Vehicle chassis parameter calibration method, device, electronic equipment and storage medium

Abstract

The present invention discloses a vehicle chassis parameter calibration method, device, electronic device, and storage medium. The method includes: obtaining an initial state quantity of a target vehicle; the initial state quantity includes the original chassis parameters of the target vehicle and the original positioning result of the target vehicle; determining a predicted positioning fusion result of the target vehicle at the current moment based on the initial state quantity; predicting the initial state quantity based on the predicted positioning fusion result at the current moment to obtain a predicted state quantity; and updating the predicted state quantity based on the predicted state quantity and positioning measurement information of the target vehicle at the current moment to obtain updated chassis parameters of the target vehicle at the current moment. The technical solution of the embodiment of the present invention can improve the robustness of the chassis parameter calibration result and the reliability of the vehicle positioning fusion result.

Description

Technical Field

[0001] The present invention relates to the field of fusion positioning technology, and in particular to a vehicle chassis parameter calibration method, device, electronic equipment and storage medium. Background Art

[0002] With the rapid development of autonomous driving technology, the requirements for the positioning accuracy of autonomous vehicles are becoming increasingly higher. Currently, the positioning of autonomous vehicles is mostly achieved based on fusion positioning technology, which combines data collected by multiple sensors to provide more accurate and reliable positioning results.

[0003] In the prior art, before performing fusion positioning on a vehicle, it is necessary to calibrate the chassis parameters of the vehicle (such as speed and steering, etc.) in advance, and obtain the positioning fusion result based on the calibrated chassis parameters.

[0004] However, since the existing technology calibrates the vehicle's chassis parameters in advance, the predetermined chassis parameters cannot change with the vehicle's actual driving dynamics, resulting in inaccurate vehicle calibration. When the vehicle is in extreme driving conditions, the calibrated chassis parameters are no longer applicable. Summary of the Invention

[0005] The present invention provides a vehicle chassis parameter calibration method, device, electronic equipment and storage medium, which can improve the robustness of chassis parameter calibration results and the reliability of vehicle positioning fusion results.

[0006] In a first aspect, the present invention provides a vehicle chassis parameter calibration method, comprising:

[0007] Obtaining the initial state of the target vehicle; the initial state includes the original chassis parameters of the target vehicle and the original positioning result of the target vehicle;

[0008] Determining a prediction and positioning fusion result of the target vehicle at the current moment according to the initial state quantity;

[0009] Predicting the initial state quantity according to the prediction positioning fusion result at the current moment to obtain a predicted state quantity;

[0010] The predicted state quantity is updated according to the predicted state quantity and the positioning measurement information of the target vehicle at the current moment to obtain the updated chassis parameters of the target vehicle at the current moment.

[0011] In a second aspect, the present invention provides a vehicle chassis parameter calibration device, comprising:

[0012] The first acquisition module is used to obtain the initial state quantity of the target vehicle; the initial state quantity includes the original chassis parameters of the target vehicle and the original positioning result of the target vehicle;

[0013] A first prediction module is used to determine the prediction positioning fusion result of the target vehicle at the current moment according to the initial state quantity;

[0014] A second prediction module is used to predict the initial state quantity according to the prediction positioning fusion result at the current moment to obtain a predicted state quantity;

[0015] The second acquisition module is used to update the predicted state quantity according to the predicted state quantity and the positioning measurement information of the target vehicle at the current moment, so as to obtain the updated chassis parameters of the target vehicle at the current moment.

[0016] In a third aspect, the present invention provides an electronic device, comprising:

[0017] at least one processor; and

[0018] a memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the vehicle chassis parameter calibration method described in any embodiment of the present invention.

[0020] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle chassis parameter calibration method described in any embodiment of the present invention when executed.

[0021] The technical solution provided by the embodiment of the present invention obtains the initial state quantity of the target vehicle; the initial state quantity includes the original chassis parameters of the target vehicle and the original positioning result of the target vehicle; based on the initial state quantity, a predicted positioning fusion result of the target vehicle at the current moment is determined; based on the predicted positioning fusion result at the current moment, the initial state quantity is predicted to obtain a predicted state quantity; based on the predicted state quantity and the positioning measurement information of the target vehicle at the current moment, the predicted state quantity is updated to obtain the updated chassis parameters of the target vehicle at the current moment, thereby improving the robustness of the chassis parameter calibration result.

