Calibration method, device, equipment, medium and program product for heading installation error

Abstract

The present disclosure provides a calibration method, device, equipment, medium and program product for heading installation error, which relates to the fields of artificial intelligence technology such as autonomous driving and intelligent transportation. An implementation of the method includes: obtaining operating parameter information of a vehicle; determining the center of mass slip angle according to the operating parameter information and a preset kinematic model; establishing a heading installation error estimation model according to the center of mass slip angle and the speed included in the operating parameter information; and determining the heading installation error according to the heading installation error estimation model.

Description

Technical Field

[0001] The present disclosure relates to the field of computers, specifically to the field of artificial intelligence technologies such as autonomous driving and intelligent transportation, and in particular to a calibration method, device, equipment, medium and program product for heading installation error. Background Art

[0002] The navigation and positioning system of vehicles needs to rely on sensor equipment such as IMU (Inertial measurement unit) and GNSS (Global Navigation Satellite System) antennas. However, installation errors are inevitable during the installation and deployment of sensors, especially heading installation errors, that is, the actual heading is not completely consistent with the installed heading. The existence of heading installation errors will cause a certain error between the heading of the vehicle fed back by the navigation and positioning system and the actual value, which directly affects the control effect of the vehicle and poses a safety risk.

[0003] In the relevant technology, it is necessary to collect the motion trajectory of each vehicle in advance, and then obtain the geographic location information and speed information of the vehicle during driving based on the global navigation satellite system GNSS sensor, so as to calculate the track angle of the main vehicle, and then calculate the difference between the track angle and the heading angle, that is, the heading installation error. Summary of the invention

[0004] The disclosed embodiments provide a method, device, equipment, medium and program product for calibrating a heading installation error.

[0005] In a first aspect, an embodiment of the present disclosure proposes a method for calibrating a heading installation error, comprising: obtaining operating parameter information of a vehicle; determining a sideslip angle of the center of mass based on the operating parameter information and a preset kinematic model; establishing a heading installation error estimation model based on the sideslip angle of the center of mass and the speed included in the operating parameter information; and determining the heading installation error based on the heading installation error estimation model.

[0006] In the second aspect, an embodiment of the present disclosure proposes a calibration device for a heading installation error, comprising: an information acquisition module, configured to acquire operating parameter information of a vehicle; an angle determination module, configured to determine the center of mass sideslip angle based on the operating parameter information and a preset kinematic model; a model establishment module, configured to establish a heading installation error estimation model based on the center of mass sideslip angle and the speed included in the operating parameter information; and an error determination module, configured to determine the heading installation error based on the heading installation error estimation model.

[0007] In a third aspect, an embodiment of the present disclosure proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in the first aspect.

[0008] In a fourth aspect, an embodiment of the present disclosure proposes a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to cause a computer to execute the method described in the first aspect.

[0009] In a fifth aspect, an embodiment of the present disclosure proposes a computer program product, including a computer program, which implements the method described in the first aspect when executed by a processor.

[0010] In a sixth aspect, an embodiment of the present disclosure proposes a means of transportation, comprising the electronic device described in the third aspect.

[0011] The calibration method, device, equipment, medium and program product of the heading installation error provided by the embodiments of the present disclosure obtain the operating parameter information of the vehicle; then determine the center of mass sideslip angle according to the operating parameter information and a preset kinematic model; then establish a heading installation error estimation model according to the center of mass sideslip angle and the speed included in the operating parameter information; finally, determine the heading installation error according to the heading installation error estimation model. Compared with the related technology, on the one hand, it is not necessary to collect the motion trajectory of each vehicle in travel in advance, so there is no need to manually calibrate the heading installation error offline, saving labor costs; on the other hand, by constructing the heading installation error estimation model, the heading installation error will not be affected by the positioning position error, so that the heading installation error can be accurately calibrated online to ensure high-precision navigation control.

[0012] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Other features, purposes and advantages of the present disclosure will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. Among them:

[0014] Figure 1 is an exemplary system architecture diagram in which the present disclosure may be applied;

[0015] Figure 2 is a flow chart of an embodiment of a method for calibrating a heading installation error according to the present disclosure;

[0016] Figure 3 This is a schematic diagram of heading installation error;

[0017] Figure 4 is a schematic diagram of an embodiment of a device for calibrating a heading installation error according to the present disclosure;

[0018] Figure 5 is a block diagram of an electronic device for implementing an embodiment of the present disclosure. DETAILED DESCRIPTION

[0019] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0020] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0021] Figure 1 An exemplary system architecture 100 is shown to which embodiments of the method and apparatus for calibrating a heading installation error of the present disclosure may be applied.

