CN112351384B

CN112351384B - Vehicle positioning data correction method, device and equipment

Info

Publication number: CN112351384B
Application number: CN202010944930.8A
Authority: CN
Inventors: 胡兴航
Original assignee: Xiaolinggou Travel Technology Co ltd; Zhejiang Geely Holding Group Co Ltd
Current assignee: Xiaolinggou Travel Technology Co ltd; Zhejiang Geely Holding Group Co Ltd
Priority date: 2020-09-10
Filing date: 2020-09-10
Publication date: 2022-09-27
Anticipated expiration: 2040-09-10
Also published as: CN112351384A

Abstract

The invention relates to a method, a device and equipment for correcting vehicle positioning data, wherein the method comprises the following steps: acquiring vehicle running data in the Internet of vehicles platform; generating a vehicle running track path according to the vehicle running data; constructing a road map containing standard positioning data according to a vehicle running track path; acquiring real-time positioning data of a target vehicle; and correcting the real-time positioning data of the target vehicle according to the road map to obtain accurate positioning data, and updating the real-time positioning data of the target vehicle. According to the invention, through learning of vehicle driving big data, standard positioning data can be added to a road map according to a driving track, and the real-time positioning data of the vehicle can be corrected by taking the standard positioning data as a standard, so that the positioning data of the vehicle can be corrected in an area with poor GPS signal or easy interference.

Description

Vehicle positioning data correction method, device and equipment

Technical Field

The invention relates to the technical field of vehicle positioning, in particular to a method, a device and equipment for correcting vehicle positioning data.

Background

In the GPS (Global positioning System) measurement, information transmitted from satellites is received by a bottom surface receiving device, distances between the bottom surface receiving device and a plurality of satellites at the same time are calculated, and a spatial distance back-intersection method is used to determine three-dimensional coordinates of ground points. Thus, GPS satellites, satellite signal propagation processes, and terrestrial receiving equipment all contribute errors to GPS measurements.

In the process of positioning the vehicle, because the vehicle is in a fast moving state, the shading and the signal of a tall building are too weak or too strong, which can cause errors of positioning data, and in a serious way, the data can be interrupted or wholly deviated.

In the prior art, a large amount of calculation is generally needed to directly obtain accurate and accurate vehicle positioning data, and the requirement on network signals is high; the correction of the positioning data with large error is generally performed by directly performing inertial operation from the vehicle end to correct the positioning data, and the instantaneous operation amount is large and the common characteristic of data correction cannot be obtained.

Disclosure of Invention

The invention aims to solve the technical problem that the positioning data of a vehicle is corrected by utilizing the internet of vehicles data to learn the characteristics of the positioning data under the condition that the GPS positioning data has errors.

In order to solve the technical problem, the invention discloses a method, a device and equipment for correcting vehicle positioning data. The specific technical scheme is as follows:

in a first aspect, the invention discloses a vehicle positioning data correction method, which comprises the following steps:

acquiring vehicle running data in the Internet of vehicles platform;

generating a vehicle running track path according to the vehicle running data;

constructing a road map containing standard positioning data according to the vehicle running track path;

acquiring real-time positioning data of a target vehicle;

and correcting the real-time positioning data of the target vehicle according to the road map to obtain accurate positioning data, and updating the real-time positioning data of the target vehicle.

Further, before the acquiring the vehicle driving data in the vehicle networking platform, the method further comprises:

acquiring real-time driving data sent by vehicle sensing equipment based on a vehicle bus CAN protocol or a vehicle-mounted terminal protocol;

adding timestamp information to the real-time driving data, and preprocessing the data to obtain vehicle driving data conforming to a preset format;

when the network communication quality does not meet the uploading requirement, caching the vehicle driving data in a local memory of the vehicle, and trying to upload the vehicle driving data to the Internet of vehicles platform again when the network communication quality meets the uploading requirement;

and/or the vehicle running data and the uploading instruction are sent to nearby vehicles through short-range communication between the vehicles, and the nearby vehicles upload the vehicle running data to the Internet of vehicles platform.

Further, the generating a vehicle travel track path according to the vehicle travel data includes:

generating at least one raster path based on a shortest path algorithm according to positioning data, speed data, angle data and/or steering data in the vehicle driving data;

and screening the at least one raster path based on a correlation analysis algorithm, and determining a vehicle driving track path and frequent recording points.

