A vehicle positioning method, apparatus, device and medium

Abstract

The application discloses a vehicle positioning method, which comprises the following steps: acquiring a front-view lane line of a target road section and a map lane line of the target road section; matching the front-view lane line with the map lane line, and recording a first position of a vehicle when the matching is successful; acquiring a second position determined based on GPS, wherein the first position and the second position are acquired at the same time; and performing Kalman filtering on the first position and the second position to realize positioning of the vehicle. The front-view lane line is matched with the map lane line to obtain a matching result, and the position of the vehicle when the matching is successful is fused with a GPS positioning position to position the vehicle, so that the positioning precision and robustness are improved.

Description

Technical Field

[0001] This invention belongs to the field of autonomous driving technology, specifically relating to a vehicle positioning method, device, equipment, and medium. Background Technology

[0002] The booming development of the automotive industry has made consumers increasingly demanding of emerging automotive manufacturing technologies. The main contradiction facing domestic automakers lies in the gap between the people's ever-growing need for vehicles and the relatively lagging development of related automotive manufacturing technologies. Statistics show that in 2021, the number of vehicles reached 395 million, and the number of licensed drivers reached 481 million. This massive number of vehicles has also led to increasingly severe traffic congestion. High-precision real-time positioning is of great significance for alleviating traffic congestion and the development of autonomous driving. However, current high-precision real-time positioning technologies are not accurate enough. Summary of the Invention

[0003] In view of the shortcomings of the prior art described above, the present invention provides a vehicle positioning method, device, equipment and medium to solve the above technical problems.

[0004] This invention provides a vehicle positioning method, the method comprising:

[0005] Obtain the forward lane lines of the vehicle on the target road segment, as well as the map lane lines of the target road segment;

[0006] The forward-looking lane lines are matched with the map lane lines, and the vehicle's first position is recorded when the match is successful.

[0007] Obtain a second location determined by GPS, wherein the first and second locations are obtained at the same time.

[0008] Kalman filtering is applied to the first and second positions to achieve vehicle positioning.

[0009] In one embodiment of the present invention, before matching the forward-looking lane lines with the map lane lines, the method further includes:

[0010] Discretize the forward lane line into multiple forward view points to obtain the forward lane line point cloud;

[0011] Discretize the map lane lines into multiple map points to obtain the map lane line point cloud;

[0012] In one embodiment of the present invention, matching the forward-looking lane lines with the map lane lines includes:

[0013] Obtain any forward lane point from the forward lane line point cloud;

[0014] Obtain the map lane point in the map lane line point cloud that is closest to any forward-looking lane point, and determine the equation of the straight line passing through any two closest map lane points to obtain multiple straight line equations;

[0015] Calculate the distance between any one of the forward-looking lane points and the multiple straight line equations to obtain multiple distances;

[0016] Each distance is compared with a preset distance threshold to complete the matching of the forward-looking lane line with the map lane line.

[0017] In one embodiment of the present invention, before comparing each distance with a preset distance threshold, the method further includes:

[0018] A preset distance threshold is set based on the curvature of the forward-looking lane line, where different curvatures correspond to different preset distance thresholds.

[0019] In one embodiment of the present invention, when the distance is less than a preset distance threshold, the forward lane point is successfully matched with two map lane points; when the number of forward lane points on the forward lane line that are successfully matched with two map lane points or the ratio of the number of forward lane points on the entire forward lane line exceeds a set threshold, the forward lane line is successfully matched with the map lane line.

[0020] Calculate the ratio K of the number of forward lane points that match the map lane points to the total number of forward lane points along the entire forward lane line, the number N of forward lane points that successfully match the map lane points along each forward lane line, and the average distance e from the successfully matched forward lane points to the straight line.

[0021] When the ratio K and the quantity N are greater than a set first threshold, and the average distance e is greater than a set second threshold, the forward-looking lane line point cloud and the map lane line point cloud are successfully matched.

[0022] In one embodiment of the present invention, the magnitudes of the first threshold and the second threshold vary with the precision of the second position.

[0023] In one embodiment of the present invention, performing Kalman filtering on the first position and the second position includes:

[0024] Initial trajectory is calculated using a kinematic model based on vehicle motion information;

[0025] Kalman filtering is performed using the first position and the second position as observations.