[0022] like Figure 1 As shown, the system architecture 100 may include a terminal 101 corresponding to a vehicle, a network 102 and a server 103. Figure 1 The example of a terminal corresponding to a vehicle is taken as an example. The network 102 is used as a medium for providing a communication link between the terminal 101 corresponding to the vehicle and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc. Figure 1 Take unmanned vehicles as an example.

[0023] The terminal 101 corresponding to the vehicle can be hardware or software. When the terminal 101 corresponding to the vehicle is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or it can be implemented as a single server. When the terminal 101 corresponding to the vehicle is software, it can be implemented as multiple software or software modules (for example, used to provide distributed services), or it can be implemented as a single software or software module. No specific limitation is made here.

[0024] The server 103 can provide various services. For example, the server 103 can obtain information to be displayed of the unmanned vehicle; obtain operating parameter information of the vehicle; determine the center of mass slip angle according to the operating parameter information and a preset kinematic model; establish a heading installation error estimation model according to the center of mass slip angle and the speed included in the operating parameter information; and determine the heading installation error according to the heading installation error estimation model.

[0025] It should be noted that the server 103 can be hardware or software. When the server 103 is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or it can be implemented as a single server. When the server 103 is software, it can be implemented as multiple software or software modules (for example, for providing distributed services), or it can be implemented as a single software or software module. No specific limitation is made here.

[0026] It should be noted that the heading installation error calibration method provided in the embodiment of the present disclosure is generally executed by the server 103 , and accordingly, the heading installation error calibration device is generally disposed in the server 103 .

[0027] It should be understood that Figure 1 The number of servers, terminals corresponding to vehicles, and networks in the embodiment is only for illustration. Any number of servers, terminals corresponding to vehicles, and networks may be provided as required.

[0028] Continue to refer Figure 2 , which shows a process 200 of an embodiment of a method for calibrating a heading installation error according to the present disclosure. The method for calibrating a heading installation error may include the following steps:

[0029] Step 201, obtaining operating parameter information of a vehicle.

[0030] In this embodiment, the execution subject of the heading installation error calibration method (for example Figure 1 The server 103 shown in FIG. 104 may be accessed through a network (eg, Figure 1 The network 102 shown) obtains transportation (e.g. Figure 1 The operating parameter information of the terminal 101 corresponding to the vehicle shown in FIG. 1 ; or the execution subject of the calibration method of the heading installation error (eg Figure 1The terminal 101 corresponding to the vehicle shown can obtain operating parameter information through various sensors and / or information collection devices of the vehicle, and the information collection device can be an image and / or video collection device, such as a camera. The above operating parameter information can be information involved in the operation of the vehicle, such as itinerary information, recommended information related to the itinerary, information related to passengers (for example, physiological characteristic parameters of passengers, such as body temperature, respiratory parameters, fatigue and other parameters), etc. The above operating parameter information can be determined according to actual needs. For example, the traffic environment status can be obtained with the help of vehicle-mounted sensor devices such as cameras, global positioning systems, inertial measurement units, millimeter wave radars, laser radars, etc., such as weather data, traffic lights, traffic topology information, location, and other information.

[0031] In the technical solution of the present invention, the collection, storage, use, processing, transmission, provision and disclosure of the operating parameter information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0032] Step 202: Determine the sideslip angle of the center of mass according to the operating parameter information and a preset kinematic model.

[0033] In this embodiment, the execution subject may calculate the mass center sideslip angle based on a preset kinematic model and using the operation parameter information. The mass center sideslip angle may be the angle between the mass center velocity direction and the direction of the head of the vehicle.

[0034] In one example, determining the center of mass sideslip angle according to the operating parameter information and a preset kinematic model may include: inputting the operating parameter information into the preset kinematic model to obtain the center of mass sideslip angle.

[0035] It should be noted that the above-mentioned preset kinematic model can reflect the relationship between the operating parameter information and the sideslip angle of the center of mass. The present embodiment does not limit the specific form of the model. In practical applications, it can be reasonably set according to needs. For example, the bicycle model can be improved to obtain the preset kinematic model, or the preset kinematic model can be obtained by fitting the relationship between the operating parameter information and the sideslip angle of the center of mass.

[0036] Step 203: Establish a heading installation error estimation model according to the sideslip angle of the center of mass and the speed included in the operating parameter information.