Further, the generating at least one raster path based on a shortest path algorithm according to the positioning data, the speed data, the angle data and/or the steering data in the vehicle driving data comprises:

establishing a double grid network;

decomposing the positioning data coordinates into the double grid network to obtain grid dots and grid dot coordinates;

and connecting the grid mesh points into at least one grid path according to the basic moving method and the shortest path algorithm of the double grid network.

Further, the screening the at least one raster path based on the association analysis algorithm to determine a vehicle driving track path and frequent recording points includes:

generating a path frequent pattern tree according to the at least one raster path;

obtaining a frequent item set and the support degree of the at least one raster path according to the path frequent pattern tree;

cleaning the raster path based on a preset frequent threshold value to obtain a high-frequency raster path;

calculating a frequency value of each of the high-frequency raster paths, and determining a vehicle driving track path according to the frequency value;

and taking each grid point in the vehicle driving track path as a frequent recording point, and recording and storing the coordinate data of the frequent recording point.

Further, the constructing the road map containing standard positioning data according to the vehicle running track path comprises the following steps:

matching the vehicle running track path with actual road map information to obtain a road map, wherein the road map comprises standard positioning data of a plurality of frequent recording points;

and/or performing networking calculation again according to the vehicle running track path and the frequent recording points to obtain a road map with path direction attributes;

and/or updating the road map according to road change information issued by the Internet of vehicles platform or the external platform.

Further, the acquiring real-time positioning data of the target vehicle comprises:

acquiring satellite real-time positioning data of a target vehicle;

and/or acquiring the real-time positioning data of the surrounding vehicle through short-range communication with the surrounding vehicle and calculating to obtain the real-time positioning data of the target vehicle.

Further, the correcting the real-time positioning data of the target vehicle according to the road map to obtain the accurate positioning data includes:

determining a positioning point and the position of the positioning point in the road map according to the real-time positioning data of the target vehicle;

determining an overall road route traveled by the target vehicle based on the moving track direction of the target vehicle;

selecting a frequent recording point with the nearest distance as a corrected positioning point by calculating the distance from the positioning point to the frequent recording point in the whole road line, and using the coordinate data of the frequent recording point as the corrected accurate positioning data of the target vehicle;

and/or determining a historical positioning point and the position of the historical positioning point in the road map according to the historical positioning data of the target vehicle;

and calculating a real-time positioning point of the target vehicle in the road map according to the historical positioning point and the vehicle driving data of the target vehicle, and taking the coordinate data of the real-time positioning point in the road map as the accurate positioning data of the target vehicle.

Further, the obtaining the accurate positioning data and updating the real-time positioning data of the target vehicle includes:

updating the positioning data in the vehicle-mounted system of the target vehicle according to the accurate positioning data;

and/or uploading the accurate positioning data to a vehicle networking platform, performing iterative computation, and updating the road map or providing the road map for other positioning applications.

In a second aspect, the present invention also discloses a vehicle positioning data correcting device, which includes:

the first acquisition module is used for acquiring vehicle running data in the Internet of vehicles platform;

the track path module is used for generating a vehicle running track path according to the vehicle running data;

the road map module is used for constructing a road map containing standard positioning data according to the vehicle running track path;

the second acquisition module is used for acquiring real-time positioning data of the target vehicle;

and the correction updating module is used for correcting the real-time positioning data of the target vehicle according to the road map to obtain accurate positioning data and updating the real-time positioning data of the target vehicle.

In a third aspect, the present invention discloses a computer device, wherein the computer device comprises: a processor and a memory, wherein at least one instruction or at least one program is stored in the memory, and the at least one instruction or the at least one program is loaded by the processor and executed to implement a vehicle positioning data correcting method according to the first aspect of the invention.

In a fourth aspect, the present invention discloses a computer storage medium, wherein at least one instruction or at least one program is stored in the computer storage medium, and the at least one instruction or the at least one program is loaded and executed by the processor to implement a vehicle positioning data correction method according to the first aspect of the present invention.

