Vehicle positioning anomaly detection method, device, equipment, medium and program

Abstract

The invention relates to the technical field of vehicles, in particular to a vehicle positioning anomaly detection method and device, equipment, a medium and a program, and the method comprises the steps: obtaining image data in a vehicle driving process; vehicle motion parameters in a three-dimensional coordinate system with the vehicle body center point as the center are analyzed according to the image data; comparing the motion parameters with corresponding parameters output by a vehicle sensor in real time to judge whether vehicle positioning is abnormal or not; and if the vehicle positioning is abnormal, controlling the vehicle to switch from the current positioning mode to the target positioning mode. Therefore, the problems of low working efficiency, poor real-time performance, poor user experience and the like caused by inaccurate positioning due to the fact that vehicle positioning is easily influenced by factors such as signals and the like in related technologies and abnormal root causes are determined by manually comparing map matching tracks mostly after abnormal positioning is found are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicles, in particular to a vehicle positioning anomaly detection method, device, equipment, medium and program. BACKGROUND

[0002] At present, there are mainly two navigation positioning methods used on the vehicle machine. The first method is to rely only on the positioning signal of GPS: the position is determined through the latitude and longitude information, but the GPS positioning signal will be affected by natural factors such as buildings, and there will be a situation of poor signal quality, which will lead to inaccurate positioning. The second method is to use the vehicle body sensor to perform dead reckoning as an auxiliary to GPS information, and to use the parameter information transmitted by the vehicle body to fuse the positioning position. The main parameter information includes the change angle of the x, y and z axes of the gyroscope and accelerometer sensor, the vehicle speed information, and the turning direction of the vehicle is determined by the angle of gyroscope deflection, and the driving speed of the vehicle is determined by the change of the vehicle speed. However, if the vehicle body parameters are inaccurate, it will trigger extreme inaccuracy of navigation positioning, and it is inevitable to cause irreparable loss.

[0003] However, in terms of handling the positioning deviation problem, after discovering the positioning anomaly, the data is uploaded to the cloud, and the vehicle machine log is pulled remotely in the future. Manual analysis and confirmation cannot confirm whether the deviation is caused by human factors or abnormal reasons caused by vehicle body parameter abnormalities, which leads to insufficient timeliness in problem handling, and also causes missed judgment and misjudgment phenomenon, low work efficiency, poor real-time performance, and poor user experience. SUMMARY

[0004] The present application provides a vehicle positioning anomaly detection method, device, equipment, medium and program to solve the problem that the vehicle positioning is easily affected by signal factors in related technologies, leading to inaccurate positioning, and after discovering the positioning anomaly, the abnormal root cause is mostly confirmed by manual comparison of map matching tracks, leading to low work efficiency, poor real-time performance, poor user experience and other problems.

[0005] The first aspect embodiment of the present application provides a vehicle positioning anomaly detection method, comprising the following steps: acquiring image data in the driving process of a vehicle; analyzing vehicle motion parameters in a three-dimensional coordinate system with a vehicle body center point as the center according to the image data; comparing the motion parameters with corresponding parameters output by a vehicle sensor in real time to determine whether the vehicle positioning is abnormal; and if the vehicle positioning is abnormal, switching the vehicle from the current positioning mode to the target positioning mode.

[0006] Optionally, the analyzing vehicle motion parameters in a three-dimensional coordinate system with a vehicle body center point as the center according to the image data comprises: extracting key features in the image data; and identifying vehicle motion parameters in a three-dimensional coordinate system with a vehicle body center point as the center according to the key features.

[0007] Optionally, the identifying the vehicle motion parameters in a three-dimensional coordinate system with the vehicle body center point as the center according to the key features comprises: identifying a first action change amount of the vehicle in a first target axis direction, a second action change amount of the vehicle in a second target axis direction, and a third action change amount of the vehicle in a third target axis direction in the key features; and generating the vehicle motion parameters by calculating the angle change amount and the acceleration in different dimensions through a motion trajectory tracking algorithm according to the first action change amount to the third action change amount.