[0037] In this embodiment, the execution subject may establish a heading installation error estimation model according to the sideslip angle of the center of mass and the speed included in the operating parameter information. The heading installation error estimation model may reflect the relationship between the operating parameter information and the sideslip angle of the center of mass. The specific form of the model is not limited in this embodiment. In practical applications, it may be reasonably set according to requirements. For example, the single-vehicle model may be improved to obtain the heading installation error estimation model.

[0038] Step 204: determining the heading installation error according to the heading installation error estimation model.

[0039] In this embodiment, the above-mentioned execution subject can determine the heading installation error according to the heading installation error estimation model.

[0040] The method for calibrating the heading installation error provided in the embodiment of the present disclosure obtains the operating parameter information of the vehicle; then determines the sideslip angle of the center of mass according to the operating parameter information and a preset kinematic model; then establishes a heading installation error estimation model according to the sideslip angle of the center of mass and the speed included in the operating parameter information; finally, determines the heading installation error according to the heading installation error estimation model. Compared with the related art, on the one hand, it is not necessary to collect the motion trajectory of each vehicle during driving in advance, so there is no need to manually calibrate the heading installation error offline, saving labor costs; on the other hand, by constructing the heading installation error estimation model, the heading installation error will not be affected by the positioning position error, so that the heading installation error can be accurately calibrated online to ensure high-precision navigation control.

[0041] In some optional implementations of this embodiment, the operating parameter information also includes: yaw angular velocity, wheel angle and wheelbase; determining the center of mass sideslip angle based on the operating parameter information and a preset kinematic model includes: inputting the yaw angular velocity, wheel angle, wheelbase and speed into the preset kinematic model to obtain the center of mass sideslip angle.

[0042] Here, the yaw rate can refer to the angle of rotation of the vehicle around the Z axis perpendicular to the ground. The angular velocity is the rate of change of the angle, also known as the yaw rate, usually represented by r.

[0043] Generally, when a vehicle turns, the vehicle is considered to be in circular motion, and there is a relationship v = rR. v is the speed of the vehicle, R is the turning radius, and r is the angular velocity.

[0044] In this implementation, the above-mentioned execution entity can input the yaw rate, wheel deflection angle, wheelbase and speed into a preset kinematic model to obtain the center of mass sideslip angle. The above-mentioned wheel deflection angle may refer to the sideslip angle generated by the wheel when the vehicle turns. The wheelbase is the distance between two perpendicular lines passing through the midpoints of two adjacent wheels on the same side of the vehicle and perpendicular to the longitudinal symmetry plane of the vehicle. Simply put, it is the distance from the center of the front axle to the center of the rear axle of the vehicle. The above-mentioned wheelbase is the distance between two perpendicular lines passing through the midpoints of two adjacent wheels on the same side of the vehicle and perpendicular to the longitudinal symmetry plane of the vehicle. Simply put, it is the distance from the center of the front axle to the center of the rear axle of the vehicle.

[0045] The above-mentioned preset kinematic model can reflect the relationship between yaw angular velocity, wheel deflection angle, wheelbase and speed and the sideslip angle of the center of mass. The present embodiment does not limit the specific form of the model. In practical applications, it can be reasonably set according to needs. For example, the single-vehicle model can be improved to obtain the preset kinematic model; or the preset kinematic model can be obtained by fitting according to the relationship between yaw angular velocity, wheel deflection angle, wheelbase and speed and the sideslip angle of the center of mass.

[0046] In this implementation, the yaw rate, wheel slip angle, wheelbase and speed may be input into a preset kinematic model to obtain the center of mass sideslip angle.

[0047] In some optional implementations of the present embodiment, a heading installation error estimation model is established based on the sideslip angle of the center of mass and the speed included in the operating parameter information, which may include: establishing a heading installation error estimation model based on the arc tangent of the ratio of the longitudinal speed of the speed to the lateral speed of the speed, and the sideslip angle of the center of mass.

[0048] In this implementation, the execution subject may establish a heading installation error estimation model based on the arc tangent of the ratio of the longitudinal speed to the lateral speed and the sideslip angle of the center of mass. The longitudinal speed may be a component of the speed perpendicular to the measuring heading, and the lateral speed may be a component of the speed along the measuring heading.

[0049] In one example, in Figure 3 In the equation, XOY is the global coordinate system (the northeast celestial coordinate system is generally used, and the celestial direction is generally ignored in autonomous driving); ε represents the heading installation error, that is, the error between the actual heading and the measured heading; β represents the center of mass sideslip angle, that is, the angle between the velocity direction and the vehicle body direction. x is the component of velocity along the measured heading, v y The component of velocity that measures heading vertically.

