Vehicle positioning method, device and equipment and storage medium

Abstract

The invention provides a vehicle positioning method, device and equipment and a storage medium, and relates to the technical field of navigation, and the method comprises the steps: calculating the first positioning data of a target vehicle at the current moment according to the measurement data of a navigation satellite at the current moment; calculating second positioning data of the target vehicle at the current moment according to measurement data of the inertial measurement unit of the target vehicle at the current moment; obtaining lane line image information of a lane where the target vehicle is located, and determining transverse distance information of the target vehicle relative to a lane line according to the lane line image information; calculating third positioning data of the target vehicle at the current moment according to the first positioning data and the second positioning data; and correcting the third positioning data according to the transverse distance information to obtain fourth positioning data of the target vehicle at the current moment. The method integrates three positioning modes of satellite navigation, inertial navigation and environment feature matching, overcomes the defects of a single positioning mode, and has the advantages of high positioning precision, stable work and high positioning information updating frequency.

Description

Technical Field

[0001] The present application relates to the field of navigation technology, and in particular to a vehicle positioning method, device, equipment and storage medium. Background Art

[0002] Vehicle positioning technology is an important support for realizing autonomous driving. Autonomous driving positioning technology can be divided into three categories based on the positioning method: signal-based positioning, namely the global navigation satellite system, including the GPS system and the RTK positioning system; track calculation, relying on sensors such as IMU to infer the current posture based on the posture at the previous moment; environmental feature matching, obtaining posture information based on image or point cloud feature matching.

[0003] However, a single positioning method still has defects: signal-based positioning relies on GPS and differential signals, and is prone to signal loss in tunnels or other obstructed areas, resulting in loss of navigation capabilities; positioning based on track calculation can only provide location information relative to a known starting point, rather than absolute geographic coordinates; positioning based on environmental feature matching has high accuracy, but the output signal frequency is unstable. Summary of the invention

[0004] The present application provides a vehicle positioning method, device, equipment and storage medium to solve the problems that signal-based positioning relies on GPS and differential signals, and is prone to signal loss and navigation capability in tunnels or other obstructed areas; positioning based on track calculation can only provide location information relative to a known starting point, rather than absolute geographic coordinates; positioning based on environmental feature matching has high accuracy, but the output signal frequency is unstable.

[0005] In a first aspect, the present application provides a vehicle positioning method, the method comprising:

[0006] Calculating first positioning data of the target vehicle at the current moment according to the measurement data of the navigation satellite at the current moment;

[0007] Calculating second positioning data of the target vehicle at the current moment according to the measurement data of the inertial measurement unit of the target vehicle at the current moment;

[0008] Acquire lane line image information of the lane where the target vehicle is located, and determine the lateral distance information of the target vehicle relative to the lane line according to the lane line image information;

[0009] Calculating the third positioning data of the target vehicle at the current moment according to the first positioning data and the second positioning data;

[0010] The third positioning data is corrected according to the lateral distance information to obtain the fourth positioning data of the target vehicle at the current moment.

[0011] In a possible design, the calculating the first positioning data of the target vehicle at the current moment according to the measurement data of the navigation satellite at the current moment includes:

[0012] The first positioning data is calculated according to the measurement data of the navigation satellite at the current moment and the measurement data of the inertial measurement unit of the target vehicle at the current moment.

[0013] In one possible design, calculating the first positioning data according to the measurement data of the navigation satellite at the current moment and the measurement data of the inertial measurement unit of the target vehicle at the current moment includes:

[0014] The first positioning data is calculated by the filtering algorithm based on the measurement data of the navigation satellite at the current moment, the measurement data of the inertial measurement unit of the target vehicle at the current moment, and the positioning data of the target vehicle at the current moment predicted by the filtering algorithm at the previous moment.

[0015] In one possible design, calculating the second positioning data of the target vehicle at the current moment according to the measurement data of the inertial measurement unit of the target vehicle at the current moment includes:

[0016] The second positioning data is calculated by the filtering algorithm according to the measurement data of the inertial measurement unit of the target vehicle at the current moment and the positioning data of the target vehicle at the current moment predicted by the filtering algorithm at the previous moment.