[0022] 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 invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 This is a flow chart of a vehicle chassis parameter calibration method provided in an embodiment of the present invention;

[0025] Figure 2 is a flow chart of another vehicle chassis parameter calibration method provided according to an embodiment of the present invention;

[0026] Figure 3 is a flow chart of another vehicle chassis parameter calibration method provided according to an embodiment of the present invention;

[0027] Figure 4 2 is a schematic structural diagram of a vehicle chassis parameter calibration device provided according to an embodiment of the present invention;

[0028] Figure 5 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] In the prior art, chassis parameters (such as speed and steering) are pre-calibrated before vehicle fusion positioning. However, these pre-determined chassis parameters cannot change with the vehicle's actual driving dynamics, resulting in inaccurate vehicle calibration. Furthermore, the calibrated chassis parameters are no longer applicable when the vehicle is under extreme driving conditions. Therefore, the present invention provides a vehicle chassis parameter calibration method.

[0032] Figure 1 This is a flow chart of a vehicle chassis parameter calibration method provided in the first embodiment of the present invention. This embodiment is applicable to real-time online calibration of vehicle chassis parameters. The method can be executed by a vehicle chassis parameter calibration device. The vehicle chassis parameter calibration device can be implemented in the form of hardware and / or software. The vehicle chassis parameter calibration device can be configured in an electronic device (such as a terminal or server). Figure 1 As shown, the method includes:

[0033] Step 110: Acquire the initial state quantity of the target vehicle; the initial state quantity includes the original chassis parameters of the target vehicle and the original positioning result of the target vehicle.

[0034] In this embodiment, the target vehicle may be a vehicle waiting for chassis parameter calibration. Optionally, the original chassis parameters of the target vehicle, such as the chassis speed coefficient and chassis steering coefficient of the target vehicle, may be obtained based on the wheel speed odometer on the target vehicle.

[0035] In addition, the original positioning result of the target vehicle can also be obtained. The original positioning result can be based on the positioning result determined by the sensor when the vehicle is started. The original positioning result can also be the positioning result of the target vehicle at the last moment. Specifically, if the last moment is the initial moment of the vehicle chassis parameter calibration process, the original positioning result can be obtained based on the preset positioning sensor collection. The positioning sensor can include any one of a global positioning system (GPS), a laser simultaneous positioning and mapping (SLAM) tool, and a visual SLAM tool.

[0036] In this step, after obtaining the original chassis parameters of the target vehicle and the original positioning result of the target vehicle, the original chassis parameters and the original positioning result can be combined to obtain the initial state of the target vehicle at the current moment.

[0037] Step 120: Determine the predicted positioning fusion result of the target vehicle at the current moment based on the initial state quantity.

[0038] In this step, optionally, when determining the predicted positioning fusion result of the target vehicle at the current moment, a linear operation can be performed on the original positioning result of the target vehicle according to the original chassis parameters of the target vehicle to obtain the predicted positioning fusion result at the current moment.

[0039] Step 130: predict the initial state quantity according to the predicted positioning fusion result at the current moment to obtain the predicted state quantity.

[0040] In this step, optionally, when predicting the initial state quantity, a linear or nonlinear operation may be performed on the predicted positioning fusion result and the initial state quantity to obtain the predicted state quantity.

[0041] Step 140: Update the predicted state quantity according to the predicted state quantity and the positioning measurement information of the target vehicle at the current moment to obtain the updated chassis parameters of the target vehicle at the current moment.

[0042] In this embodiment, the positioning measurement information may be the current position information of the target vehicle collected by a positioning sensor. Optionally, a linear or nonlinear operation may be performed on the positioning measurement information and the predicted state quantity to obtain an updated predicted state quantity. The updated chassis parameters of the target vehicle at the current moment are then extracted from the updated predicted state quantity.

[0043] In this embodiment, the predicted positioning fusion result for the target vehicle at the current moment is determined using the target vehicle's initial state at the current moment. This state is then updated based on the current positioning measurement information, enabling online calibration of the target vehicle's chassis parameters. Compared to the prior art method of pre-calibrating chassis parameters before the vehicle is used, this method avoids the issue of calibrated chassis parameters becoming inappropriate under extreme driving conditions. This improves the robustness of the chassis parameter calibration results and facilitates vehicle positioning fusion.

[0044] The technical solution provided by the embodiment of the present invention obtains the initial state quantity of the target vehicle; the initial state quantity includes the original chassis parameters of the target vehicle and the original positioning result of the target vehicle, determines the predicted positioning fusion result of the target vehicle at the current moment based on the initial state quantity, predicts the initial state quantity based on the predicted positioning fusion result at the current moment to obtain the predicted state quantity, updates the predicted state quantity based on the predicted state quantity and the positioning measurement information of the target vehicle at the current moment, and obtains the updated chassis parameters of the target vehicle at the current moment. This technical solution can improve the robustness of the chassis parameter calibration result and the reliability of the vehicle positioning fusion result.