By adopting the technical scheme, the method, the device and the equipment for correcting the vehicle positioning data have the following beneficial effects that: the method utilizes massive Internet of vehicles data to mine and learn the positioning data characteristics, can correct the positioning data of the vehicle with smaller calculation amount and ensure the continuity of the positioning data, can greatly fill the delay of the existing GPS system and the error of the positioning data under the condition of weaker signals, ensures the accuracy of a navigation system, and is convenient for traveling.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic flow chart of a method for correcting vehicle positioning data according to an embodiment of the present invention;

fig. 2 is a schematic diagram of a dual grid network according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of removing an abnormal anchor point according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of the moving direction in a dual grid network according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of a path supplement according to an embodiment of the present invention;

FIG. 6 is a schematic diagram of a continuous raster path that is obtained by embodiments of the present invention;

FIG. 7 is a schematic diagram illustrating the calculation of positioning data obtained through vehicle-to-vehicle communication according to an embodiment of the present invention;

fig. 8 is a schematic structural diagram of a vehicle positioning data correcting apparatus according to an embodiment of the present invention;

fig. 9 is a block diagram of a hardware configuration of a computer device that executes a vehicle positioning data correction method according to an embodiment of the present invention.

Detailed Description

In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic may be included in at least one implementation of the invention. In describing the present invention, it is to be understood that the terms "first," "second," "third," and "fourth," etc. in the description and claims of the present invention and the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements but may alternatively include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

First, key terms and abbreviations involved in the embodiments of the present disclosure are defined.

The Internet of vehicles: the vehicle-mounted equipment on the vehicle effectively utilizes all vehicle dynamic information in the information network platform through a wireless communication technology, and provides different functional services in the running process of the vehicle. The vehicle networking can provide guarantee for the distance between the vehicles, and the probability of collision accidents of the vehicles is reduced; the Internet of vehicles can help the vehicle owner to navigate in real time, and the efficiency of traffic operation is improved through communication with other vehicles and a network system.

Double grid: and the network mode with the supplementary network is used for making up the difference of the pure network.

The shortest path algorithm comprises the following steps: from a certain vertex, the path which passes along the edge of the graph to reach another vertex, and the path with the minimum sum of the weights on each edge is called the shortest path. The following algorithm is used to solve the shortest path problem: dijkstra algorithm, Bellman-Ford algorithm, Floyd algorithm, SPFA algorithm, etc. Among them, Dijkstra's algorithm is a shortest path algorithm from one vertex to the rest of vertices, and is commonly used to solve the shortest path problem in weighted graphs.

FP-Growth algorithm: the FP-Growth algorithm is a correlation analysis algorithm proposed by Hanjiawe et al in 2000, and adopts the following divide and conquer strategy: a database that provides a frequent itemset is compressed into a frequent pattern tree, but the itemset association information is retained. A data structure called a frequent pattern tree is used in the algorithm. The frequent pattern tree is a special prefix tree and is composed of a frequent item head table and an item prefix tree. The FP-Growth algorithm accelerates the whole excavation process based on the structure.

FP-Tree: the method comprises the steps that a Frequent Pattern Tree (frequency Pattern Tree) sorts all transaction data items in a transaction data table according to support degrees, the data items in each transaction are sequentially inserted into a Tree with NULL as a root node in a descending order, and the support degree of the node is recorded at each node.

Conditional mode base: conditional Pattern Base, contains the set of prefix paths in the FP-Tree that occur with the suffix Pattern.

Conditional frequent pattern tree: and forming a new frequent pattern tree by the conditional pattern base according to the construction principle of the frequent pattern tree.

Fig. 1 is a schematic flow chart of a vehicle positioning data correction method provided by an embodiment of the invention, and the present specification provides the operation steps of the method according to the embodiment or the schematic flow chart, but more or less operation steps can be included based on conventional or non-inventive labor. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of sequences, and does not represent a unique order of performance. In practice, the system or server product may be implemented in a sequential or parallel manner (e.g., parallel processor or multi-threaded environment) according to the embodiments or methods shown in the figures. Specifically, as shown in fig. 1, the method for correcting vehicle positioning data may include:

s100: and acquiring vehicle running data in the Internet of vehicles platform.

Further, before the step S100 provided in the embodiment of the present invention, the following steps are further included:

s110: and acquiring real-time running data sent by the vehicle sensing equipment based on a vehicle bus CAN protocol or a vehicle-mounted terminal protocol.

S120: and adding timestamp information to the real-time driving data, and preprocessing the data to obtain vehicle driving data conforming to a preset format.