[0017] In a possible design, calculating the third positioning data of the target vehicle at the current moment according to the first positioning data and the second positioning data includes:

[0018] The third positioning data is calculated by the filtering algorithm according to the first positioning data, the second positioning data, and the positioning data of the target vehicle at the current moment predicted by the filtering algorithm at the previous moment.

[0019] In a possible design, the acquiring lane line image information of the lane where the target vehicle is located, and determining the lateral distance information of the target vehicle relative to the lane line according to the lane line image information, includes:

[0020] Determining first description information of the lane line according to the lane line image information;

[0021] Acquire second description information of the lane line from a map according to the third positioning data;

[0022] The first description information is matched with the second description information to determine the lateral distance information.

[0023] In a possible design, the third positioning data is corrected according to the lateral distance information to obtain the fourth positioning data of the target vehicle at the current moment, including:

[0024] According to the lateral distance information, as well as the position information, heading angle information, speed information, angular velocity information, and acceleration information of the target vehicle in the third positioning data, the corrected position information, corrected heading angle information, corrected speed information, corrected angular velocity information, and corrected acceleration information in the fourth positioning data are calculated respectively.

[0025] In a second aspect, the present application provides a vehicle positioning device, the device comprising:

[0026] The satellite positioning module is used to calculate the first positioning data of the target vehicle at the current moment according to the measurement data of the navigation satellite at the current moment;

[0027] An inertial measurement unit positioning module, used to: calculate the second positioning data of the target vehicle at the current moment according to the measurement data of the inertial measurement unit of the target vehicle at the current moment;

[0028] The lane line positioning module is used to obtain the lane line image information of the lane where the target vehicle is located, and determine the lateral distance information of the target vehicle relative to the lane line according to the lane line image information;

[0029] Fusion positioning module for:

[0030] Calculating the third positioning data of the target vehicle at the current moment according to the first positioning data and the second positioning data;

[0031] The third positioning data is corrected according to the lateral distance information to obtain the fourth positioning data of the target vehicle at the current moment.

[0032] In a third aspect, the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0033] The memory stores computer-executable instructions;

[0034] The processor executes the computer-executable instructions stored in the memory to implement the vehicle positioning method as described in the first aspect.

[0035] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the vehicle positioning method as described in the first aspect.

[0036] The vehicle positioning method, device, equipment and storage medium provided by this application achieve the following technical effects:

[0037] The method calculates the first motion state information of the target vehicle through the measurement data of the navigation satellite, calculates the second motion state information of the target vehicle through the measurement data of the inertial measurement unit, and calculates the positioning information of the target vehicle by fusing the first motion state information and the second motion state information. The positioning information is then corrected by the lateral distance of the target vehicle relative to the lane line of the current lane. The method combines the three navigation and positioning methods of satellite navigation, inertial navigation, and environmental feature matching, overcomes the defects of a single positioning method, and has the advantages of high positioning accuracy, stable operation, and high frequency of positioning information update. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 Schematic diagram of the vehicle positioning method provided in the embodiment of the present application Figure 1 ;

[0040] Figure 2 Schematic diagram of the vehicle positioning method provided in the embodiment of the present application Figure 2 ;

[0041] Figure 3 A schematic diagram of combined navigation and positioning based on local Kalman filtering provided in an embodiment of the present application;

[0042] Figure 4 A schematic diagram of an inertial measurement unit positioning based on a local Kalman filter provided in an embodiment of the present application;

[0043] Figure 5 A schematic diagram of a method for determining lateral distance information provided in an embodiment of the present application;

[0044] Figure 6 A schematic diagram of fusion positioning provided in an embodiment of the present application;

[0045] Figure 7 A schematic diagram of the structure of a vehicle positioning device provided in an embodiment of the present application;

[0046] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation methods described in the following exemplary embodiments do not represent all implementation methods consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the attached claims, rather than all embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0048] First, the relevant concepts or nouns involved in this application are explained:

[0049] Inertial Measurement Unit: An inertial measurement unit is an electronic device that is able to measure and report specific forces, angular velocity, and sometimes magnetic fields of an object. An IMU typically contains an accelerometer and gyroscope, and sometimes a magnetometer. Accelerometers measure linear acceleration, gyroscopes measure angular velocity, and magnetometers measure magnetic field strength.