[0008] Optionally, the motion parameters comprise a pitch angle change amount and a pitch acceleration around the first target axis, a roll angle change amount and a roll acceleration around the second target axis, and a yaw angle change amount and a yaw acceleration around the third target axis.

[0009] Optionally, the comparing the motion parameters with the corresponding parameters output in real time by the vehicle sensors to determine whether the vehicle positioning is abnormal comprises: if a deviation value of the motion parameters and the corresponding parameters output in real time by the vehicle sensors is lower than a preset threshold, determining that the vehicle positioning is normal, otherwise, determining that the vehicle positioning is abnormal.

[0010] Optionally, the method further comprises: continuously monitoring a deviation value of the vehicle body sensor data and the image analysis result, and automatically switching back to the current positioning mode when the deviation value returns to within a preset threshold.

[0011] The second aspect embodiment of the present application provides a vehicle positioning abnormality detection device, comprising the following steps: an acquisition module configured to acquire image data in a vehicle driving process; an analysis module configured to analyze vehicle motion parameters in a three-dimensional coordinate system with a vehicle body center point as the center according to the image data; a comparison module configured to compare the motion parameters with corresponding parameters output in real time by vehicle sensors to determine whether the vehicle positioning is abnormal; and a control module configured to control the vehicle to switch from a current positioning mode to a target positioning mode if the vehicle positioning is abnormal.

[0012] The third aspect embodiment of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to perform the vehicle positioning abnormality detection method as described in the above embodiments.

[0013] The fourth aspect embodiment of the present application provides a computer readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to perform the vehicle positioning abnormality detection method as described in the above embodiments.

[0014] The fifth aspect embodiment of the present application provides a computer program product comprising a computer program or instructions, wherein the computer program or instructions are executed to implement the vehicle positioning abnormality detection method as described in the above embodiments.

[0015] Therefore, the present application has at least the following beneficial effects:

[0016] The embodiment of the present application can use the camera to capture the picture in real time during the driving process of the vehicle, analyze the vehicle motion parameters of the vehicle in the three-dimensional coordinate system based on the processed image, including the change angle and change acceleration around the x-axis, y-axis and z-axis, compare the vehicle motion parameters obtained from the image with the data output by the vehicle sensor, and if there is a significant difference between the two, it indicates that the vehicle may have a positioning abnormality. Once it is confirmed that the vehicle positioning is abnormal, the system will automatically stop using the current fusion positioning method, avoid the navigation deviation caused by the wrong vehicle body parameters, effectively improve the detection ability and working efficiency of the vehicle in the real-time navigation process for the positioning abnormality, ensure the real-time, accuracy and reliability of the positioning information, take timely measures when the positioning abnormality is found, reduce the potential risks caused by delayed correction, and improve the user experience.

[0017] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0018] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description, including the accompanying drawings, wherein:

[0019] Figure 1 A flowchart of a vehicle positioning abnormality detection method according to an embodiment of the present application is provided;

[0020] Figure 2 A schematic diagram of the x, y, z axes of the vehicle body gyroscope according to the embodiment of the present application is provided;

[0021] Figure 3 A schematic diagram of the image acquisition and analysis process according to the embodiment of the present application is provided;

[0022] Figure 4 A schematic diagram of the positioning method switching logic according to the embodiment of the present application is provided;

[0023] Figure 5 A block schematic diagram of a vehicle positioning abnormality detection device according to the embodiment of the present application is provided;

[0024] Figure 6 A schematic diagram of the structure of an electronic device according to the embodiment of the present application is provided. DETAILED DESCRIPTION

[0025] Embodiments of the present application are described below in detail, examples of which are shown in the accompanying drawings, in which the same or similar notations represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by reference to the drawings are exemplary and are intended to explain the present application, and cannot be understood as limiting the present application.

[0026] A vehicle positioning anomaly detection method, device, equipment, medium and program of embodiments of the present application are described below with reference to the accompanying drawings.

[0027] Specifically, Figure 1 A flowchart of a vehicle positioning anomaly detection method provided by embodiments of the present application.

[0028] As Figure 1 indicated, the vehicle positioning anomaly detection method includes the following steps:

[0029] In step S101, image data in a vehicle driving process is acquired.