Preferably, the vehicle driving data may include, but is not limited to: the vehicle comprises a frame number, a timestamp, longitude, latitude, a license plate, rotating speed, driving mileage, oil quantity and electric quantity.

Specifically, the preprocessing of the data may include comparing according to the vehicle frame number and the timestamp, removing duplicate data records, and ensuring uniqueness of the vehicle networking data records.

S130: when the network communication quality does not meet the uploading requirement, the vehicle driving data is cached in a local memory of the vehicle, and when the network communication quality meets the uploading requirement, the vehicle driving data is tried to be uploaded to the Internet of vehicles platform again.

S140: and/or the vehicle running data and the uploading instruction are sent to nearby vehicles through short-range communication between the vehicles, and the nearby vehicles upload the vehicle running data to the Internet of vehicles platform.

It is understood that V2V (Vehicle to Vehicle) communication technology is a communication technology that is not limited to fixed base stations, and provides direct end-to-end wireless communication for moving vehicles. That is, through the V2V communication technology, the vehicle terminals directly exchange wireless information with each other without being forwarded through the base station.

S200: and generating a vehicle running track path according to the vehicle running data.

Preferably, step S200 provided in the embodiment of the present invention may include the following steps:

s210: and generating at least one raster path based on a shortest path algorithm according to the positioning data, the speed data, the angle data and/or the steering data in the vehicle driving data.

Preferably, step S210 provided by the embodiment of the present invention may include the following steps:

s211: and establishing a double grid network.

Preferably, as shown in fig. 2, an initial grid including two staggered grids is set, wherein the main grid is a solid square grid composed of fixed lengths and one point of the solid square grid is selected as an origin, and the secondary grid is a supplementary grid with a dotted line formed by connecting the central points of the main grid. It is understood that the purpose of constructing the supplementary mesh is to represent the center coordinates of the mesh points of the supplementary mesh when it is not possible to determine which mesh point the anchor point falls on the edge of the mesh of the main mesh. And (3) constructing a dual grid network, wherein only four points of a single grid are difficult to judge the coordinate attribution under extreme conditions, and the points can be judged to fall into a main grid or a supplementary grid under most conditions.

S212: and decomposing the positioning data coordinate into the double grid network to obtain grid mesh points and grid mesh point coordinates.

In some possible embodiments, decomposing the positioning data coordinates into the dual grid network may obtain two grid mesh points and corresponding mesh point coordinates thereof, and may select a grid center point with a smaller distance as a final grid mesh point by calculating and comparing euclidean distances between positioning point coordinates and two grid center points.

It can be understood that, because the timestamp information is added to the vehicle driving data uploaded to the internet of vehicles platform, it can be known that each grid point also includes the vehicle driving information at that moment in addition to the positioning data information.

S213: and connecting the grid mesh points into at least one grid path according to the basic moving method and the shortest path algorithm of the double grid network.

Preferably, in the case of a dual raster network, the basic mobility method includes two ways: moving from the own network to the own network, wherein the moving distance can be a grid length unit; move from one network to another network by a distance of

A unit of grid length.

Preferably, due to the problems of the existing GPS itself, the GPS satellite orbit problem, or the vehicle peripheral signal, the data recorded by the car networking platform may have repeated, interrupted, and discontinuous abnormal situations, as shown in fig. 3, the upper point is obviously separated from the lower point group, and it can be determined as an abnormal location point. By setting the dot threshold, discontinuous accidental points in the positioning data or points with abnormal data of speed and angle can be cleared. In general, the threshold may be assumed to be the maximum range distance that most vehicles move in different directions at the average speed at this point.

Preferably, according to the continuous nature of the vehicle spatial state, the points where the vehicle appears generally vary within eight coordinates around the current grid point, as shown in fig. 4, for a point of one grid appearing in the main grid, there may be four directions, up, down, left and right, moving to the main grid, and moving to the supplementary grid also has four lateral directions.

Preferably, as shown in fig. 5, point S represents a positioning point in a previous state, point U represents a positioning point in a subsequent state, and points S and U are closer to a center point of the main grid, so that the starting grid point and the ending grid point are both grid points of the main grid.

Preferably, the continuous lines are selected based on Dijkstra algorithm in the shortest path algorithm and/or the principle of minimizing the area occupied by the grid points, so as to obtain at least one raster path as shown in fig. 6.

