Vehicle location information acquisition method, storage medium and electronic device

Abstract

The present application provides a method for obtaining vehicle position information, a storage medium and an electronic device. The method includes: obtaining vehicle body data; establishing a vehicle motion equation based on the vehicle body data; obtaining the vehicle's current speed, wheel status and sensor measurement data, and fusing the vehicle's current speed, wheel status, sensor measurement data and vehicle motion equation according to a filtering algorithm to obtain vehicle state parameters; wherein, in obtaining the wheel status, each wheel speed is mapped to a virtual speed relative to the center of the vehicle's rear axle, and the wheel status is determined based on the difference between each virtual speed; and the vehicle position information is obtained based on the vehicle state parameters. The present application can better adapt to the needs of wheel anomaly detection and vehicle positioning under different road conditions.

Description

Technical Field

[0001] The present application belongs to the field of intelligent driving technology, and in particular to the field of vehicle position control technology in automatic parking. Background Art

[0002] In modern automotive technology, the level of vehicle intelligence and automation is rapidly improving. As one of the core technologies of autonomous driving and advanced driver assistance systems (ADAS), the accuracy of vehicle position information acquisition directly affects driving safety and user experience. Currently, there are many methods for acquiring vehicle position information, some of which may use sensors such as inertial measurement units (IMUs), global positioning systems (GPS), cameras, etc., combined with algorithms to estimate the vehicle's position and direction.

[0003] Traditional vehicle wheel speed anomaly detection methods have problems with insufficient accuracy and low positioning precision, and cannot meet the current needs of intelligent driving. Summary of the invention

[0004] The present application provides a vehicle position information acquisition method, a storage medium and an electronic device for improving the accuracy of vehicle wheel speed abnormality detection and vehicle positioning.

[0005] In a first aspect, an embodiment of the present application provides a method for obtaining vehicle position information, comprising: obtaining vehicle body data; establishing a vehicle motion equation based on the vehicle body data; obtaining the vehicle's current speed, wheel status, and sensor measurement data, and fusing the vehicle's current speed, the wheel status, the sensor measurement data, and the vehicle motion equation according to a filtering algorithm to obtain vehicle state parameters; wherein, in obtaining the wheel status, each wheel speed is mapped to a virtual speed relative to the center of the vehicle's rear axle, and the wheel status is determined based on the difference between each of the virtual speeds; and the vehicle position information is obtained based on the vehicle state parameters.

[0006] In an implementation of the first aspect, the vehicle motion equation includes a state equation and an observation equation of a vehicle model; the fusion processing of the vehicle current speed, the wheel state, the sensor measurement data and the vehicle motion equation according to the filtering algorithm includes: predicting the vehicle state and state covariance matrix at the next moment according to the state equation of the vehicle model; calculating the observation residual and the Kalman gain according to the sensor measurement data and the observation equation; obtaining the observation update of the vehicle state according to the observation residual, the Kalman gain and the predicted vehicle state at the next moment, and obtaining the observation update of the state covariance matrix according to the predicted state covariance matrix at the next moment, the Kalman gain and the Jacobian matrix of the observation equation; wherein the parameters of the Kalman gain are dynamically adjusted according to the current vehicle speed and the wheel state.

[0007] In an implementation of the first aspect, the Kalman gain is calculated based on the predicted state covariance matrix at the next moment, the Jacobian matrix of the observation equation, and the observation noise covariance matrix; and the observation noise covariance matrix in the Kalman gain is dynamically adjusted based on the current speed of the vehicle and the wheel state.

[0008] In an implementation of the first aspect, dynamically adjusting the observation noise covariance matrix in the Kalman gain according to the current speed of the vehicle and the wheel state includes: dynamically constructing a diagonal matrix according to the current speed of the vehicle and the wheel state; using the diagonal matrix as coefficients of the observation noise covariance matrix to adjust the observation noise covariance matrix.

[0009] In an implementation manner of the first aspect, the diagonal matrix is:

[0010]

[0011] Among them, λ is a diagonal matrix, S is the wheel state, and v is the current speed of the vehicle.