[0030] It can be understood that the camera can be used to capture the picture in the vehicle driving process in real time, so as to analyze the vehicle motion parameters in the three-dimensional coordinate system with the vehicle body center point as the center in the subsequent process.

[0031] In step S102, the vehicle motion parameters in the three-dimensional coordinate system with the vehicle body center point as the center are analyzed according to the image data.

[0032] The motion parameters include the pitch angle change amount and the pitch acceleration around the first target axis, the roll angle change amount and the roll acceleration around the second target axis, and the yaw angle change amount and the yaw acceleration around the third target axis.

[0033] It can be understood that the embodiments of the present application can know the change of the vehicle in the height direction, the left-right direction and the driving direction through the analysis of the image data, so as to quickly identify whether the vehicle parameters are abnormal in the subsequent process.

[0034] In the embodiments of the present application, the vehicle motion parameters in the three-dimensional coordinate system with the vehicle body center point as the center are analyzed according to the image data, including: extracting key features in the image data; identifying the vehicle motion parameters in the three-dimensional coordinate system with the vehicle body center point as the center according to the key features.

[0035] It can be understood that the embodiments of the present application can identify the change angle and the change acceleration of the vehicle around the x-axis, the y-axis and the z-axis based on the extracted key features, so as to obtain the accurate motion parameters of the vehicle in the three-dimensional coordinate system, which not only can significantly improve the accuracy of vehicle positioning, but also can timely find and correct the problems that may occur in the positioning process.

[0036] In the embodiment of the present application, the vehicle motion parameters in a three-dimensional coordinate system with the center point of the vehicle body as the center are identified according to the key features, including: identifying the first action change amount of the vehicle in the first target axis direction, the second action change amount in the second target axis direction, and the third action change amount in the third target axis direction in the key features; and generating the vehicle motion parameters by calculating the angle change amount and acceleration in different dimensions through the motion trajectory tracking algorithm according to the first action change amount to the third action change amount.

[0037] It can be understood that, based on the action change amounts in the first target axis direction, the second target axis direction, and the third target axis direction, the angle change amount and acceleration in different dimensions can be calculated in the embodiment of the present application, so as to provide more reliable motion parameters for the vehicle and improve the fusion positioning accuracy.

[0038] It should be noted that the first target axis is the X axis, the second target axis is the Y axis, and the third target axis is the Z axis, which are not specifically limited.

[0039] In step S103, it is determined whether the vehicle positioning is abnormal by comparing the motion parameters with the corresponding parameters output in real time by the vehicle sensor.

[0040] It can be understood that, by comparing the image analysis result with the sensor data, the positioning deviation caused by the sensor error can be effectively identified and corrected in the embodiment of the present application, which not only enhances the accuracy and reliability of the vehicle positioning system, but also improves the ability to respond to unexpected situations, thereby providing a smoother and more reliable navigation experience for users.

[0041] In the embodiment of the present application, it is determined whether the vehicle positioning is abnormal by comparing the motion parameters with the corresponding parameters output in real time by the vehicle sensor, including: if the deviation value of the motion parameters from the corresponding parameters output in real time by the vehicle sensor is lower than a preset threshold, it is determined that the vehicle positioning is normal, otherwise it is determined that the vehicle positioning is abnormal.

[0042] The preset threshold can be set according to actual needs, which is not specifically limited.

[0043] It can be understood that, by carefully comparing the motion parameters with the sensor data, the high precision and high reliability of the vehicle positioning are ensured in the embodiment of the present application, and the reaction speed and the ability to respond to unexpected situations of the system are also improved, thereby providing a safer and smoother driving experience for users.

[0044] In step S104, if the vehicle positioning is abnormal, the vehicle is controlled to switch from the current positioning mode to a target positioning mode.

[0045] The target positioning mode can be GPS positioning, which is not specifically limited.

[0046] It can be understood that the embodiment of the present application can immediately take measures to switch the positioning mode once it is confirmed that there is an abnormality in the vehicle positioning, which can effectively correct the position deviation and prevent further navigation errors. It can not only respond quickly when positioning abnormalities are detected, ensuring the continuity and accuracy of navigation, but also improve the reliability of the overall system and user satisfaction.