[0050] according to Figure 3 We can get:

[0051] atan(v y / vx )=β+ε (1)

[0052] Among them, atan represents the inverse tangent function, v x 、v y They represent the lateral velocity and longitudinal velocity respectively, β represents the sideslip angle of the center of mass, and ε represents the heading installation error.

[0053] Among them, the preset kinematic model can be:

[0054]

[0055] in, is the vehicle yaw rate, δ f represents the front wheel deflection angle, δ r represents the rear wheel deflection angle, and L is the wheelbase.

[0056] It should be noted that the wheel deflection angle includes the front wheel deflection angle and the rear wheel deflection angle.

[0057] According to formula (2), the calculation formula of the center of mass sideslip angle is further obtained as follows:

[0058]

[0059] Then, according to formulas (1) and (2), the heading installation error estimation model is established as follows:

[0060]

[0061] in, arccos stands for the inverse cosine function.

[0062] It should be noted that, according to the corresponding functional module, the heading installation error estimation model can be based on the speed included in the center of mass sideslip angle and the operating parameter information. The functional module can have an image processing function to fit the center of mass sideslip angle and the operating parameter information to establish the heading installation error estimation model.

[0063] In this implementation, a heading installation error estimation model may be established based on the arc tangent of the ratio of the longitudinal velocity to the lateral velocity and the center of mass sideslip angle.

[0064] In some optional implementations of the present embodiment, the heading installation error is determined according to a heading installation error estimation model, including: obtaining the current speed, the current yaw angular velocity and the current wheel deflection angle; in response to the current speed satisfying the corresponding preset speed threshold and the current wheel deflection angle satisfying the corresponding preset deflection angle threshold, inputting the current yaw angular velocity, the current wheel deflection angle, the wheelbase and the current speed into a preset kinematic model to obtain the current center of mass sideslip angle; inputting the arc tangent of the ratio of the longitudinal velocity of the current speed to the lateral velocity of the current speed and the current center of mass sideslip angle into the heading installation error estimation model to obtain the heading installation error.

[0065] In this implementation, the above-mentioned execution entity can obtain the current speed, the current yaw angular velocity and the current wheel deflection angle; then, determine whether the current speed meets the corresponding preset speed threshold, and whether the current wheel deflection angle meets the corresponding preset deflection angle threshold. When the current speed meets the corresponding preset speed threshold, and the current wheel deflection angle meets the current corresponding preset deflection angle threshold, the current yaw angular velocity, the current wheel deflection angle, the wheelbase and the current speed are input into the preset kinematic model to obtain the current center of mass sideslip angle; then, the arc tangent of the ratio of the longitudinal speed of the current speed to the lateral speed of the current speed, and the current center of mass sideslip angle are input into the heading installation error estimation model to obtain the heading installation error.

[0066] In one example, the step of determining a heading installation error may include:

[0067] The first step is to obtain the longitudinal velocity v of the vehicle's current speed through the navigation and positioning system. x , lateral velocity v y 、Current yaw rate And the current front wheel deflection angle δ can also be obtained through chassis feedback f and the rear wheel deflection angle δ r .

[0068] It should be noted that for front-wheel steering vehicles, the rear wheel deflection angle δ is generally r Can be 0.

[0069] The second step is to determine whether the current speed meets the preset speed threshold and whether the current front and rear wheel deflection angles (i.e., the current wheel deflection angles) meet the corresponding preset deflection angle thresholds. If so, execute the third step; if not, execute the first step.

[0070] In one example, v min <v<v max , δ min <(tan(δ f )-tan(δ r ))<δ max , where v min>0,δ min > 0. If it does not meet the allowed range, go back to the first step, otherwise continue to the third step.

[0071] It should be noted that in order to prevent the formula (4) from dividing by 0, it is necessary to ensure that v x 、(v*(tan(δ f )-tan(δ r ))are not 0.

[0072] It should be noted that the preset speed threshold and the preset deflection angle threshold can be set according to the requirements of the heading installation error and the safety factors of the vehicle during actual driving.

[0073] In one example, to ensure the accuracy of the center of mass slip angle calculated by formula (3), the current wheel takes 1m / s <v<10m / s,0.1<(tan(δ f )-tan(δ r ))<0.5.