[0026] The present invention provides a vehicle positioning device, the device comprising:

[0027] The data acquisition module is used to acquire the forward lane lines of the vehicle on the target road segment, as well as the map lane lines of the target road segment;

[0028] The matching module is used to match the forward-looking lane lines with the map lane lines, and record the vehicle's first position when the match is successful;

[0029] The location acquisition module is used to acquire a second location determined by GPS, wherein the first and second locations are acquired at the same time.

[0030] The filtering module is used to perform Kalman filtering on the first position and the second position to achieve vehicle positioning.

[0031] The present invention provides an electronic device, the electronic device comprising:

[0032] One or more processors;

[0033] A storage device for storing one or more programs that, when executed by one or more processors, cause the electronic device to perform the steps of the vehicle positioning method described above.

[0034] The present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a computer processor, causes the computer to perform the steps of the above-described vehicle positioning method.

[0035] The beneficial effects of this invention are as follows: A vehicle positioning method of this invention includes: acquiring the forward-looking lane lines of a vehicle on a target road segment and the map lane lines of the target road segment; matching the forward-looking lane lines with the map lane lines, and recording the first position of the vehicle when the matching is successful; acquiring a second position determined based on GPS, wherein the first position and the second position are acquired at the same time; and performing Kalman filtering on the first position and the second position to achieve vehicle positioning. This invention matches the forward-looking lane lines with the map lane lines to obtain a matching result, and fuses the vehicle position when the matching is successful with the GPS positioning position to locate the vehicle, thereby improving the accuracy and robustness of positioning.

[0036] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0037] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0038] Figure 1 This is a flowchart illustrating a vehicle positioning method in an exemplary embodiment of this application;

[0039] Figure 2 This is a flowchart illustrating the matching of forward-looking lane lines with map lane lines, as shown in an exemplary embodiment of this application;

[0040] Figure 3 This is a block diagram illustrating a vehicle positioning device in an exemplary embodiment of this application;

[0041] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0042] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0043] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0044] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0045] Please see Figure 1 , Figure 1This application illustrates a vehicle positioning method as an exemplary embodiment. Figure 1 The vehicle positioning method specifically includes steps S110 to S140, which are described in detail below:

[0046] Step S110: Obtain the forward lane lines of the vehicle on the target road segment, as well as the map lane lines of the target road segment;

[0047] It should be noted that the vehicle is equipped with a forward-facing camera, and the target road segment refers to the road segment that the vehicle travels from the starting point to the destination.

[0048] The aforementioned forward-view lane lines can be lane lines captured by the forward-view camera; the aforementioned map lane lines can be lane lines within a preset range obtained from the map based on the vehicle's current position.

[0049] In this embodiment, the latitude and longitude coordinates of the lane lines output by the forward-looking camera are converted into relative coordinates in the vehicle coordinate system, and then fitted into a cubic curve, which is used to represent the forward-looking lane lines.

[0050] The forward lane markings indicate:

[0051] y = C0 + C1x + C2x 2 +C3x 3 x is the longitudinal distance in the vehicle coordinate system, y is the lateral distance in the vehicle coordinate system, C0, C1, C2, and C3 are the coefficients of the output reference curve, C0 corresponds to the lateral position at the origin of the vehicle coordinate system, C1 corresponds to the heading angle at the origin of the vehicle coordinate system, C2 corresponds to the curvature at the origin of the vehicle coordinate system, and C3 corresponds to the rate of change of curvature at the origin of the vehicle coordinate system.

[0052] Step S120: Match the forward-looking lane line with the map lane line, and record the first position of the vehicle when the match is successful;

[0053] In one embodiment, before matching the forward-looking lane lines with the map lane lines, the method further includes:

[0054] In one embodiment, generating a forward-looking lane line point cloud based on the forward-looking lane line includes:

[0055] Discretize the forward lane line into multiple forward view points to obtain the forward lane line point cloud;

[0056] Discretize the map lane lines into multiple map points to obtain the map lane line point cloud.

[0057] Please see Figure 2 , Figure 2This is a flowchart illustrating the matching of forward-looking lane lines with map lane lines, as shown in an exemplary embodiment of this application. It should be noted that matching the forward-looking lane lines with map lane lines actually involves matching the forward-looking lane line point cloud with the map lane line point cloud. Figure 2 In the process, matching the forward-looking lane lines with the map lane lines includes:

[0058] Step S210: Obtain any one forward lane point from the forward lane line point cloud;

[0059] The forward lane line point cloud includes multiple points. In order to match the forward lane line point cloud with the map lane line point cloud, a forward lane point is first selected from the multiple points in the forward lane line point cloud.