[0050] Heading angle: Heading angle refers to the direction of an object relative to a reference direction (usually geographic north).

[0051] Northeast Celestial Coordinate System: Northeast Celestial Coordinate System is a geographic coordinate system used to represent the three-dimensional position of a point on the earth's surface. The three axes of this coordinate system are: East axis: pointing to the geographic east; North axis: pointing to the geographic north; Up axis: perpendicular to the earth's surface, pointing to the sky (i.e. the opposite direction of gravity).

[0052] In view of the problems that the signal-based positioning in the prior art relies on GPS and differential signals, and is prone to signal loss and navigation capability in tunnels or other obstructed areas; the positioning based on track calculation can only provide location information relative to a known starting point, rather than absolute geographic coordinates; the positioning based on environmental feature matching has high accuracy, but the output signal frequency is unstable, the technical concept of the present application is: according to the measurement data of the navigation satellite, the first positioning data of the target vehicle at the current moment is calculated; according to the measurement data of the inertial measurement unit (IMU) of the target vehicle, the second positioning data of the target vehicle at the current moment is calculated; then according to the lane line information of the lane where the target vehicle is located, the lateral distance of the target vehicle relative to the lane line is determined; after the first positioning data and the second positioning data are fused and calculated, the third positioning data of the target vehicle can be determined, and then the third positioning data is corrected using the above-mentioned lateral distance of the target vehicle relative to the current lane line to obtain more accurate fourth positioning data.

[0053] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0054] Embodiment 1

[0055] Figure 1 Schematic diagram of the vehicle positioning method provided in the embodiment of the present application Figure 1 , this method can be applied to the vehicle's navigation and positioning system. Figure 1 As shown, the method includes:

[0056] S101, calculating first positioning data of the target vehicle at the current moment according to the measurement data of the navigation satellite at the current moment;

[0057] Specifically, the target vehicle's satellite signal receiving device, such as a GPS receiver, can receive measurement data from multiple navigation satellites. Through the measurement data of the navigation satellite, the first positioning data of the target vehicle at the current moment can be calculated, for example, including the time data at the current moment, the longitude data and latitude data of the target vehicle, the altitude data of the target vehicle, the heading angle data of the target vehicle, and the speed data and acceleration data of the target vehicle in the northeast sky coordinate system.

[0058] S102, calculating the second positioning data of the target vehicle at the current moment according to the measurement data of the inertial measurement unit of the target vehicle at the current moment;

[0059] Specifically, by acquiring the measurement data of each sensor of the inertial measurement unit of the target vehicle, the second positioning data of the target vehicle at the current moment is calculated, which may include time data, and the position data, speed data, altitude data, heading angle data, angular velocity data, and acceleration data of the target vehicle.

[0060] S103, obtaining lane line image information of the lane where the target vehicle is located, and determining the lateral distance information of the target vehicle relative to the lane line according to the lane line image information;

[0061] In this step, a camera (usually a front camera) installed on the target vehicle can be used to collect road images in real time, detect lane lines, and determine relevant description information of the lane lines, such as the position information of the lane lines in the world coordinate system. At the same time, the relevant description information of the lane lines can be obtained from the high-precision electronic map, and the lane line description information obtained by visual detection and the lane line description information obtained by the high-precision electronic map can be matched to determine the precise lateral position of the target vehicle relative to the current lane line.