[0047] In an embodiment of the present application, it also includes: continuously monitoring the deviation value between the vehicle body sensor data and the image analysis result, and automatically switching back to the current positioning mode when the deviation value returns to within a preset threshold.

[0048] It can be understood that the embodiments of the present application can maximize the advantages of various types of data by continuously monitoring and adjusting the positioning mode in a timely manner, thereby improving navigation accuracy as much as possible while ensuring safety, thereby achieving the goal of improving system stability and adaptability while ensuring navigation accuracy.

[0049] According to the vehicle positioning anomaly detection method proposed in the embodiment of the present application, the image of the vehicle during driving is captured in real time. Based on the processed image, the vehicle motion parameters of the vehicle in the three-dimensional coordinate system are analyzed, including the change angle and change acceleration around the x-axis, y-axis and z-axis. The vehicle motion parameters analyzed from the image are compared with the data output by the vehicle sensor. If there is a significant difference between the two, it indicates that the vehicle may have a positioning anomaly. Once it is confirmed that the vehicle positioning is abnormal, the system will automatically stop using the current fusion positioning method to avoid navigation deviations caused by incorrect vehicle body parameters, effectively improving the detection capability and work efficiency of positioning anomalies during real-time vehicle navigation, ensuring the real-time, accuracy and reliability of positioning information, taking timely measures when positioning anomalies are found, reducing potential risks caused by delayed corrections, and improving the user experience.

[0050] The following will be combined Figure 2-4 The vehicle positioning anomaly detection method of this application is described in detail, and the specific steps are as follows:

[0051] Step 1: Use the camera to capture the driving picture in real time, analyze and record the image changes at specific time points in real time, and analyze the vehicle's Figure 2 The changes in the x, y, and z directions are shown.

[0052] (1) Determine the angle and acceleration of the vehicle around the x-axis. The resulting values ​​are temporarily named x1 and x2. The positive direction of the x-axis points to the right side of the vehicle. Because the vehicle pitches when rotating around the x-axis, it is also called the pitch axis.

[0053] (2) Determine the angle of the vehicle around the y-axis and the change acceleration, the resulting value is temporarily named y1, y2. The positive direction of the y-axis points to the front of the vehicle, because when rotating around the y-axis, the vehicle is doing the action of rolling, so it is also called the roll axis;

[0054] (3) Determine the angle of the vehicle around the z-axis and the change acceleration, the resulting value is temporarily named z1, z2. The positive direction of the z-axis points to the top of the vehicle, because when rolling around the Z-axis, the vehicle is doing the action of yaw, so it is also called the yaw axis;

[0055] (4) It should be noted that: image recognition is mainly based on the picture taken by the camera, first, the image is denoised, transformed and smoothed, etc. Operation makes the image features more obvious; second, feature extraction and selection, identify the important features of the image, exclude irrelevant information; finally, compare the changes of image features in each dimension, and then get the result, such as Figure 3 .

[0056] Step 2, the data analyzed by the image recognition in the first step, compare the accelerometer sensor and gyroscope sensor of the vehicle body at the same time, the comparison criteria are:

[0057] (1) The data obtained in the first step, x1, y1, z1 are compared with the x, y, z axis data of the gyroscope respectively to see if they are equal;

[0058] (2) The data obtained in the first step, x2, y2, z2 are compared with the x, y, z axis data of the accelerometer respectively to see if they are equal;

[0059] Step 3, the vehicle speed data analyzed by the image recognition in the first step is compared with the vehicle speed transmitted by the vehicle.

[0060] Step 4, after the comparison of the above steps 2 and 3, it can be confirmed whether the data transmitted by the vehicle body is correct, if abnormal is found, stop these data from participating in the fusion positioning calculation in time, switch to pure GPS positioning mode, such as Figure 4 .