[0060] Step S220: Obtain the map lane point in the map lane line point cloud that is closest to any forward-looking lane point, and determine the equation of the straight line passing through any two closest map lane points to obtain multiple straight line equations;

[0061] The map lane line point cloud also includes multiple map lane points. To match the map lane line point cloud with the forward-looking lane line point cloud, two map lane points need to be selected from the multiple map lane points. These two map lane points are the closest to the forward-looking lane point. Connect the two map lane points and extend the line segment passing through the two map lane points in both directions to obtain a straight line passing through the two map lane points.

[0062] Since there may be multiple map lane points that are closest to the forward-looking lane point, there can be multiple straight lines that cross any two map lane points.

[0063] Step S230: Calculate the distance between any one forward-looking lane point and the plurality of straight line equations to obtain multiple distances;

[0064] The distance between the forward-looking lane point and the line equation is calculated using the formula for the distance from a point to a line. Since the forward-looking lane line point cloud includes multiple forward-looking lane points, it is necessary to match each forward-looking lane point with the map lane points, that is, to calculate the distance between each forward-looking lane point and the corresponding line equation, resulting in multiple distances.

[0065] Step S240: Compare each distance with a preset distance threshold to complete the matching of the forward-looking lane line with the map lane line.

[0066] It should be noted that the preset distance threshold is set based on the curvature of the forward-looking lane line, and different curvatures correspond to different preset distance thresholds.

[0067] Due to the limitations of camera perception, the fitting effect for cubic curves is better on straight roads, while the fitting effect is less satisfactory for lanes with greater curvature, such as ramps and roundabouts, which are farther away from the vehicle in the x-direction (direction of travel). The greater the curvature of the lane line, the worse the curve fitting effect becomes at greater distances. Therefore, this embodiment uses different preset distance thresholds, i.e., the distance in the x-direction of the vehicle, for different curvatures.

[0068] In one embodiment, when the distance is less than a preset distance threshold, the forward-looking lane point is successfully matched with the two map lane points;

[0069] It is understood that when the distance is greater than the preset distance threshold, it is considered that the forward lane point and the two map lane points are not matched successfully. At this time, the matching result of the forward lane point and the two map lane points can be abandoned. That is, when locating the vehicle, the forward lane point and the two map lane points are excluded. This method can eliminate curves with few matching points and large errors, and ensure the real-time accuracy of the positioning.

[0070] After matching the forward lane points with the map lane points through the above steps, we obtain: minimum distance from the point to the straight line: dmin, number of successfully matched forward point clouds: F(fc), and number of successfully matched forward lane lines: L(line).

[0071] After traversal, it can be determined that a forward lane point can correspond to multiple map lane points with the same distance. Correspondingly, multiple straight lines passing through two map lane points can be determined, thus obtaining a forward lane point corresponding to several distances. The minimum distance from the point to the straight line represents the minimum value among these several distances.

[0072] A successfully matched forward-looking point cloud refers to the forward-looking lane points that are successfully matched with two map lane points, i.e., the number of forward-looking point clouds.

[0073] A successfully matched forward lane line is defined as one where the number of successfully matched forward lane points on a forward lane line exceeds a set value, or the ratio of the number of successfully matched forward lane points to the total number of matched forward lane points on the entire forward lane line exceeds a set value.

[0074] Calculate the ratio K of the number of forward lane points that match the map lane points to the total number of forward lane points on the entire forward lane line: K = F(fc) / F(source); the number N of forward lane points that successfully match the map lane points on each forward lane line: N = F(fc) / L(line); the average distance (average error) from the successfully matched forward lane points to the straight line: e = sum(dmin) / F(fc).

[0075] When the ratio K and the number N are greater than the first set threshold and the average distance e is greater than the second set threshold, the forward lane line point cloud is considered to be successfully matched with the map lane line point cloud.

[0076] It should be noted that the magnitudes of the first and second thresholds vary with the accuracy of the second location. That is, the magnitudes of the first and second thresholds are determined based on the GPS status. Where the GPS status is poor, i.e., when the GPS accuracy is low, the first and second thresholds are correspondingly smaller; while where the GPS status is good, i.e., when the GPS accuracy is high, the first and second thresholds are correspondingly larger.