[0012] In an implementation of the first aspect, mapping the speed of each wheel to a virtual speed relative to the center of the rear axle of the vehicle includes: acquiring the wheel speed, the vehicle wheelbase and the vehicle width from the vehicle body data, and mapping the steering wheel angle to a turning radius according to a preset mapping table; mapping the speed of each rear wheel of the vehicle to a virtual speed relative to the center of the rear axle of the vehicle according to the wheel speed, the vehicle width and the turning radius, and mapping the speed of each front wheel of the vehicle to a virtual speed relative to the center of the rear axle of the vehicle according to the wheel speed, the vehicle width, the vehicle wheelbase and the turning radius.

[0013] In an implementation of the first aspect, determining the wheel state based on the difference between the virtual speeds includes: obtaining the mean and / or standard deviation of the difference between the virtual speed of any wheel mapped to the center of the rear axle of the vehicle and the virtual speeds of other wheels mapped to the center of the rear axle of the vehicle; when the absolute value of the mean is greater than the mean threshold and / or the absolute value of the standard deviation is greater than the standard deviation threshold, confirming that the wheel state is abnormal.

[0014] In an implementation manner of the first aspect, the filtering algorithm is a Kalman filtering algorithm, an extended information filtering algorithm, a particle filtering algorithm, or a Gaussian mixture model filtering algorithm.

[0015] In a second aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the vehicle position information acquisition method described in any one of the first aspects of the present application.

[0016] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor and a memory; the memory stores program instructions; the processor is used to run the program instructions to execute the vehicle position information acquisition method described in any one of the first aspects of the present application.

[0017] The vehicle location information acquisition method provided in the embodiment of the present application has the following beneficial effects:

[0018] The present application converts the speed of each wheel into a virtual speed relative to the center of the rear axle of the vehicle to form a unified reference system, which is convenient for wheel abnormality detection. By statistically analyzing the virtual speed differences of the four wheels, the abnormal state of the wheel can be effectively identified and located, thereby improving the accuracy of abnormality detection. In addition, the present application combines an extended filtering algorithm for vehicle positioning and dynamically adjusts the filtering parameters to improve the accuracy and robustness of positioning, so that the present application can better adapt to the needs of wheel abnormality detection and vehicle positioning under different road conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Shown is a flow chart of a method for obtaining vehicle location information according to an embodiment of the present application.

[0020] Figure 2 Shown is a schematic flow chart of the implementation principle of a method for acquiring vehicle location information according to an embodiment of the present application.

[0021] Figure 3 Shown is a schematic diagram of the principle of establishing a vehicle motion equation in a vehicle position information acquisition method according to an embodiment of the present application.

[0022] Figure 4 Shown is a schematic diagram of the principle flow of identifying the wheel status in a method for acquiring vehicle position information in an embodiment of the present application.

[0023] Figure 5 Shown is a flow chart of a method for acquiring vehicle position information in an embodiment of the present application for fusing the vehicle's current speed, wheel status, sensor measurement data, and vehicle motion equation according to a filtering algorithm.

[0024] Figure 6 Shown is a schematic diagram of the principles of prediction and updating in a method for acquiring vehicle position information according to an embodiment of the present application.

[0025] Figure 7 Shown is a schematic diagram of dynamically adjusting filtering parameters in a vehicle position information acquisition method according to an embodiment of the present application.

[0026] Figure 8 Shown is a schematic diagram of the principle flow of obtaining vehicle status parameters in a vehicle position information acquisition method according to an embodiment of the present application.

[0027] Fig. 9 Shown is a flow chart of mapping the wheel speed to a virtual speed relative to the center of the rear axle of the vehicle in a method for acquiring vehicle position information according to an embodiment of the present application.

[0028] Fig.10 Shown is a schematic diagram of the structure of an electronic device in an embodiment of the present application.

[0029] Component number description

[0030] 100 electronic devices

[0031] 101 Memory

[0032] 102 processors

[0033] 103 Display

[0034] Steps S100 to S400

[0035] Steps S310 to S350

[0036] Steps S301 to S303

[0037] Steps S1 to S4

[0038] Steps S2.1 to S2.3

[0039] Steps S3.1 to S3.4 DETAILED DESCRIPTION

[0040] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways according to different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0041] The embodiment of the present application provides a method for obtaining vehicle position information, which is used to improve the accuracy of vehicle wheel speed abnormality detection and vehicle positioning. Figure 1 To Attachment Fig.10 , the technical solution in the embodiment of the present application is described in detail, so that those skilled in the art can understand and implement the vehicle position information acquisition method of the present embodiment without creative work.

