is acquired, and each acquired three-dimensional space position and corresponding vehicle speed can correspond to the acquired time. Three-dimensional spatial position of vehicle
， />
Indicating the%>
The position in three-dimensional space of the device,
representing the longitude and latitude and altitude of the location, respectively. The real-time vehicle speed of the vehicle at the three-dimensional space position is
。
In the embodiment of the application, the alternative road section can be determined according to the three-dimensional space position and the map of the vehicle. The three-dimensional space position may be matched with the map, and a road section conforming to the spatial relationship of the three-dimensional space position may be used as the candidate road section. The conditions described above may include, for example, a distance, i.e., a distance between a road segment in the map and a three-dimensional space position is within a set range, i.e., an alternative road segment. The determination of the alternative road segments may also be combined with the head direction of the vehicle. And taking a road section with the distance between the road section and the three-dimensional space position in the map within a set range and the included angle between the road section and the direction of the vehicle head being smaller than a preset angle as an alternative road section. Of course, the determination of the alternative road segments may also be performed by known schemes, or by other suitable methods, which will not be described in detail herein.
In this embodiment of the present application, the map may be a GIS (geographic information system) map, and the positioning device may determine the candidate road section in combination with the GIS map.
In some embodiments, euclidean distance
Obtained by the following formula (1):
In some embodiments, the vehicle is located in an alternative route
Likelihood value +.>
From the bottomThe formula (2) is obtained:
In some embodiments, the constraint of likelihood value calculation formula (2) is: the sum of weights of the candidate road-segment likelihood values calculated at three-dimensional spatial positions within the window width of the filter is equal to 1. Specifically, the following formula (3):
in some embodiments, the weight when the three-dimensional space position is used to calculate the candidate road likelihood value is determined by the real-time positioning variance corresponding to the three-dimensional space position, and specifically, the weight is obtained by the following formula (4):
in the formula ,
is->
Weight when three-dimensional space position is used for calculating alternative road section likelihood value,/>
Is->
The vehicle corresponding to the three-dimensional space position locates variance in real time.
The real-time positioning variance of the vehicle is determined by the stationary positioning variance of the positioning device and the vehicle speed. Taking GPS positioning as an example, time consuming for each global positioning is set
The positioning variance due to the moving speed of the vehicle is +.>
. The variance of the sum of two independent normal distributions is the sum of the two variances.
In some embodiments, the vehicle real-time positioning variance is determined by a stationary positioning variance of the positioning device and the vehicle speed, the vehicle real-time positioning variance being obtained by the following equation (5):
wherein ,
is->
Real-time positioning variance of vehicle corresponding to three-dimensional space position, < >>
For the stationary positioning variance of the positioning device, +.>
Is->
Vehicle speed in three-dimensional space position +.>
Time consuming determination of the vehicle position for each positioning device.
In the specific implementation, the first acquired at a certain moment
The judging of the road section where the three-dimensional space positions are located can comprise the following steps:
acquisition of
To->
Co (all ]>
Three-dimensional space positions; calculate +.>
Will->
Substituting formula (4) to calculate +.>
Further, the likelihood value +_for each road segment is calculated according to equation (2)>
The method comprises the steps of carrying out a first treatment on the surface of the Comparison of
，/>
Select->
Maximum value +.>
The value is used as a decision result. I.e. < th->
Road section is made->
Maximum, then decision is at +.>
In the case of a three-dimensional spatial position, the vehicle is located at +.>
Alternative road segments are striped.
Application scenario
Referring to fig. 2, the vehicle travels on a double-deck overpass,
the upper layer road corresponds to->
The lower road corresponds to->
The distance between two layers of roads is 7 meters, the vehicle runs on the upper layer, and the positioning device loaded on the vehicle is a GPS positioning device.
Three-dimensional spatial positions of the vehicle are continuously acquired by positioning means, i.e.
Three-dimensional space positions are respectively +.>
，/>
，/>
Three dimensional spatial locations are shown as five-pointed stars in the figure. Obtaining three-dimensional space positions and Euclidean distance of the upper road according to the formula (1) as +.>
m，/>
m，/>
m, three-dimensional space positions and the position of the lower road are respectively +.>
m，/>
m，/>
m; the vehicle speeds corresponding to the three-dimensional space positions are +.>
，/>
，/>
。
Static positioning variance of positioning device
Time for processing data by positioning device each time
。
From equation (5):
，/>
，/>
。
from equation (4):
，/>
，/>
。
from equation (2):
，/>
。
the likelihood values of the two candidate road segments are compared,
therefore, determine->
Three-dimensional spatial position->
Is positioned on the upper road.