[0074] The third step is to calculate the current heading installation error ε according to the heading installation error estimation model (Formula (4)).

[0075] It should be noted that the above-mentioned current heading installation error, current speed, current wheel angle and current yaw angular velocity may be the heading installation error, speed, wheel angle and yaw angular velocity corresponding to a certain moment. For example, the current heading installation error may be the heading installation error corresponding to the current moment, the current speed may be the speed corresponding to the current moment, and the current wheel angle may be the wheel angle corresponding to the current moment.

[0076] In some optional implementations of this embodiment, the method further includes: obtaining a heading installation error within a preset time period; and filtering the heading installation error within the preset time period using a mean filter to obtain a filtered heading installation error.

[0077] In this implementation, the heading installation error within a preset time period may also be obtained; then, the heading installation error within the preset time period is filtered using mean filtering to obtain a filtered heading installation error.

[0078] It should be noted that the above-mentioned preset time period may be a period of time in the past. The heading installation error within the above-mentioned preset time period may be the heading installation error corresponding to each moment of the preset time period. Afterwards, the heading installation error within the preset time period is filtered using mean filtering to obtain a filtered heading installation error. Afterwards, the operation of the vehicle may be controlled based on the heading installation error.

[0079] It should be noted that other filtering methods may also be used, such as median filtering, sampling filtering, etc., and the appropriate filtering method may be selected according to the requirements for heading installation error and safety factors.

[0080] In this implementation, mean filtering may be used to filter the heading installation error within a preset time period, so as to accurately control the operation of the vehicle based on the filtered installation error and ensure driving safety.

[0081] In some optional implementations of this embodiment, mean filtering includes: Kalman filtering.

[0082] In this implementation, the above-mentioned mean filtering may include Kalman filtering and complementary filtering.

[0083] Further references Figure 4 As an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a calibration device for heading installation error. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0084] like Figure 4 As shown, the heading installation error calibration device 400 of this embodiment may include: an information acquisition module 401, a deflection determination module 402, a model establishment module 403 and an error determination module 404. Among them, the information acquisition module 401 is configured to acquire the operation parameter information of the vehicle; the deflection determination module 402 is configured to determine the center of mass side slip angle according to the operation parameter information and a preset kinematic model; the model establishment module 403 is configured to establish a heading installation error estimation model according to the center of mass side slip angle and the speed included in the operation parameter information; the error determination module 404 is configured to determine the heading installation error according to the heading installation error estimation model.

[0085] In this embodiment, in the device for calibrating the heading installation error 400, the specific processing of the information acquisition module 401, the deflection determination module 402, the model establishment module 403 and the error determination module 404 and the technical effects thereof can be referred to respectively. Figure 2 The relevant descriptions of steps 201 - 204 in the corresponding embodiment are not repeated here.

[0086] In some optional implementations of this embodiment, the operating parameter information also includes: yaw angular velocity, wheel deflection angle and wheelbase; the deflection angle determination module 402 is further configured to: input the yaw angular velocity, wheel deflection angle, wheelbase and speed into a preset kinematic model to obtain the center of mass sideslip angle.

[0087] In some optional implementations of this embodiment, the model building module 403 is further configured to: establish a heading installation error estimation model based on the arc tangent of the ratio of the longitudinal speed to the lateral speed, and the center of mass sideslip angle.

[0088] In some optional implementations of the present embodiment, the error determination module 404 is further configured to: obtain the current speed, the current yaw angular velocity and the current wheel slip angle; in response to the current speed satisfying the corresponding preset speed threshold, and the current wheel slip angle satisfying the corresponding preset slip angle threshold, input the current yaw angular velocity, the current wheel slip angle, the wheelbase and the current speed into a preset kinematic model to obtain the current center of mass sideslip angle; input the arc tangent of the ratio of the longitudinal velocity of the current speed to the lateral velocity of the current speed, and the current center of mass sideslip angle into the heading installation error estimation model to obtain the heading installation error.

[0089] In some optional implementations of this embodiment, the device also includes: an error acquisition module, configured to obtain a heading installation error within a preset time period; a filtering processing module, configured to use mean filtering to filter the heading installation error within the preset time period to obtain a filtered heading installation error.

[0090] In some optional implementations of this embodiment, mean filtering includes: Kalman filtering.

[0091] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device (for example, Figure 1 The server 103 shown or the terminal 101 corresponding to the vehicle), a readable storage medium, and a computer program product.

[0092] According to an embodiment of the present disclosure, the present disclosure also provides a vehicle, which may also include the above-mentioned electronic device.