[0042] Figure 1 The flowchart of the vehicle location information acquisition method in the embodiment of the present application is shown. Figure 1 As shown, the vehicle position information acquisition method provided in the embodiment of the present application includes the following steps S100 to S400.

[0043] Step S100, obtaining vehicle body data;

[0044] Step S200, establishing a vehicle motion equation according to the vehicle body data;

[0045] Step S300, obtaining the current speed of the vehicle, the wheel state, and the sensor measurement data, and fusing the current speed of the vehicle, the wheel state, the sensor measurement data, and the vehicle motion equation according to a filtering algorithm to obtain a vehicle state parameter; wherein, in obtaining the wheel state, each wheel speed is mapped to a virtual speed relative to the center of the rear axle of the vehicle, and the wheel state is determined according to the difference between each virtual speed;

[0046] Step S400, obtaining vehicle position information according to the vehicle state parameters.

[0047] Figure 2 The following is a schematic diagram of the implementation principle of the vehicle location information acquisition method according to an embodiment of the present application. Figure 2 As shown, in this embodiment, when implemented specifically, it includes: S1, vehicle body data input and data preprocessing; S2, wheel abnormal pattern recognition; S3, vehicle positioning information calculation; S4, positioning information output. The above steps S100 to S400 of the parking position update method in automatic parking in this embodiment are described in detail below.

[0048] Step S100, obtaining vehicle body data.

[0049] Specifically, in this embodiment, various sensor data of the vehicle, various operating parameters of the vehicle, body data such as status parameters, etc. can be read from the electronic control unit (ECU), electronic stability control system (ESC) and on-board diagnostic system (OBD) of the vehicle.

[0050] In this embodiment, the vehicle body data includes various data required to construct the vehicle motion equation, and the vehicle body data includes but is not limited to tire sensor pulse and direction, steering wheel angle, rear axle center angular velocity output by ESC, vehicle wheelbase and vehicle width, etc.

[0051] In this embodiment, the acquired vehicle body data is also pre-processed according to the needs of subsequent data processing. For example, the pulse signal of the detected tire sensor is converted into the actual rotation speed of the wheel, that is, the wheel speed signal; using a pre-calibrated angle table, the steering wheel angle information is mapped into the actual angle of the front wheel and the turning radius of the vehicle.

[0052] Step S200: establishing a vehicle motion equation according to the vehicle body data.

[0053] This embodiment establishes a differential equation describing the motion of the vehicle, namely, the vehicle motion equation, based on the kinematic characteristics of the vehicle.

[0054] In a specific implementation of this embodiment, the vehicle motion equation includes a state equation and an observation equation of a vehicle model.

[0055] Specifically, in this embodiment, the state vector X and the observation vector Z are first selected, and the state equation and the observation equation of the vehicle model are constructed respectively, and the difference between the theoretical observation value (predicted and updated by Kalman filtering) and the observation vector Z (actual collection) is minimized to solve the state vector X.

[0056] Figure 3 The schematic diagram is a schematic diagram showing the principle of establishing a vehicle motion equation in a method for obtaining vehicle position information according to an embodiment of the present application. Figure 3 As shown, this embodiment considers the vehicle kinematic characteristics and selects the state vector X = [x, y, θ, β, v, ω] T , and establish the state equation f(X k ,u k ):

[0057]

[0058] in:

[0059] x represents the horizontal coordinate of the center of the rear axle of the vehicle in the initial coordinate system;

[0060] y represents the ordinate of the center of the rear axle of the vehicle in the initial coordinate system;

[0061] θ represents the heading angle of the rear axle center of the vehicle in the initial coordinate system;

[0062] β represents the side slip angle of the vehicle’s rear axle center;

[0063] v represents the centerline speed of the vehicle's rear axle;

[0064] ω represents the angular velocity of the rear axle of the vehicle.

[0065] u β 、u v 、u ωis the process noise.