And if so, judging according to the three-dimensional space position and Euclidean distance between the two candidate road sections. Due to
Will be->
Three-dimensional spatial position->
Misjudgment is that the road is positioned on the lower layer. The method can improve the accuracy of road network matching of the vehicle running track and the vehicle position data in the high-speed running scene of multi-layer roads such as overpasses and overhead roads.
The embodiment of the application provides a road network matching device for vehicle position data, which can realize the method of the embodiment, the embodiment of the method can be used for understanding the device of the embodiment of the application, and the description part of the embodiment of the device can also be used for understanding the method of the embodiment.
Referring to fig. 3, the road network matching device for vehicle position data in the embodiment of the present application includes a positioning module, a map module, a calculation module and a matching module, where the positioning module is configured to obtain a three-dimensional space position of a vehicle and a corresponding vehicle speed; the map module is used for determining an alternative road section of the vehicle in the three-dimensional space position; the calculation module is used for calculating likelihood values of the vehicle on each alternative road section by using the adaptive filter based on the three-dimensional space position and the Euclidean distance of the alternative road section, and the vehicle speed is used as a parameter of the adaptive filter; the matching module is used for determining the road section where the vehicle is located from the alternative road sections according to the likelihood value.
In some embodiments, the calculation module calculates the Euclidean distance
Obtained by the following formula:
In some embodiments, the calculation module calculates that the vehicle is located in an alternative road segment
Likelihood value +.>
Obtained by the following formula (2):
In some embodiments, the constraint of likelihood value calculation formula (2) is: the sum of weights of the candidate road-segment likelihood values calculated at three-dimensional spatial positions within the window width of the filter is equal to 1. Specifically, the following formula (3):
in some embodiments, the weight when the three-dimensional space position is used to calculate the candidate road likelihood value is determined by the real-time positioning variance corresponding to the three-dimensional space position, and specifically, the weight is obtained by the following formula (4):
in the formula ,
is->
Weight when three-dimensional space position is used for calculating alternative road section likelihood value,/>
Is->
The vehicle corresponding to the three-dimensional space position locates variance in real time.
In some embodiments, the vehicle real-time positioning variance is determined by a stationary positioning variance of the positioning device and the vehicle speed, the vehicle real-time positioning variance being obtained by the following equation (5):
wherein ,
is->
Real-time positioning variance of vehicle corresponding to three-dimensional space position, < >>
For the stationary positioning variance of the positioning device, +.>
Is->
Vehicle speed in three-dimensional space position +.>
Time consuming determination of the vehicle position for each positioning device.
An embodiment of the present application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing a method of any one of the above when executing the computer program.
Referring to fig. 4, a schematic structural diagram of an electronic device is provided in an embodiment of the present application. As shown in fig. 4, the terminal 600 may include: at least one processor 601, at least one network interface 604, a user interface 603, a memory 605, at least one communication bus 602.
Wherein the communication bus 602 is used to enable connected communications between these components.
The user interface 603 may include a Display screen (Display), a Camera (Camera), and the optional user interface 603 may further include a standard wired interface, a wireless interface.
The network interface 604 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
Wherein the processor 601 may include one or more processing cores. The processor 601 connects various parts within the overall terminal 600 using various interfaces and lines, performs various functions of the terminal 600 and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 605, and invoking data stored in the memory 605. Alternatively, the processor 601 may be implemented in hardware in at least one of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (ProgrammableLogic Array, PLA). The processor 601 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 601 and may be implemented by a single chip.
The memory 605 may include a random access memory (Random Access Memory, RAM) or a Read-only memory (Read-only memory). Optionally, the memory 605 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). Memory 605 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 605 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, etc.; the storage data area may store data or the like referred to in the above respective method embodiments. The memory 605 may also optionally be at least one storage device located remotely from the processor 601. As shown in fig. 4, an operating system, a network communication module, a user interface module, and application programs may be included in the memory 605, which is one type of computer storage medium.