[0093] Figure 5 A schematic block diagram of an example electronic device 500 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0094] like Figure 5As shown, the device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0095] A number of components in the device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0096] The computing unit 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 501 performs the various methods and processes described above, such as the calibration method of the heading installation error. For example, in some embodiments, the calibration method of the heading installation error may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the calibration method of the heading installation error described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to execute the calibration method of the heading installation error in any other appropriate manner (for example, by means of firmware).

[0097] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0098] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0099] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (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 foregoing.

[0100] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0101] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0102] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0103] Artificial intelligence is a discipline that studies computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.). It includes both hardware-level and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural speech processing technology, as well as machine learning / deep learning, big data processing technology, knowledge graph technology, and other major directions.

[0104] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions mentioned in this disclosure can be achieved, and this document does not limit this.

[0105] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for calibrating a heading installation error, comprising: Acquiring operating parameter information of a vehicle, wherein the operating parameter information includes: yaw rate, wheel deflection angle, wheelbase and speed; Inputting the yaw rate, wheel deflection angle, wheelbase and speed into a preset kinematic model to obtain a center of mass sideslip angle, wherein the kinematic model is used to characterize the relationship between the yaw rate, the wheel deflection angle, the wheelbase, the speed and the center of mass sideslip angle, wherein the wheel deflection angle includes a front wheel deflection angle and a rear wheel deflection angle, and the speed includes a longitudinal speed and a lateral speed; Establishing a heading installation error estimation model according to an arc tangent function of the ratio of the longitudinal velocity to the lateral velocity and the center of mass sideslip angle; The heading installation error is determined according to the heading installation error estimation model.

2. The method according to claim 1, wherein: Determining the heading installation error according to the heading installation error estimation model includes: Get the current speed, current yaw rate and current wheel angle; In response to the current speed satisfying a corresponding preset speed threshold and the current wheel slip angle satisfying a corresponding preset slip angle threshold, the current yaw rate, the current wheel slip angle, the wheelbase and the current speed are input into a preset kinematic model to obtain a current center of mass sideslip angle; The arc tangent function of the ratio of the longitudinal speed of the current speed to the lateral speed of the current speed and the current center of mass sideslip angle are input into the heading installation error estimation model to obtain the heading installation error.

3. The method according to claim 1 or 2, further comprising: Get the heading installation error within a preset time period; The heading installation error within a preset time period is filtered using mean filtering to obtain a filtered heading installation error.

4. The method according to claim 3, wherein: The mean filtering includes: Kalman filtering.

5. A device for calibrating a heading installation error, comprising: An information acquisition module is configured to acquire operating parameter information of a vehicle, wherein the operating parameter information further includes: yaw rate, wheel deflection angle, wheelbase and speed; a slip angle determination module, configured to input the yaw rate, the wheel slip angle, the wheelbase and the speed into a preset kinematic model to obtain a center of mass slip angle, wherein the kinematic model is used to characterize the relationship between the yaw rate, the wheel slip angle, the wheelbase, the speed and the center of mass slip angle, wherein the wheel slip angle includes a front wheel slip angle and a rear wheel slip angle, and the speed includes a longitudinal speed and a lateral speed; A model building module is configured to build a heading installation error estimation model according to an arc tangent function of the ratio of the longitudinal velocity to the lateral velocity and the center of mass sideslip angle; The error determination module is configured to determine the heading installation error according to the heading installation error estimation model.

6. The device according to claim 5, wherein: The error determination module is further configured to: Get the current speed, current yaw rate and current wheel angle; In response to the current speed satisfying a corresponding preset speed threshold and the current wheel slip angle satisfying a corresponding preset slip angle threshold, the current yaw rate, the current wheel slip angle, the wheelbase and the current speed are input into a preset kinematic model to obtain a current center of mass sideslip angle; The arc tangent function of the ratio of the longitudinal speed of the current speed to the lateral speed of the current speed and the current center of mass sideslip angle are input into the heading installation error estimation model to obtain the heading installation error.

7. The device according to claim 5 or 6, further comprising: An error acquisition module is configured to acquire a heading installation error within a preset time period; The filtering processing module is configured to perform filtering processing on the heading installation error within a preset time period by using mean filtering to obtain a filtered heading installation error.

8. The device according to claim 7, wherein: The mean filtering includes: Kalman filtering.

9. An electronic device, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 4.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 4.

11. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 4.

12. A vehicle comprising the electronic device according to claim 9.

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