[0066] Select observation vector in:

[0067] Represents the observed value of the centerline speed of the vehicle's rear axle, output by the body sensor;

[0068] Represents the observed angular velocity of the rear axle center of the vehicle, output by the body sensor;

[0069] Represents the observed side slip angle of the rear axle of the vehicle. It can be expressed as: Among them, ξ is the front wheel steering angle, k1 is the proportional coefficient, and the value varies depending on the vehicle model.

[0070] The observation equation can be expressed as in To measure noise, h(X) can be expressed as:

[0071]

[0072] Among them, k2 is the proportional coefficient, and its value varies according to different vehicle models.

[0073] Figure 4 The schematic diagram of the principle flow of obtaining vehicle status parameters in the vehicle position information obtaining method according to an embodiment of the present application is shown. Figure 4 As shown, obtaining vehicle state parameters and estimating the accurate position of the vehicle include: S3.1, establishing state equations and observation equations; S3.2, state prediction; S3.3, observation update; S3.4, dynamically adjusting the observation noise covariance matrix. The following will continue to explain the specific implementation process of state prediction / observation update and dynamic adjustment of the observation noise covariance matrix without S300.

[0074] Step S300, obtaining the current speed of the vehicle, the wheel state and the sensor measurement data, and fusing the current speed of the vehicle, the wheel state, the sensor measurement data and the vehicle motion equation according to a filtering algorithm to obtain vehicle state parameters; wherein, in obtaining the wheel state, each wheel speed is mapped to a virtual speed relative to the center of the rear axle of the vehicle, and the wheel state is determined based on the difference between each of the virtual speeds.

[0075] In a specific implementation of this embodiment, the filtering algorithm is but not limited to a Kalman filtering algorithm, an extended information filtering algorithm, a particle filtering algorithm or a Gaussian mixture model filtering algorithm. This embodiment selects an appropriate filtering algorithm to estimate the accurate position of the vehicle by fusing sensor measurement data and vehicle motion equations and other data.

[0076] Figure 5 The flowchart of the vehicle position information acquisition method according to an embodiment of the present application is to fuse the vehicle current speed, wheel status, sensor measurement data and vehicle motion equation according to the filtering algorithm. Figure 5 As shown, the fusing process of the current vehicle speed, the wheel state, the sensor measurement data and the vehicle motion equation according to the filtering algorithm includes the following steps S310 to S350.

[0077] Step S310, predicting the vehicle state and state covariance matrix at the next moment according to the state equation of the vehicle model;

[0078] Step S320, calculating the observation residual according to the sensor measurement data and the observation equation, and calculating the Kalman gain;

[0079] Step S330, obtaining an observation update of the vehicle state according to the observation residual, the Kalman gain and the predicted vehicle state at the next moment;

[0080] Step S340, obtaining an observation update of the state covariance matrix according to the predicted state covariance matrix at the next moment, the Kalman gain, and the Jacobian matrix of the observation equation;

[0081] Step S350: dynamically adjusting the parameters of the Kalman gain according to the current speed of the vehicle and the state of the wheels.

[0082] Figure 6 The diagram is a schematic diagram of the principle of prediction and updating in the vehicle position information acquisition method according to an embodiment of the present application. Figure 6 As shown, the specific process of fusing the current speed of the vehicle, the wheel state, the sensor measurement data and the vehicle motion equation according to the filtering algorithm is as follows:

[0083] 1) State prediction: At each time step k, the state X at the next moment is predicted based on the state equation of the vehicle model in step a). k+1 , and predict the state covariance matrix:

[0084] (A): In predicting the vehicle state at the next moment according to the state equation of the vehicle model, the state X at the next moment is calculated based on the following formula: k+1 Prediction:

[0085] X k+1|k =f(X k ,u k )

[0086] Among them, f(X k ,u k ) is the state equation, Xk+1|k is the vehicle state at the next moment, where u k is the process noise, that is, u in each time step k mentioned above β 、u v 、u ω .

[0087] (B): The state covariance matrix of the next moment is predicted according to the state equation of the vehicle model, and the state covariance matrix of the next moment is predicted based on the following formula:

[0088]

[0089] Among them, P k+1|k is the state covariance matrix at the next moment, P k is the current state covariance matrix, F k is the Jacobian matrix of the state equation, Q k is the process noise covariance matrix.