[0045] Figure 2This is a flow chart of another vehicle chassis parameter calibration method provided in this embodiment. In this embodiment, the technical solution of this embodiment can be combined with one or more methods in the solutions of the above embodiments, such as Figure 2 As shown, the method provided in this embodiment may further include:

[0046] Step 210: Acquire the initial state of the target vehicle; the initial state includes the original chassis parameters of the target vehicle and the original positioning result of the target vehicle.

[0047] In a specific embodiment, the initial state X of the target vehicle can be obtained. Specifically, X=(x, y, θ, k v ,k0,k1).

[0048] Among them, the chassis speed coefficient k v , chassis steering coefficients k0 and k1 are the original chassis parameters of the target vehicle. The east position x, north position y and heading θ of the target vehicle at the previous moment are the original positioning results of the target vehicle.

[0049] Step 220: Correct the wheel speed and steering information of the target vehicle at the current moment according to the original chassis parameters.

[0050] In this embodiment, since there is a deviation between the actual wheel speed and steering of the target vehicle and the wheel speed and steering measured by the wheel speed odometer, it is necessary to use the chassis speed coefficient and the chassis steering coefficient to correct the wheel speed and steering information measured by the wheel speed odometer.

[0051] In this step, optionally, the original chassis parameters may be used to perform linear operations on the wheel speed and steering information measured by the wheel speed odometer to obtain corrected wheel speed and steering information.

[0052] In a specific embodiment, it is assumed that the wheel speed and steering information measured by the wheel speed odometer are v can and β, then the corrected wheel speed can be k v v can , the corrected steering can be k0β+k1.

[0053] Step 230: Determine the predicted positioning fusion result of the target vehicle at the current moment based on the corrected wheel speed and steering information and the original positioning result of the target vehicle.

[0054] In this step, optionally, a linear or nonlinear operation can be performed on the original positioning result of the target vehicle according to the corrected wheel speed and steering information to obtain the predicted positioning fusion result at the current moment.

[0055] In one implementation of this embodiment, the predicted positioning fusion result of the target vehicle at the current moment is determined based on the corrected wheel speed and steering information, and the original positioning result of the target vehicle, including: constructing a vehicle dynamics model that matches the target vehicle; determining the predicted positioning fusion result of the target vehicle at the current moment based on the vehicle dynamics model, the corrected wheel speed and steering information, and the original positioning result of the target vehicle.

[0056] In this embodiment, a vehicle dynamics model matching the target vehicle can be constructed with the point on the target vehicle's rear axle as the origin. The predicted positioning fusion result of the target vehicle at the current moment is then determined based on the vehicle motion recursion rules preset in the vehicle dynamics model, the corrected wheel speed and steering information, and the target vehicle's original positioning result. Specifically, the predicted positioning fusion result of the target vehicle at the current moment can be determined using the following formula:

[0057] x'=x+k v v can dt*cosθ

[0058] y'=y+k v v can dt*sinθ

[0059]

[0060] Where x', y', and θ' are the predicted positioning fusion results at the current moment. x' is the predicted east position of the target vehicle at the current moment, y' is the predicted north position of the target vehicle at the current moment, and θ' is the predicted heading of the target vehicle at the current moment. dt is the time interval between the current moment and the previous moment, and L is the wheelbase of the target vehicle.

[0061] The advantage of this setting is that by constructing a vehicle dynamics model that matches the target vehicle and determining the predicted positioning fusion result at the current moment based on the vehicle dynamics model, the reliability of the predicted positioning fusion result can be guaranteed and the robustness of the subsequent chassis parameter calibration results can be improved.

[0062] Step 240: predict the initial state quantity according to the predicted positioning fusion result at the current moment to obtain the predicted state quantity.

[0063] Step 250: Update the predicted state quantity according to the predicted state quantity and the positioning measurement information of the target vehicle at the current moment to obtain the updated chassis parameters of the target vehicle at the current moment.

[0064] Step 260: Use the updated chassis parameters as the original chassis parameters of the target vehicle at the next moment.

[0065] The advantage of this setting is that the chassis parameters of the target vehicle can be iteratively updated, thereby improving the robustness of the chassis parameter calibration results in the subsequent process and the reliability of the positioning fusion results.

[0066] The technical solution provided by the embodiment of the present invention obtains the initial state quantity of the target vehicle, corrects the wheel speed and steering information of the target vehicle at the current moment according to the original chassis parameters, determines the predicted positioning fusion result of the target vehicle at the current moment according to the corrected wheel speed and steering information and the original positioning result of the target vehicle, predicts the initial state quantity according to the predicted positioning fusion result to obtain the predicted state quantity, updates the predicted state quantity according to the predicted state quantity and the positioning measurement information of the target vehicle at the current moment to obtain the updated chassis parameters of the target vehicle at the current moment, and uses the updated chassis parameters as the original chassis parameters of the target vehicle at the next moment. This technical means can improve the robustness of the chassis parameter calibration result of the target vehicle.