[0077] Step S130: Obtain the second location determined by GPS, wherein the first and second locations are obtained at the same time.

[0078] This application will use map semantic positioning and GPS positioning to fuse. Therefore, after obtaining the first location, that is, the vehicle location obtained by map semantic positioning, in the aforementioned steps, it is also necessary to obtain the second location, that is, the vehicle location determined based on GPS.

[0079] It should be noted that GPS positioning is generated by the vehicle's own inertial navigation system (INS), which outputs positioning information along with the positioning type and covariance. The INS outputs positioning latitude and longitude information and the corresponding covariance at a certain frequency.

[0080] Step S140: Perform Kalman filtering on the first position and the second position to achieve vehicle positioning.

[0081] Specifically, performing Kalman filtering on the first position and the second position includes:

[0082] The initial trajectory is calculated using a kinematic model based on vehicle motion information; Kalman filtering is performed using the first and second positions as observations.

[0083] The Kalman filtering process generally consists of two steps: prediction and update. First, information such as wheel speed, angular velocity, and acceleration from the IMU (Inertial Measurement Unit) is used to calculate the initial trajectory using a kinematic model. Then, semantic localization information and GPS positioning information are received to update the initial prediction results. Finally, the covariance matrix of the filter is dynamically updated based on the state provided by the GPS itself to obtain better update results.

[0084] More specifically, the prior state is calculated based on the motion model during the Kalman filtering process. and prior covariance

[0085] Prior state: R is the rotational torque, v is the velocity, and Δt represents the time interval.

[0086] The Kalman filter update uses two observations: the GPS positioning result (second position) and the semantic positioning result (first position). The GPS update covariance matrix is ​​given by the combined inertial navigation system itself. When the semantic positioning (look-ahead lane lines and map lane lines) matches well (i.e., K, N, and e are all greater than given threshold values, K and N are greater than the first threshold, and e is greater than the second threshold), the position and attitude covariances are set to 0.3*scale and 0.17*scale, respectively. Here, 0.3 and 0.17 are empirical values ​​and need to be adjusted when the matching is poor. The scale is dynamically given based on the GPS status; when the GPS status is good, the scale is set to 3×10. -2 When GPS status is poor, set the scale to 5×10. -3 Then the posterior covariance P and the final positioning result x were calculated.

[0087]

[0088] I represents the identity matrix, K k H represents the gain, and H represents the input covariance. Let X represent the final result of the prior state. max This indicates that, based on the vehicle's x-axis (forward direction) length threshold (preset distance threshold), Z... k Indicates the amount of observation.

[0089] This invention provides a vehicle positioning method that, through the setting of dynamic thresholds, flexibly adapts to different thresholds for different scenarios, improves the stability of the algorithm, optimizes the fusion positioning results, and enhances its accuracy and robustness.

[0090] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0091] Figure 3 This is a block diagram illustrating a vehicle positioning device according to an exemplary embodiment of this application. The device can be specifically configured in an in-vehicle infotainment system. The device can also be applied to other exemplary implementation environments and specifically configured in other devices; this embodiment does not limit the implementation environment to which the device is applicable.

[0092] like Figure 3 As shown, this application provides a vehicle positioning device, which includes:

[0093] The data acquisition module 310 is used to acquire the forward lane lines of the vehicle on the target road segment, as well as the map lane lines of the target road segment;

[0094] The matching module 320 is used to match the forward-looking lane line with the map lane line, and record the first position of the vehicle when the match is successful.

[0095] The location acquisition module 330 is used to acquire a second location determined based on GPS, wherein the first and second locations are acquired at the same time.

[0096] The filtering module 340 is used to perform Kalman filtering on the first position and the second position to achieve vehicle positioning.

[0097] It should be noted that the vehicle positioning device and the vehicle positioning method provided in the above embodiments belong to the same concept. The specific operation methods of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the vehicle positioning device provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0098] Embodiments of this application also provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the vehicle positioning method provided in the above embodiments.