[0090] 2) Observation update: execution state X k+1 Observation update with state covariance matrix

[0091] The observation residual is calculated based on the sensor measurement data and the observation equation. Specifically, at time step k+1, the sensor observation Z is obtained k+1 , calculate the observed residual y k+1 :

[0092] y k+1 =Z k+1 -h(X k+1|k )

[0093] Among them, h(X k+1|k ) is the observation equation, and the predicted observations are based on the state estimation.

[0094] In a specific implementation of this embodiment, the Kalman gain is calculated based on the predicted state covariance matrix of the next moment, the Jacobian matrix of the observation equation, and the observation noise covariance matrix. Specifically, the Kalman gain K is calculated. k+1 The method is:

[0095] Among them, H k+1 is the Jacobian matrix of the observation equation, is the observation noise covariance matrix.

[0096] The observation update of the vehicle state is obtained according to the observation residual, the Kalman gain and the predicted vehicle state at the next moment, specifically: k+1 =X k+1|k +K k+1 yk+1

[0097] The observation update of the state covariance matrix is ​​obtained according to the predicted state covariance matrix of the next moment, the Kalman gain and the Jacobian matrix of the observation equation, specifically: k+1 =(IK k+1 H k+1 ) k+1|k .

[0098] In this embodiment, the covariance matrix is ​​dynamically adjusted according to the current speed of the vehicle and the abnormal state of the tire to improve the robustness of the filtering algorithm to the system state. Specifically, the observation noise covariance matrix in the Kalman gain is dynamically adjusted according to the current speed of the vehicle and the wheel state.

[0099] Figure 7 The diagram shows a schematic diagram of dynamically adjusting filtering parameters in a method for acquiring vehicle position information according to an embodiment of the present application. Figure 7 As shown, in a specific implementation of the present embodiment, the dynamically adjusting the observation noise covariance matrix in the Kalman gain according to the current speed of the vehicle and the wheel state includes: dynamically constructing a diagonal matrix according to the current speed of the vehicle and the wheel state; using the diagonal matrix as the coefficient of the observation noise covariance matrix to adjust the observation noise covariance matrix.

[0100] When a tire is in an abnormal state, the confidence of the tire will be reduced. If the confidence data sent by the sensor is used directly, the vehicle position information obtained by subsequent calculations will be inaccurate. Therefore, in this embodiment, the observation noise covariance matrix is ​​dynamically adjusted according to the current vehicle speed and the abnormal state of the tire to improve the robustness of the filtering algorithm to the system state. R k+1 is a constant, λ is a diagonal matrix. In a specific implementation of this embodiment, the diagonal matrix is:

[0101]

[0102] Where S is the wheel state, and v is the current speed of the vehicle. (1:4)=S means that the elements of the 1st to 4th rows of the diagonal matrix are S, (5)=1 means that the elements of the 5th row of the diagonal matrix are 1, if the current speed of the vehicle is <0.1m / s, then the elements of the 6th row of the diagonal matrix are 0, otherwise, the elements of the 6th row of the diagonal matrix are 1, (7)=1 means that the elements of the 7th row of the diagonal matrix are 1.

[0103] This embodiment combines the extended Kalman filter (EKF) algorithm to perform vehicle positioning and dynamically adjusts the filter parameters, which can improve the accuracy and robustness of vehicle positioning.

[0104] In this embodiment, in obtaining the wheel state, each wheel speed is mapped to a virtual speed relative to the center of the rear axle of the vehicle, and the wheel state is determined according to the difference between the virtual speeds; for wheel abnormal state recognition; Figure 8 The schematic diagram of the principle flow of identifying the wheel status in the vehicle position information acquisition method according to an embodiment of the present application is shown. Figure 8 As shown, it includes: S2.1, wheel speed filtering; S2.2, virtual speed solution, S2.3, abnormal state identification.

[0105] Specifically, Fig. 9 The flowchart of the method for obtaining vehicle position information according to one embodiment of the present application is shown as follows: Fig. 9 As shown, in a specific implementation of this embodiment, mapping each wheel speed to a virtual speed relative to the center of the rear axle of the vehicle includes the following steps S301 to S303.