[0062] S104, calculating the third positioning data of the target vehicle at the current moment according to the first positioning data and the second positioning data;

[0063] In this step, the first positioning data and the second positioning data can be fused to calculate the third positioning data of the target vehicle at the current moment, which may include the position data, heading angle data, speed data, angular velocity data, and acceleration data of the target vehicle. By fusing the first positioning data and the second positioning data, the shortcomings of the navigation satellite positioning method that it is easy to lose signals and lose navigation capabilities in tunnels or other obstructed areas, and the shortcomings of the inertial measurement unit positioning that relies on track calculation and can only provide position information relative to a known starting point, rather than absolute geographic coordinates, can be overcome.

[0064] S105. Correct the third positioning data according to the lateral distance information to obtain fourth positioning data of the target vehicle at the current moment.

[0065] Since the method of determining the lateral distance information of the target vehicle relative to the lane line based on the lane line image information utilizes the environmental characteristics around the target vehicle, the positioning accuracy is relatively high. The lateral distance information can be used to correct the data of each dimension in the third positioning data obtained in the previous step to improve the positioning accuracy. At the same time, combined with the navigation satellite positioning method and the inertial measurement unit positioning method, the problem of unstable output signal frequency of the environmental feature matching positioning method is compensated.

[0066] The method provided in this embodiment achieves the following technical effects:

[0067] The method calculates the first motion state information of the target vehicle through the measurement data of the navigation satellite, calculates the second motion state information of the target vehicle through the measurement data of the inertial measurement unit, and calculates the positioning information of the target vehicle by fusing the first motion state information and the second motion state information. The positioning information is then corrected by the lateral distance of the target vehicle relative to the lane line of the current lane. The method combines the three navigation and positioning methods of satellite navigation, inertial navigation, and environmental feature matching, overcomes the defects of a single positioning method, and has the advantages of high positioning accuracy, stable operation, and high frequency of positioning information update.

[0068] The vehicle positioning method of the present application is described in detail using a specific embodiment below.

[0069] Embodiment 2

[0070] Figure 2 Schematic diagram of the vehicle positioning method provided in the embodiment of the present application Figure 2 .like Figure 2 As shown, the method includes:

[0071] S201, calculating first positioning data by a filtering algorithm according to the measurement data of the navigation satellite at the current moment, the measurement data of the inertial measurement unit of the target vehicle at the current moment, and the positioning data of the target vehicle at the current moment predicted by the filtering algorithm at the previous moment;

[0072] Specifically, in this step, the first positioning data at the current moment may be solved by using a filtering algorithm in combination with the measurement data of the navigation satellite at the current moment and the measurement data of the inertial measurement unit of the target vehicle.

[0073] In the embodiment of the present application, the filtering algorithm can be a Kalman filter algorithm, a particle filter algorithm, an extended Kalman filter algorithm, etc. Taking the Kalman filter algorithm as an example, in the prediction step of the previous moment, the Kalman filter uses the state transition model of the target vehicle to predict the state of the target vehicle at the current moment (the positioning data of each dimension of the target vehicle, such as position, heading angle, etc., can be regarded as the state of the target vehicle) and the covariance matrix, and in the update step of the current moment, the Kalman filter uses the measurement data at the current moment, that is, the measurement data of the navigation satellite at the current moment and the measurement data of the inertial measurement unit of the target vehicle at the current moment, to update the state of the target vehicle, thereby obtaining the first positioning data, and updating the covariance matrix at the same time. The specific calculation steps of the Kalman filter are not repeated in this application. Figure 3 A schematic diagram of combined navigation and positioning based on local Kalman filtering provided in an embodiment of the present application is shown in FIG. Figure 3As shown, after the measurement data of the navigation satellite at the current moment and the measurement data of the inertial measurement unit of the target vehicle are solved by the Kalman filter, the output first positioning data may include the time data at the current moment, the longitude data and latitude data of the target vehicle, the altitude data of the target vehicle, the heading angle data of the target vehicle, the speed data and acceleration data of the target vehicle in the northeast celestial coordinate system, and the first positioning data is sent to the global Kalman filter fusion positioning module.