[0061] In summary, this application uses a camera to capture driving images and analyze image changes to derive changes in the vehicle in three dimensions: height, left and right, and driving direction. This includes but is not limited to identifying the vehicle's bumps, forward or backward movement, and turning. The parameters transmitted by the vehicle body are compared to determine the accuracy of the data transmitted by the vehicle body. If an abnormality is found in the vehicle body parameters, the current positioning method is no longer used, and the positioning method that only uses GPS location information is switched to. By combining the vehicle body camera to capture road images and analyze images, the current road changes can be effectively identified, compared with the vehicle body parameters at the time, and the accuracy of the vehicle body parameters can be effectively identified. In terms of fusion positioning, this solution provides an effective positioning abnormality calibration method, which can improve the yaw problem of real-time vehicle navigation.

[0062] Next, the vehicle positioning anomaly detection device proposed in accordance with an embodiment of the present application will be described with reference to the accompanying drawings.

[0063] Figure 5 4 is a block diagram of a vehicle positioning anomaly detection device according to an embodiment of the present application.

[0064] like Figure 5 As shown, the vehicle positioning anomaly detection device 10 includes: an acquisition module 100, an analysis module 200, a comparison module 300 and a control module 400.

[0065] Among them, the acquisition module 100 is used to acquire image data during the vehicle's driving process; the analysis module 200 is used to analyze the vehicle motion parameters in a three-dimensional coordinate system centered on the vehicle body center point based on the image data; the comparison module 300 is used to compare the motion parameters with the corresponding parameters output by the vehicle sensor in real time to determine whether the vehicle positioning is abnormal; the control module 400 is used to control the vehicle to switch from the current positioning mode to the target positioning mode if the vehicle positioning is abnormal.

[0066] According to the vehicle positioning anomaly detection device proposed in the embodiment of the present application, the image of the vehicle during driving is captured in real time. Based on the processed image, the vehicle motion parameters of the vehicle in the three-dimensional coordinate system are analyzed, including the change angle and change acceleration around the x-axis, y-axis and z-axis. The vehicle motion parameters analyzed from the image are compared with the data output by the vehicle sensor. If there is a significant difference between the two, it indicates that the vehicle may have a positioning anomaly. Once it is confirmed that the vehicle positioning is abnormal, the system will automatically stop using the current fusion positioning method to avoid navigation deviations caused by incorrect vehicle body parameters, effectively improving the detection capability and work efficiency of positioning anomalies during real-time vehicle navigation, ensuring the real-time, accuracy and reliability of positioning information, taking timely measures when positioning anomalies are found, reducing potential risks caused by delayed corrections, and improving the user experience.

[0067] Figure 6A structural schematic diagram of an electronic device provided in an embodiment of the present application is shown. The electronic device can include

[0068] The memory 601, the processor 602 and the computer program stored in the memory 601 and executable on the processor 602.

[0069] The processor 602 implements the vehicle positioning anomaly detection method provided in the above embodiments when executing the program.

[0070] Further, the electronic device further includes

[0071] The communication interface 603 is used for communication between the memory 601 and the processor 602.

[0072] The memory 601 is used for storing the computer program executable on the processor 602.

[0073] The memory 601 can include a high-speed RAM memory, and can also include a non-volatile memory, for example, at least one disk memory.

[0074] If the memory 601, the processor 602 and the communication interface 603 are independently implemented, the communication interface 603, the memory 601 and the processor 602 can be connected to each other through a bus and complete communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0075] Optionally, in a specific implementation, if the memory 601, the processor 602 and the communication interface 603 are integrated on a chip, the memory 601, the processor 602 and the communication interface 603 can complete communication between each other through an internal interface.

[0076] The processor 602 can be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to perform the operations of the embodiments of the present application.

[0077] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program or instructions, and the computer program or instructions are executed by a processor to implement the vehicle positioning anomaly detection method.

[0078] The embodiments of the present application also provide a computer program product, which includes a computer program or instructions, and the computer program or instructions are executed by a processor to implement the vehicle positioning anomaly detection method.

[0079] In the description of the present application, the description of the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present application, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or N embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the present application and the features of the different embodiments or examples without contradiction.

[0080] In addition, the terms "first", "second" are only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically limited.