[0106] Step S301, obtaining wheel speed, vehicle wheelbase and vehicle width from the vehicle body data, and mapping the steering wheel angle to a turning radius according to a preset mapping table;

[0107] Step S302, mapping the speed of each rear wheel of the vehicle into a virtual speed relative to the center of the rear axle of the vehicle according to the wheel speed, the vehicle width and the turning radius;

[0108] Step S303: Mapping the speed of each front wheel of the vehicle into a virtual speed relative to the center of the rear axle of the vehicle according to the wheel speed, the vehicle width, the vehicle wheelbase and the turning radius.

[0109] The vehicle's four-wheel speed can be expressed as: v = [v lf ,v rf ,v lr ,v rr ], where v lf ,v rf ,v lr ,v rr Represent the speeds of the left front wheel, right front wheel, left rear wheel, and right rear wheel respectively.

[0110] In this embodiment, filtering technology is applied to smooth the wheel speed signal and reduce the impact of noise, and the vehicle kinematic model is used to calculate the virtual speed of each wheel mapped to the center of the vehicle's rear axle. Finally, a statistical method is used to analyze the four wheel speed signals to identify possible abnormal conditions (such as passing over speed bumps, potholes, and other road surfaces).

[0111] Specifically, since the output frequency of the vehicle's four-wheel speed is relatively high, this embodiment uses a filtering algorithm to smooth the wheel speed signal and reduce the impact of noise. In this embodiment, taking sliding window filtering as an example, the filtered four-wheel speed at time k is It is expressed as: Where N is the sliding window size, The sliding window filtering algorithm is used to reduce the noise in the wheel speed signal and improve the reliability of the wheel speed data.

[0112] Then, the vehicle kinematic model is used to calculate the virtual speed of each wheel mapped to the center of the vehicle's rear axle. The details are as follows:

[0113] Convert the steering wheel angle to the theoretical turning radius R through the corresponding table TT =H(δ), where δ is the steering wheel angle. In this embodiment, the turning radius calculated in the data preprocessing in step S100 can be directly called.

[0114] The virtual speed of the left rear wheel mapped to the center of the vehicle's rear axle can be expressed as:

[0115] The virtual speed of the right rear wheel mapped to the center of the vehicle's rear axle can be expressed as:

[0116] The virtual speed of the left front wheel mapped to the center of the vehicle's rear axle can be expressed as:

[0117] The virtual speed of the right front wheel mapped to the center of the vehicle's rear axle can be expressed as:

[0118] Where: W represents the width of the vehicle, L represents the wheelbase of the vehicle, R TT is the turning radius.

[0119] This embodiment converts the speed of each wheel into a virtual speed relative to the center of the rear axle of the vehicle through a vehicle kinematic model, forming a unified reference system to facilitate wheel abnormality detection.

[0120] In a specific implementation of this embodiment, determining the wheel state based on the difference between the virtual speeds includes: obtaining the mean and / or standard deviation of the difference between the virtual speed of any wheel mapped to the center of the rear axle of the vehicle and the virtual speeds of other wheels mapped to the center of the rear axle of the vehicle; when the absolute value of the mean is greater than the mean threshold and / or the absolute value of the standard deviation is greater than the standard deviation threshold, confirming that the wheel state is abnormal.

[0121] This embodiment uses a statistical method to analyze the four wheel speed signals of the vehicle to identify possible abnormal conditions, such as passing over speed bumps, potholes, and other road surfaces.

[0122] On a flat road, the four-wheel speeds corresponding to the virtual speed of the vehicle's rear axle center through the kinematic model should be the same. Considering factors such as sensor accuracy and data output frequency, this embodiment performs statistical analysis on the wheel speeds over a period of time to identify abnormal conditions and locate the wheels with abnormal conditions. The wheel state can be expressed as S = [S lf ,S rf ,S lr ,S rr ],S lf is the state of the left front wheel, S rf is the state of the right front wheel, S lr is the status of the left rear wheel, S rr is the state of the right rear wheel, each S lf ,S rf ,S lr ,S rr A value of 1 indicates abnormality and a value of 0 indicates normality.