[0074] The use of filtering algorithms can provide high-precision state estimation. Compared with the measurement data of the navigation satellite at the current moment and the measurement data of the inertial measurement unit of the target vehicle at the current moment, the target vehicle positioning data obtained by the filtering algorithm has higher accuracy. At the same time, this step combines the navigation satellite positioning and the inertial measurement unit positioning method, combines the absolute positioning capability of the navigation satellite and the high-frequency motion detection capability of the IMU, thereby achieving high-precision and high-frequency vehicle positioning, and the first positioning data finally given includes the absolute geographic coordinates such as the latitude and longitude of the vehicle.

[0075] S202, calculating second positioning data by a filtering algorithm according to the measurement data of the inertial measurement unit of the target vehicle at the current moment and the positioning data of the target vehicle at the current moment predicted by the filtering algorithm at the previous moment;

[0076] Specifically, by acquiring the measurement data of each sensor of the inertial measurement unit of the target vehicle, the second positioning data of the target vehicle at the current moment is solved by a filtering algorithm.

[0077] Taking the Kalman filter algorithm as an example, Figure 4 A schematic diagram of an inertial measurement unit positioning based on a local Kalman filter provided in an embodiment of the present application, such as Figure 4 As shown, the second positioning data may include time data, as well as position data, speed data, altitude data, heading angle data, angular velocity data, and acceleration data of the target vehicle. Finally, the second positioning data is sent to the global Kalman filter fusion positioning module.

[0078] In this step, the measurement data of each sensor of the inertial measurement unit of the target vehicle is solved by a filtering algorithm, so that the second positioning data has a higher accuracy than the measurement data of each sensor of the inertial measurement unit of the target vehicle.

[0079] S203, calculating third positioning data by a filtering algorithm based on the first positioning data, the second positioning data, and the positioning data of the target vehicle at the current moment predicted by the filtering algorithm at the previous moment;

[0080] Taking the Kalman filter algorithm as an example, specifically, the global Kalman filter fusion positioning module can adopt an event-triggered fusion mechanism. When the first positioning data and the second positioning data enter the module, Kalman filtering is used to perform data fusion positioning and output the third positioning data. The third positioning data may include the position information, heading angle information, speed information, angular velocity information, and acceleration information of the target vehicle.

[0081] Since the first positioning data and the second positioning data are both obtained by filtering, at this time, the first positioning data and the second positioning data are filtered again. By using a multi-level filtering algorithm, the accuracy of the third positioning data can be further improved.

[0082] S204, obtaining lane line image information of the lane where the target vehicle is located, and determining the lateral distance information of the target vehicle relative to the lane line according to the lane line image information;

[0083] Optionally, this embodiment provides a method for determining lateral distance information. Figure 5 A schematic flow chart of a method for determining lateral distance information provided in an embodiment of the present application is shown in FIG. Figure 5 As shown, the method includes:

[0084] S501, determining first description information of the lane line according to the lane line image information;

[0085] Specifically, in this step, the lane line can be detected using an image detection algorithm, and the first description information of the lane line can be calculated using the camera model of the target vehicle camera, and the first description information includes the position of the lane line in the world coordinate system. The camera model includes the camera internal and external parameter matrix, and the camera model can be used to convert the points in the lane line image into the world coordinate system.

[0086] S502: Obtain second description information of the lane line from the map according to the third positioning data;

[0087] Specifically, a high-precision electronic map in the Open-DRIVE format may be used, which includes information such as road definitions, lane lines, intersections, traffic signals, and lane rules. Through the third positioning data of the target vehicle, the lane line near the target vehicle, that is, the second description information of the lane line detected by the previous image, can be estimated through the electronic map, and the second description information may be feature information of the lane line.

[0088] S503: Match the first description information with the second description information to determine horizontal distance information.

[0089] In the target vehicle coordinate system, the first description information is matched with the second description information, and the rotation and translation between the lane line obtained by image detection and the lane line in the high-precision electronic map are calculated to determine the lateral distance information of the target vehicle relative to the lane line. In this step, by combining the high-precision electronic map and the image detection algorithm, accurate lateral distance information can be determined for subsequent correction steps.