[0081] Any process or method descriptions in flow charts or described elsewhere herein can be understood as representing code modules, segments, or portions of code which include one or more executable instructions for performing steps associated with the functions described in the flow charts or elsewhere herein, and that the various embodiments of preferred implementations can include memory that stores programs of instructions (e.g., as carrier waves) that, when executed by a machine, such as a processor, can cause the machine to perform a process described in or with respect to the flow charts. The software can be stored on one or more machine readable media, such as a hard disk, optical disk, or other storage device.

[0082] It should be understood that portions of the application can be implemented in hardware, software, firmware, or combinations thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As such, if implemented in hardware, and in another embodiment, the hardware can include any or a combination of the following technologies, which are all well known in the art: a discrete logic circuit having logic gates for implementing logic functions upon an application of data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), and / or the like.

[0083] Those skilled in the art can understand that all or part of the steps carried out by the above-mentioned embodiment methods can be completed by programs instructing related hardware, and the programs can be stored in a computer readable storage medium. When the programs are executed, one or a combination of the steps of the method embodiments is included.

Claims

1. A vehicle positioning anomaly detection method, characterized in that: The following steps are involved: Acquire image data during vehicle driving; Analyzing the vehicle motion parameters in a three-dimensional coordinate system centered on the center point of the vehicle body according to the image data; Comparing the motion parameters with corresponding parameters output in real time by vehicle sensors to determine whether the vehicle positioning is abnormal; If the vehicle positioning is abnormal, the vehicle is controlled to switch from the current positioning mode to the target positioning mode.

2. The vehicle positioning anomaly detection method according to claim 1, characterized in that: The analyzing the vehicle motion parameters in a three-dimensional coordinate system centered on the vehicle body center point according to the image data includes: extracting key features from the image data; The vehicle motion parameters in a three-dimensional coordinate system centered on the center point of the vehicle body are identified according to the key features.

3. The vehicle positioning anomaly detection method according to claim 2, characterized in that: The identifying, based on the key features, vehicle motion parameters in a three-dimensional coordinate system centered on the vehicle body center point includes: Identifying a first motion change amount of the vehicle in a first target axis direction, a second motion change amount in a second target axis direction, and a third motion change amount in a third target axis direction in key features; According to the first motion change amount to the third motion change amount, the vehicle motion parameters are generated by calculating the angle change amounts and accelerations in different dimensions through a motion trajectory tracking algorithm.

4. The vehicle positioning anomaly detection method according to claim 3, characterized in that: The motion parameters include a pitch angle change and a pitch acceleration around a first target axis, a roll angle change and a roll acceleration around a second target axis, and a yaw angle change and a yaw acceleration around a third target axis.

5. The vehicle positioning anomaly detection method according to claim 1, characterized in that: The comparing the motion parameters with corresponding parameters output in real time by the vehicle sensor to determine whether the vehicle positioning is abnormal includes: If the deviation value between the motion parameter and the corresponding parameter output by the vehicle sensor in real time is lower than a preset threshold, the vehicle positioning is determined to be normal; otherwise, the vehicle positioning is determined to be abnormal.

6. The vehicle positioning anomaly detection method according to claim 5, characterized in that: Also includes: Continuously monitor the deviation between the vehicle body sensor data and the image analysis results. When the deviation value returns to within the preset threshold, it automatically switches back to the current positioning mode.

7. A vehicle positioning anomaly detection device, characterized in that: The following steps are involved: An acquisition module, used for acquiring image data during the vehicle's driving process; An analysis module, configured to analyze vehicle motion parameters in a three-dimensional coordinate system centered on a vehicle body center point based on the image data; A comparison module, configured to compare the motion parameters with corresponding parameters outputted in real time by vehicle sensors to determine whether the vehicle positioning is abnormal; The control module is used to control the vehicle to switch from the current positioning mode to the target positioning mode if the vehicle positioning is abnormal.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle positioning anomaly detection method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the vehicle positioning anomaly detection method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises a computer program, which, when executed by a processor, is used to implement the vehicle positioning anomaly detection method according to any one of claims 1 to 6.

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