[0123] Taking the left rear wheel as an example, calculate the mean and standard deviation of the virtual speeds of the right rear wheel, left front wheel, right front wheel mapped to the center of the vehicle's rear axle and the virtual speed of the left rear wheel mapped to the center of the vehicle's rear axle. When the absolute values ​​of the mean and standard deviation are greater than their respective corresponding thresholds, it means that the speed of the left rear wheel is unreliable. lr Set to 1, otherwise 0. The same applies to the right rear wheel, left front wheel, and right front wheel, which will not be repeated here.

[0124] This embodiment effectively identifies and locates the abnormal state of the wheels by statistically analyzing the virtual speed differences of the four wheels, thereby improving the accuracy of abnormality detection.

[0125] In addition, in other implementations, the four-wheel speed data can also be pattern recognized through machine learning or deep learning algorithms to determine the wheel status, thereby further improving the accuracy and response speed of abnormality detection.

[0126] Finally, in this embodiment, the vehicle position information is acquired according to the vehicle state parameters.

[0127] Specifically, the vehicle position information is obtained by calculating the vehicle state parameters through a preset positioning algorithm, and the vehicle position information includes the coordinates and direction of the vehicle.

[0128] The preset positioning algorithm may adopt an existing positioning algorithm. For example, in some preset positioning algorithms, the vehicle shape is used as a geometric constraint condition. The relative position between vehicle nodes can be determined through the ranging nodes on the vehicle boundary feature points (such as the four corners); the preset positioning algorithm may involve the solution of nonlinear programming problems. By establishing the objective function and constraints, the relative position coordinates of the vehicle are solved using an optimization algorithm.

[0129] The protection scope of the vehicle position information acquisition method described in the embodiment of the present application is not limited to the execution order of the steps listed in this embodiment. All solutions implemented by adding, reducing or replacing steps in the prior art based on the principles of the present application are included in the protection scope of the present application.

[0130] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the vehicle position information acquisition method provided in any embodiment of the present application is implemented.

[0131] In the embodiment of the present application, any combination of one or more storage media can be used. The storage medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, - but not limited to - an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a RAM, a ROM, an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.

[0132] An embodiment of the present application also provides an electronic device. Fig.10 Shown is a schematic diagram of the structure of the electronic device 100 provided in the embodiment of the present application. In some embodiments, the electronic device may be a mobile phone, a tablet computer, a wearable device, a vehicle-mounted device, an augmented reality (AR) / virtual reality (VR) device, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA) and other terminal devices. In addition, the vehicle location information acquisition method provided in the present application can also be applied to databases, servers, and service response systems based on terminal artificial intelligence. The embodiment of the present application does not impose any restrictions on the specific application scenarios of the vehicle location information acquisition method.

[0133] like Fig.10 As shown, the electronic device 100 provided in the embodiment of the present application includes a memory 101 and a processor 102 .

[0134] The memory 101 is used to store computer programs; preferably, the memory 101 includes: ROM, RAM, disk, USB flash drive, memory card or CD-ROM and other media that can store program codes.

[0135] Specifically, the memory 101 may include a computer system readable medium in the form of a volatile memory, such as a random access memory (RAM) and / or a cache memory. The electronic device 100 may further include other removable / non-removable, volatile / non-volatile computer system storage media. The memory 101 may include at least one program product having a set (e.g., at least one) of program modules, which are configured to perform the functions of the various embodiments of the present application.

[0136] The processor 102 is connected to the memory 101 and is used to execute the computer program stored in the memory 101 so that the electronic device 100 executes the vehicle position information acquisition method provided in any embodiment of the present application.

[0137] Optionally, the processor 102 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0138] Optionally, the electronic device 100 in this embodiment may further include a display 103. The display 103 is communicatively connected with the memory 101 and the processor 102, and is used to display a GUI interaction interface related to the vehicle location information acquisition method.

[0139] In summary, the present application converts the speed of each wheel into a virtual speed relative to the center of the rear axle of the vehicle to form a unified reference system, which is convenient for wheel abnormality detection. By statistically analyzing the virtual speed differences of the four wheels, the abnormal state of the wheel can be effectively identified and located, which improves the accuracy of abnormality detection. In addition, the present application combines the extended filtering algorithm for vehicle positioning and dynamically adjusts the filtering parameters to improve the accuracy and robustness of positioning, so that the present application can better adapt to the needs of wheel abnormality detection and vehicle positioning under different road conditions. Therefore, the present application effectively overcomes the various shortcomings of the prior art and has a high industrial utilization value.