[0099] Figure 4 A schematic diagram of a computer system suitable for implementing the embodiments of this application is shown. It should be noted that... Figure 4 The computer system of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0100] like Figure 4 As shown, the computer system includes a Central Processing Unit (CPU), which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) or loaded from storage into Random Access Memory (RAM), such as executing the methods described in the above embodiments. The RAM also stores various programs and data required for system operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0101] The following components are connected to the I / O interface: input sections including keyboards, mice, etc.; output sections including cathode ray tubes (CRTs), liquid crystal displays (LCDs), and speakers; storage sections including hard drives; and communication sections including network interface cards such as LAN (Local Area Network) cards and modems. The communication sections perform communication processing via networks such as the Internet. Drives are also connected to the I / O interface as needed. Removable media, such as disks, optical discs, magneto-optical discs, semiconductor memories, etc., are installed on the drive as needed so that computer programs read from them can be installed into the storage section as required.

[0102] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including methods for performing processes. Figure 2 The computer program illustrating the method. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When executed by a central processing unit (CPU), the computer program performs the various functions defined in the system of this application.

[0103] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0104] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0105] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0106] Another aspect of this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a computer's processor, causes the computer to perform the vehicle positioning method as described above. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not assembled into the electronic device.

[0107] Another aspect of this application provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the vehicle positioning method provided in the various embodiments described above.

[0108] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A vehicle positioning method characterized by comprising: The method includes: Obtain the forward lane lines of the vehicle on the target road segment, as well as the map lane lines of the target road segment; The forward-looking lane lines are matched with the map lane lines, and the vehicle's first position is recorded when the match is successful. Obtain a second location determined by GPS, wherein the first and second locations are obtained at the same time. Kalman filtering is applied to the first and second positions to achieve vehicle positioning; The Kalman filtering of the first and second positions includes: Initial trajectory is calculated using a kinematic model based on vehicle motion information; Kalman filtering is performed using the first position and the second position as observations.

2. The vehicle positioning method according to claim 1, characterized by, Before matching the forward-looking lane lines with the map lane lines, the process also includes: Discretize the forward lane line into multiple forward view points to obtain the forward lane line point cloud; Discretize the map lane lines into multiple map points to obtain the map lane line point cloud.

3. The vehicle positioning method according to claim 2, characterized by, The matching of the forward-looking lane lines with the map lane lines includes: Obtain any forward lane point from the forward lane line point cloud; Obtain the map lane point in the map lane line point cloud that is closest to any forward-looking lane point, and determine the equation of the straight line passing through any two closest map lane points to obtain multiple straight line equations; Calculate the distance between any one of the forward-looking lane points and the multiple straight line equations to obtain multiple distances; Each distance is compared with a preset distance threshold to complete the matching of the forward-looking lane line with the map lane line.

4. The vehicle positioning method according to claim 3, characterized by, Before comparing each distance with a preset distance threshold, the method further includes: A preset distance threshold is set based on the curvature of the forward-looking lane line, where different curvatures correspond to different preset distance thresholds.

5. The vehicle positioning method according to claim 3, characterized by, When the distance is less than a preset distance threshold, the forward lane point is successfully matched with two map lane points; when the number of forward lane points on the forward lane line that are successfully matched with two map lane points or the ratio of the number of forward lane points on the entire forward lane line exceeds a set threshold, the forward lane line is successfully matched with the map lane line. Calculate the ratio K of the number of forward lane points that match the map lane points to the total number of forward lane points along the entire forward lane line, the number N of forward lane points that successfully match the map lane points along each forward lane line, and the average distance e from the successfully matched forward lane points to the straight line. When the ratio K and the quantity N are greater than a set first threshold, and the average distance e is greater than a set second threshold, the forward-looking lane line point cloud and the map lane line point cloud are successfully matched.

6. The vehicle positioning method according to claim 5, characterized by, The magnitudes of the first threshold and the second threshold vary with the precision of the second position.

7. A vehicle positioning apparatus characterized by comprising: The device includes: The data acquisition module is used to acquire the forward lane lines of the vehicle on the target road segment, as well as the map lane lines of the target road segment; The matching module is used to match the forward-looking lane lines with the map lane lines, and record the vehicle's first position when the match is successful; The location acquisition module is used to acquire a second location determined by GPS, wherein the first and second locations are acquired at the same time. A filtering module is used to perform Kalman filtering on the first position and the second position to achieve vehicle positioning; the Kalman filtering on the first position and the second position includes: Initial trajectory is calculated using a kinematic model based on vehicle motion information; Kalman filtering is performed using the first position and the second position as observations.

8. An electronic device, comprising: The electronic device includes: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the electronic device to perform the steps of the vehicle positioning method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by the computer's processor, causes the computer to perform the steps of the vehicle positioning method according to any one of claims 1 to 6.

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