[0090] S205. Calculate the corrected position information, corrected heading angle information, corrected speed information, corrected angular velocity information, and corrected acceleration information in the fourth positioning data respectively based on the lateral distance information and the position information, heading angle information, speed information, angular velocity information, and acceleration information of the target vehicle in the third positioning data.

[0091] Taking the Kalman filter algorithm as an example, Figure 6 The fusion positioning schematic diagram provided in the embodiment of the present application is as follows: Figure 6 As shown, the combined navigation positioning module outputs the first positioning data to the global Kalman filter fusion positioning module through the local Kalman filter, the inertial measurement unit positioning module outputs the second positioning data to the global Kalman filter fusion positioning module through the local Kalman filter, the global Kalman filter fusion positioning module fuses and solves the first positioning data and the second positioning data through the Kalman filter, and outputs the third positioning data, the lane line matching positioning module determines the lateral distance information of the target vehicle relative to the lane line according to the third positioning data, performs low-pass filtering on the lateral distance information, and corrects the third positioning data to obtain the fourth positioning data.

[0092] In this embodiment, the purpose of low-pass filtering the third positioning data is to overcome the problem of unstable frequency of the output signal of the lane matching module and provide stability and smoothness of the output lateral distance information. Specifically, the following formula can be used for low-pass filtering:

[0093] Y(n)=αX(n)+(1-α)Y(n-1)

[0094] in,

[0095] α is the filter coefficient; X(n) is the current sampling value; Y(n-1) is the previous filter output value; Y(n) is the current filter output value.

[0096] The embodiment of the present invention can divide the electronic device or the main control device into functional modules according to the above method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present invention is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0097] Figure 7 This is a schematic diagram of the structure of the vehicle positioning device provided in the embodiment of the present application. Figure 7 As shown, the device 70 includes:

[0098] The satellite positioning module 701 is used to calculate the first positioning data of the target vehicle at the current moment according to the measurement data of the navigation satellite at the current moment;

[0099] The inertial measurement unit positioning module 702 is used to: calculate the second positioning data of the target vehicle at the current moment according to the measurement data of the inertial measurement unit of the target vehicle at the current moment;

[0100] The lane line positioning module 703 is used to: obtain the lane line image information of the lane where the target vehicle is located, and determine the lateral distance information of the target vehicle relative to the lane line according to the lane line image information;

[0101] The fusion positioning module 704 is used to:

[0102] Calculating the third positioning data of the target vehicle at the current moment according to the first positioning data and the second positioning data;

[0103] The third positioning data is corrected according to the lateral distance information to obtain the fourth positioning data of the target vehicle at the current moment.

[0104] The vehicle positioning device provided in this embodiment can execute the vehicle positioning method of the above embodiment, and its implementation principle and technical effect are similar, which will not be described in detail in this embodiment.

[0105] In the specific implementation of the aforementioned vehicle positioning device, each module may be implemented as a processor, and the processor may execute computer-executable instructions stored in the memory, so that the processor executes the aforementioned vehicle positioning method.

[0106] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 8 As shown, the electronic device 80 includes: at least one processor 801 and a memory 802. The electronic device 80 also includes a communication component 803. The processor 801, the memory 802 and the communication component 803 are connected via a bus 804.

[0107] In a specific implementation process, at least one processor 801 executes the computer-executable instructions stored in the memory 802, so that at least one processor 801 executes the above-mentioned vehicle positioning method.

[0108] The specific implementation process of the processor 801 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0109] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0110] The memory may include a high-speed RAM memory, and may also include a non-volatile storage NVM, such as at least one disk storage.

[0111] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0112] The above-mentioned functions implemented by the electronic device and the main control device introduce the scheme provided by the embodiment of the present invention. It can be understood that in order to implement the above-mentioned functions, the electronic device or the main control device includes a hardware structure and / or software module corresponding to the execution of each function. In combination with the units and algorithm steps of each example described in the embodiment disclosed in the embodiment of the present invention, the embodiment of the present invention can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the technical solution of the embodiment of the present invention.

[0113] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above-mentioned vehicle positioning method is implemented.

[0114] The computer-readable storage medium mentioned above can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0115] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in an electronic device or a main control device.