[0140] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A method for obtaining vehicle position information, characterized in that: include: Get vehicle body data; Establishing a vehicle motion equation based on the vehicle body data; Acquire the current speed of the vehicle, the wheel state and the sensor measurement data, and fuse the current speed of the vehicle, the wheel state, the sensor measurement data and the vehicle motion equation according to a filtering algorithm to obtain a vehicle state parameter; wherein, in acquiring the wheel state, each wheel speed is mapped to a virtual speed relative to the center of the rear axle of the vehicle, and the wheel state is determined according to the difference between each virtual speed; The vehicle position information is acquired according to the vehicle state parameter.

2. The vehicle position information acquisition method according to claim 1, characterized in that: The vehicle motion equations include state equations and observation equations of the vehicle model; The fusing process of the current vehicle speed, the wheel state, the sensor measurement data and the vehicle motion equation according to the filtering algorithm comprises: Predicting the vehicle state and state covariance matrix at the next moment according to the state equation of the vehicle model; Calculating the observation residual according to the sensor measurement data and the observation equation, and calculating the Kalman gain; The observation update of the vehicle state is obtained based on the observation residual, the Kalman gain and the predicted vehicle state at the next moment, and the observation update of the state covariance matrix is ​​obtained based on the predicted state covariance matrix at the next moment, the Kalman gain and the Jacobian matrix of the observation equation; wherein the parameters of the Kalman gain are dynamically adjusted according to the current speed of the vehicle and the wheel state.

3. The vehicle position information acquisition method according to claim 2, characterized in that: Calculating the Kalman gain according to the predicted state covariance matrix at the next moment, the Jacobian matrix of the observation equation, and the observation noise covariance matrix; The observation noise covariance matrix in the Kalman gain is dynamically adjusted according to the current speed of the vehicle and the wheel state.

4. The vehicle position information acquisition method according to claim 3, characterized in that: The dynamically adjusting the observation noise covariance matrix in the Kalman gain according to the current speed of the vehicle and the wheel state comprises: Dynamically construct a diagonal matrix according to the current speed of the vehicle and the wheel state; The diagonal matrix is ​​used as a coefficient of the observation noise covariance matrix to adjust the observation noise covariance matrix.

5. The vehicle position information acquisition method according to claim 4, characterized in that: The diagonal matrix is: Among them, λ is a diagonal matrix, S is the wheel state, and v is the current speed of the vehicle.

6. The vehicle position information acquisition method according to claim 1, characterized in that: Mapping each wheel speed to a virtual speed relative to the center of the rear axle of the vehicle comprises: Acquire wheel speed, vehicle wheelbase and vehicle width from the vehicle body data, and map the steering wheel angle to a turning radius according to a preset mapping table; The speed of each rear wheel of the vehicle is mapped to a virtual speed relative to the center of the rear axle of the vehicle according to the wheel speed, the vehicle width and the turning radius, and the speed of each front wheel of the vehicle is mapped to a virtual speed relative to the center of the rear axle of the vehicle according to the wheel speed, the vehicle width, the vehicle wheelbase and the turning radius.

7. The vehicle position information acquisition method according to claim 1 or 6, characterized in that: Determining the wheel state according to the difference between the virtual speeds comprises: Obtaining a mean and / or standard deviation of a difference between a virtual speed of any wheel mapped to a center of a rear axle of the vehicle and a virtual speed of other wheels mapped to a center of a rear axle of the vehicle; When the absolute value of the mean is greater than the mean threshold and / or the absolute value of the standard deviation is greater than the standard deviation threshold, it is confirmed that the wheel state is abnormal.

8. The vehicle position information acquisition method according to claim 1, characterized in that: The filtering algorithm is a Kalman filtering algorithm, an extended information filtering algorithm, a particle filtering algorithm or a Gaussian mixture model filtering algorithm.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the vehicle position information acquisition method according to any one of claims 1 to 8 is implemented.

10. An electronic device, characterized in that: The electronic device comprises: Processor and memory; The memory stores program instructions; The processor is used to run the program instructions to execute the vehicle position information acquisition method as described in any one of claims 1 to 8.

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