In the electronic device 600 shown in fig. 4, the user interface 603 is mainly used for providing an input interface for a user, and acquiring data input by the user; and processor 601 may be operative to invoke application programs stored in memory 605 and to perform in particular the operations of any of the method embodiments described above.
The present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the above method. The computer readable storage medium may include, among other things, any type of disk including floppy disks, optical disks, DVDs, CD-ROMs, micro-drives, and magneto-optical disks, ROM, RAM, EPROM, EEPROM, DRAM, VRAM, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of media or device suitable for storing instructions and/or data.
The present application also provides a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform part or all of the steps of any one of the methods described in the method embodiments above.
It will be clear to a person skilled in the art that the solution of the present application may be implemented by means of software and/or hardware. "Unit" and "module" in this specification refer to software and/or hardware capable of performing a particular function, either alone or in combination with other components, such as Field programmable gate arrays (Field-ProgrammaBLE Gate Array, FPGAs), integrated circuits (IntegratedCircuit, IC), and the like.
It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required in the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, such as a division of units, merely a division of logic functions, and there may be additional divisions in actual implementation, such as multiple units or components may be combined or 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, or may be in electrical or other forms.
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 each embodiment of the present application 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 integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, including several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned memory includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be implemented by a program that instructs associated hardware, and the program may be stored in a computer readable memory, which may include: flash disk, read-Only Memory (ROM), random-access Memory (RandomAccess Memory, RAM), magnetic or optical disk, and the like.
The foregoing is merely exemplary embodiments of the present disclosure and is not intended to limit the scope of the present disclosure. That is, equivalent changes and modifications are contemplated by the teachings of this disclosure, which fall within the scope of the present disclosure. Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a scope and spirit of the disclosure being indicated by the claims.
Claims (10)
1. A road network matching method for vehicle position data, comprising:
acquiring a three-dimensional space position of a vehicle and a corresponding vehicle speed;
determining an alternative road section of the vehicle at the three-dimensional space position;
calculating likelihood values of vehicles on each alternative road section by using an adaptive filter based on the three-dimensional space position and Euclidean distance of the alternative road section, wherein the vehicle speed is used as a parameter of the adaptive filter;
and determining the road section where the vehicle is located from the alternative road sections according to the likelihood value.
2. The method of claim 1, wherein the euclidean distance is obtained by the following formula:
3. The method of claim 1, wherein the likelihood of the vehicle being located on the alternative road segment
The method is obtained by the following formula:
4. A method according to claim 3, wherein the constraints of the likelihood value calculation formula are as follows:
5. the method according to claim 4, wherein the three-dimensional space position is used for calculating a weight when the candidate road-segment likelihood value is calculated, and the weight is determined by a real-time positioning variance of the vehicle corresponding to the three-dimensional space position, and the formula is as follows:
6. The method of claim 5, wherein the vehicle real-time positioning variance is determined from a stationary positioning variance of the positioning device and a vehicle speed, as follows:
wherein ,
is->
Real-time positioning variance of vehicle corresponding to three-dimensional space position, < >>
For the stationary positioning variance of the positioning device, +.>
For the vehicle to be located at->
Speed at three-dimensional spatial position +.>
Time consuming determination of the vehicle position for each positioning device.
7. A road network matching apparatus for vehicle position data, comprising:
the positioning module is used for acquiring the three-dimensional space position of the vehicle and the corresponding vehicle speed;
a map module for determining an alternative road segment of the vehicle in the three-dimensional space position;
a calculation module for calculating likelihood values of vehicles located on the alternative road segments by using an adaptive filter based on the three-dimensional space position and the Euclidean distance of the alternative road segments, wherein the vehicle speed is used as a parameter of the adaptive filter;
and the matching module is used for determining the road section where the vehicle is located from the alternative road sections according to the likelihood value.
8. The apparatus of claim 7, wherein the euclidean distance
Obtained by the following formula:
9. A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any of claims 1-6.
10. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the method of any one of claims 1-6 when the computer program is executed.
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| CN115877343A (en) * | 2023-02-02 | 2023-03-31 | 中电信数字城市科技有限公司 | Man-vehicle matching method and device based on radar target tracking and electronic equipment |
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