[0116] The present application also provides a computer program product, which includes: a computer program, which is stored in a readable storage medium. At least one processor of an electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device executes the solution provided by any of the above embodiments.

[0117] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A vehicle positioning method, characterized in that: The method comprises: Calculating first positioning data of the target vehicle at the current moment according to the measurement data of the navigation satellite at the current moment; Calculating second positioning data of the target vehicle at the current moment according to the measurement data of the inertial measurement unit of the target vehicle at the current moment; Acquire lane line image information of the lane where the target vehicle is located, and determine the lateral distance information of the target vehicle relative to the lane line according to the lane line image information; Calculating the third positioning data of the target vehicle at the current moment according to the first positioning data and the second positioning data; The third positioning data is corrected according to the lateral distance information to obtain the fourth positioning data of the target vehicle at the current moment.

2. The method according to claim 1, characterized in that The step of calculating the first positioning data of the target vehicle at the current moment according to the measurement data of the navigation satellite at the current moment comprises: The first positioning data is calculated according to the measurement data of the navigation satellite at the current moment and the measurement data of the inertial measurement unit of the target vehicle at the current moment.

3. The method according to claim 2, characterized in that The calculating the first positioning data according to the measurement data of the navigation satellite at the current moment and the measurement data of the inertial measurement unit of the target vehicle at the current moment comprises: The first positioning data is calculated by the filtering algorithm based on the measurement data of the navigation satellite at the current moment, the measurement data of the inertial measurement unit of the target vehicle at the current moment, and the positioning data of the target vehicle at the current moment predicted by the filtering algorithm at the previous moment.

4. The method according to claim 3, characterized in that: The step of calculating the second positioning data of the target vehicle at the current moment according to the measurement data of the inertial measurement unit of the target vehicle at the current moment comprises: The second positioning data is calculated by the filtering algorithm according to the measurement data of the inertial measurement unit of the target vehicle at the current moment and the positioning data of the target vehicle at the current moment predicted by the filtering algorithm at the previous moment.

5. The method according to claim 4, characterized in that The step of calculating the third positioning data of the target vehicle at the current moment according to the first positioning data and the second positioning data includes: The third positioning data is calculated by the filtering algorithm according to the first positioning data, the second positioning data, and the positioning data of the target vehicle at the current moment predicted by the filtering algorithm at the previous moment.

6. The method according to claim 1, characterized in that The acquiring lane line image information of the lane where the target vehicle is located, and determining the lateral distance information of the target vehicle relative to the lane line according to the lane line image information, comprises: Determining first description information of the lane line according to the lane line image information; Acquire second description information of the lane line from a map according to the third positioning data; The first description information is matched with the second description information to determine the lateral distance information.

7. The method according to claim 1, characterized in that The method of correcting the third positioning data according to the lateral distance information to obtain the fourth positioning data of the target vehicle at the current moment includes: According to the lateral distance information, as well as the position information, heading angle information, speed information, angular velocity information, and acceleration information of the target vehicle in the third positioning data, the corrected position information, corrected heading angle information, corrected speed information, corrected angular velocity information, and corrected acceleration information in the fourth positioning data are calculated respectively.

8. A vehicle positioning device, characterized in that: The device comprises: The satellite positioning module is used to calculate the first positioning data of the target vehicle at the current moment according to the measurement data of the navigation satellite at the current moment; An inertial measurement unit positioning module, used to: calculate the second positioning data of the target vehicle at the current moment according to the measurement data of the inertial measurement unit of the target vehicle at the current moment; The lane line positioning module is used to obtain the lane line image information of the lane where the target vehicle is located, and determine the lateral distance information of the target vehicle relative to the lane line according to the lane line image information; Fusion positioning module for: Calculating the third positioning data of the target vehicle at the current moment according to the first positioning data and the second positioning data; The third positioning data is corrected according to the lateral distance information to obtain the fourth positioning data of the target vehicle at the current moment.

9. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the vehicle positioning method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the vehicle positioning method according to any one of claims 1 to 7.

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