S220: and screening the at least one raster path based on a correlation analysis algorithm to determine a vehicle driving track path and frequent recording points.

Preferably, step S230 provided in the embodiment of the present invention may include the following steps:

s221: and generating a path frequent pattern tree according to the at least one raster path.

Preferably, step S221 provided in the embodiment of the present invention may include:

traversing and counting the frequency of grid points in a dense area in the double grids to obtain a project set, wherein the projects in the project set comprise the grid points and the frequency values thereof;

deleting the items lower than the preset threshold according to the preset support threshold or frequency threshold, and performing descending arrangement on the items in the item set according to the support;

and readjusting the sequence of the raster mesh points in the raster path according to the item set in descending order, and constructing a path frequent pattern tree according to the adjusted sequence.

S222: and obtaining the frequent item set and the support degree of the at least one raster path according to the path frequent pattern tree.

Specifically, according to each item in the path frequent pattern tree, a corresponding conditional pattern base is obtained, where the conditional pattern base is a path set ending with an item to be searched, and each path is a prefix path. And then, constructing a conditional frequent pattern tree of the project according to the conditional pattern base, and excavating a frequent item set and the support degree of the project.

S223: and cleaning the raster path based on a preset frequent threshold value to obtain a high-frequency raster path.

In some possible implementations, the frequent threshold may include a limit on the number of items in the frequent item set or a limit on the degree of support for the frequent item set.

S224: and calculating the frequency value of each of the high-frequency raster paths, and determining a vehicle driving track path according to the frequency value.

Specifically, a processing order may be selected according to a frequent threshold, and the frequent item set may be stored in a database according to the processing order, so as to obtain a record of each of the high-frequency raster paths. And performing recursive statistics on the records to obtain a final route as a vehicle driving track path.

S225: and taking each grid mesh point in the vehicle driving track path as a frequent recording point, and recording and storing the coordinate data of the frequent recording point.

S300: and constructing a road map containing standard positioning data according to the vehicle running track path.

Preferably, step S300 provided in the embodiment of the present invention may include the following steps:

s310: and matching the vehicle running track path with the actual road map information to obtain a road map, wherein the road map comprises standard positioning data of a plurality of frequent recording points.

Specifically, the position near the starting point of the road may be queried according to an actual road map, and the data such as the starting point, the ending point, or the speed in the vehicle driving track path may be compared to perform matching.

In particular, in some regions such as under overpasses or high-rise dense areas, the overall position deviation of the positioning data can be realized by matching the vehicle driving track path with an actual road map through an angle calculation mode.

S320: and/or performing networking calculation again according to the vehicle driving track path and the frequent recording points to obtain a road map with a path direction attribute.

Preferably, the same frequent recording point may represent two driving directions, a path in the same side direction may be selected for path rasterization again, and a vehicle driving track path in a different direction is taken as two lines.

S330: and/or updating the road map according to road change information issued by the Internet of vehicles platform or the external platform.

In some possible implementation manners, abnormal positioning data of the vehicle can be compared according to information such as road modification and road closure issued by the vehicle networking platform or the external platform, and the problem of positioning error or overall deviation of the area can be corrected.

It can be understood that the road map containing the standard positioning data embodies the error commonality and/or the deviation commonality about the vehicle positioning obtained by learning the vehicle driving data of the internet of vehicles platform, and can be used as a standard reference frame to correct the real-time positioning data of the target vehicle.

S400: and acquiring real-time positioning data of the target vehicle.

Preferably, step S400 provided by the embodiment of the present invention may include the following steps:

s410: and acquiring satellite real-time positioning data of the target vehicle.

S420: and/or acquiring the real-time positioning data of the surrounding vehicle through short-range communication with the surrounding vehicle and calculating to obtain the real-time positioning data of the target vehicle.

As shown in fig. 7, in the case where satellite real-time positioning data cannot be acquired, the position of the target vehicle is determined from the positioning data of different vehicles a, B, and C. The positioning data of the vehicles A, B and C are (x0, y0), (x1, y1), (x2, y2), respectively, the position point of the target vehicle is (x, y), the three-point position coordinates (x0, y0), (x1, y1), (x2, y2) and the distances d0, d1, d2 from the found position point (x, y) to the three points, respectively. Three circles are drawn with the radii d0, d1 and d2, and a position calculation formula of the position point is obtained according to the pythagorean theorem, that is:

the real-time positioning data of the target vehicle can be obtained through solving the equation.

S500: and correcting the real-time positioning data of the target vehicle according to the road map to obtain accurate positioning data, and updating the real-time positioning data of the target vehicle.

Preferably, step S500 provided in the embodiment of the present invention may include the following steps:

s510: and determining a positioning point and the position of the positioning point in the road map according to the real-time positioning data of the target vehicle.

S520: and determining the whole road route driven by the target vehicle based on the moving track direction of the target vehicle.

S530: and calculating the distance from the locating point to a frequent recording point in the whole road line, selecting the frequent recording point with the closest distance as a modified locating point, and taking the coordinate data of the frequent recording point as the modified accurate locating data of the target vehicle.

S540: and/or determining a historical positioning point and the position of the historical positioning point in the road map according to the historical positioning data of the target vehicle.

S550: and calculating a real-time positioning point of the target vehicle in the road map according to the historical positioning point and the vehicle driving data of the target vehicle, and taking coordinate data of the real-time positioning point in the road map as accurate positioning data of the target vehicle.

In a specific implementation scenario, when a target vehicle runs into a tunnel, positioning data of the target vehicle outside the tunnel but not entering the tunnel can be acquired, and real-time positioning data cannot be acquired due to weak signals in the tunnel or position errors and/or time delays exist in the acquired real-time positioning data, so that a moving track and an overall road line on the road map can be judged according to historical positioning points of the target vehicle, and then the real-time positioning points of the target vehicle are calculated according to running speed information, angle information, steering information and the like of the target vehicle.

S560: and/or acquiring real-time positioning data of the surrounding vehicle through short-range communication with the surrounding vehicle and calculating to obtain accurate positioning data of the target vehicle.

In a specific embodiment, the overall road route of the target vehicle is already determined, but the network transmission signal is not good, and the real-time accurate positioning data cannot be obtained, so the accurate positioning data of the target vehicle can be obtained from the near end by the three-point positioning method in step S420 provided by the embodiment of the present invention.

S570: and updating the positioning data in the vehicle-mounted system of the target vehicle according to the accurate positioning data.

S580: and/or uploading the accurate positioning data to a vehicle networking platform, performing iterative computation, and updating the road map or providing the road map for other positioning applications.

An embodiment of the present invention further provides a vehicle positioning data correction device, as shown in fig. 8, where the vehicle positioning data correction device includes:

the first obtaining module 810 is configured to obtain vehicle driving data in the internet of vehicles platform.

And a track path module 820, configured to generate a vehicle driving track path according to the vehicle driving data.

And a road map module 830 for constructing a road map containing standard positioning data according to the vehicle running track path.

The second obtaining module 840 is configured to obtain real-time location data of the target vehicle.

And a correction updating module 850, configured to correct the real-time positioning data of the visual inspection vehicle according to the road map, to obtain accurate positioning data, and update the real-time positioning data of the target vehicle.

The vehicle positioning data correcting device and method embodiments according to the embodiments of the present invention are based on the same inventive concept, and please refer to the method embodiments for details, which are not described herein again.

An embodiment of the present invention further provides a computer device, where the computer device includes: the vehicle positioning data correcting device comprises a processor and a memory, wherein at least one instruction or at least one program is stored in the memory, and the at least one instruction or the at least one program is loaded by the processor and executed to realize the vehicle positioning data correcting method provided by the embodiment of the invention.

The memory may be used to store software programs and modules, and the processor may execute various functional applications by executing the software programs and modules stored in the memory. The memory can mainly comprise a program storage area and a data storage area, wherein the program storage area can store an operating system, application programs needed by functions and the like; the storage data area may store data created according to use of the apparatus, and the like. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory may also include a memory controller to provide the processor access to the memory.

The method embodiments provided by the embodiments of the present invention may be executed in a computer terminal, a server, or a similar computing device, that is, the computer device may include a computer terminal, a server, or a similar computing device. Fig. 9 is a block diagram of a hardware structure of a computer device for operating a vehicle positioning data correction method according to an embodiment of the present invention, and as shown in fig. 9, the internal structure of the computer device may include, but is not limited to: a processor, a network interface, and a memory. The processor, the network interface, and the memory in the computer device may be connected by a bus or in other manners, and fig. 9 shown in the embodiment of the present specification is exemplified by being connected by a bus.

The processor (or CPU) is a computing core and a control core of the computer device. The network interface may optionally include a standard wired interface, a wireless interface (e.g., WI-FI, mobile communication interface, etc.). Memory (Memory) is a Memory device in a computer device used to store programs and data. It is understood that the memory herein may be a high-speed RAM storage device, or may be a non-volatile storage device (non-volatile memory), such as at least one magnetic disk storage device; optionally, at least one memory device located remotely from the processor. The memory provides storage space that stores an operating system of the electronic device, which may include, but is not limited to: a Windows system (an operating system), a Linux system (an operating system), an Android system, an IOS system, etc., which are not limited in the present invention; also, one or more instructions, which may be one or more computer programs (including program code), are stored in the memory space and are adapted to be loaded and executed by the processor. In an embodiment of the present specification, the processor loads and executes one or more instructions stored in the memory to implement the vehicle positioning data correction method provided in the foregoing method embodiment.

The embodiment of the present invention further provides a computer storage medium, where at least one instruction or at least one program is stored in the computer storage medium, and the at least one instruction or the at least one program is loaded by the processor and executes the vehicle positioning data correction method according to the embodiment of the present invention.

Optionally, in this embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.

It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And that specific embodiments have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus, system and server embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference may be made to some descriptions of the method embodiments for relevant points.

It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims

1. A vehicle positioning data correcting method, characterized by comprising:

acquiring vehicle running data in the Internet of vehicles platform;

generating at least one raster path from the vehicle travel data;

screening the at least one raster path based on a correlation analysis algorithm to determine a vehicle driving track path;

acquiring real-time positioning data of a target vehicle;

correcting the real-time positioning data of the target vehicle according to the road map to obtain accurate positioning data, and updating the real-time positioning data of the target vehicle;

wherein, the screening the at least one raster path based on the association analysis algorithm to determine the vehicle driving track path comprises:

and calculating the frequency value of each raster path in the high-frequency raster paths, and determining the vehicle driving track path according to the frequency value.

2. The vehicle positioning data correcting method according to claim 1, wherein the generating at least one raster path according to the vehicle travel data includes:

and generating at least one raster path based on a shortest path algorithm according to the positioning data, the speed data, the angle data and/or the steering data in the vehicle driving data.

3. The vehicle positioning data correction method according to claim 2, wherein the generating at least one raster path based on a shortest path algorithm according to the positioning data, speed data, angle data and/or steering data in the vehicle driving data comprises:

establishing a double grid network;

4. A vehicle positioning data correcting method according to claim 1, characterized by further comprising:

5. The method according to claim 4, wherein the step of constructing a road map containing standard positioning data according to the vehicle travel track path comprises:

and/or updating the road map according to road change information issued by the Internet of vehicles platform or other external platforms.

6. The vehicle positioning data correcting method according to claim 1, wherein the acquiring real-time positioning data of the target vehicle comprises:

acquiring satellite real-time positioning data of a target vehicle;

7. The method according to claim 1, wherein the step of correcting the real-time positioning data of the target vehicle according to the road map to obtain accurate positioning data comprises:

and calculating a real-time positioning point of the target vehicle in the road map according to the historical positioning point and the vehicle driving data of the target vehicle, and taking coordinate data of the real-time positioning point in the road map as accurate positioning data of the target vehicle.

8. The vehicle positioning data correcting method according to claim 1, wherein the obtaining of the accurate positioning data and the updating of the real-time positioning data of the target vehicle comprise:

9. A vehicle positioning data correcting apparatus characterized by comprising:

the first acquisition module is used for acquiring vehicle running data in the vehicle networking platform;

the track path module is used for generating at least one raster path according to the vehicle running data;

the correction updating module is used for correcting the real-time positioning data of the target vehicle according to the road map to obtain accurate positioning data and updating the real-time positioning data of the target vehicle;

the screening the at least one raster path based on the association analysis algorithm to determine the vehicle driving track path comprises:

10. A computer device, characterized in that the computer device comprises: a processor and a memory, said memory having stored therein at least one instruction or at least one program, said at least one instruction or said at least one program being loaded and executed by said processor to implement a vehicle positioning data correcting method according to any one of claims 1 to 8.