[0067] Figure 3 This is a flow chart of another vehicle chassis parameter calibration method provided in this embodiment. In this embodiment, the technical solution of this embodiment can be combined with one or more methods in the solutions of the above embodiments, such as Figure 3 As shown, the method provided in this embodiment may further include:

[0068] Step 301: Acquire the initial state of the target vehicle; the initial state includes the original chassis parameters of the target vehicle and the original positioning result of the target vehicle.

[0069] Step 302: Determine the predicted positioning fusion result of the target vehicle at the current moment based on the initial state quantity.

[0070] Step 303: According to the prediction positioning fusion result at the current moment, partial derivatives of the components in the initial state quantity are obtained to obtain a state transfer matrix corresponding to the filter.

[0071] In this step, the x, y, θ, k in the initial state can be calculated based on the predicted positioning fusion results. v , k0 and k1 are derived in sequence to obtain the state transfer matrix F corresponding to the Kalman filter.

[0072] In a specific embodiment, taking the predicted positioning fusion result (x', y', and θ') in step 230 as an example, after taking partial derivatives of each component in the initial state quantity based on the predicted positioning fusion result, the obtained state transfer matrix F can be expressed as:

[0073]

[0074] Step 304: predict the initial state quantity in the filter according to the state transfer matrix corresponding to the filter to obtain the predicted state quantity.

[0075] In this step, the initial state quantity X can be predicted according to the state transfer matrix F to obtain the predicted state quantity X'. Specifically, the predicted state quantity X' can be calculated using the following formula:

[0076] X′=FX

[0077] Step 305: Obtain the original covariance matrix of the filter at the current moment.

[0078] In this embodiment, optionally, if it is the first time that the chassis parameters of the target vehicle are calibrated, the default covariance matrix in the filter can be obtained as the original covariance matrix at the current moment; if it is not the first time that the chassis parameters of the target vehicle are calibrated, the covariance matrix generated during the chassis parameter calibration process at the previous moment can be obtained as the original covariance matrix corresponding to the filter at the current moment.

[0079] Step 306: Predict the original covariance matrix according to the state transfer matrix and the preset noise matrix corresponding to the filter to obtain a predicted covariance matrix.

[0080] In a specific embodiment, assuming that the original covariance matrix of the filter at the current moment is P and the preset noise matrix is ​​Q, the predicted covariance matrix P' can be obtained by the following formula:

[0081] P'=FPF T +Q

[0082] Step 307: Obtain the Kalman gain preset in the filter.

[0083] In this step, the default Kalman gain in the filter can be obtained, or the Kalman gain K can be calculated by predicting the covariance matrix P', the preset measurement matrix H corresponding to the filter, and the preset observation noise matrix R. Specifically, the Kalman gain K can be calculated by the following formula:

[0084] K=P'H T (HP'H T +R) -1

[0085] Step 308: Update the predicted state quantity in the filter according to the Kalman gain, the predicted state quantity, and the positioning measurement information of the target vehicle at the current moment to obtain the updated chassis parameters of the target vehicle at the current moment.

[0086] In this step, the target vehicle's current positioning information z can be obtained through GPS, z = (x0, y0, θ0). Among them, x0 is the target vehicle's east position at the current moment measured by GPS, y0 is the target vehicle's north position at the current moment measured by GPS, and θ0 is the target vehicle's heading at the current moment measured by GPS.

[0087] In a specific embodiment, the predicted state quantity X' can be updated by the following formula to obtain the updated predicted state quantity X":

[0088] X'=X'+K(z-HX')

[0089] In this embodiment, the updated predicted state X' includes the positioning fusion results x', y' and θ' of the target vehicle at the current moment, as well as the updated chassis parameter k v ”, k0” and k1”.

[0090] In one implementation of this embodiment, after the predicted state quantity is updated, it also includes: obtaining the positioning fusion result of the target vehicle at the current moment based on the updated predicted state quantity; and using the positioning fusion result at the current moment as the original positioning result in the initial state quantity of the target vehicle at the next moment.

[0091] The advantage of this setting is that the chassis parameters of the target vehicle at the next moment can be quickly updated, thereby improving the updating efficiency of the chassis parameters in the subsequent process.

[0092] Step 309: Update the predicted covariance matrix according to the Kalman gain, the preset unit matrix and the preset observation matrix corresponding to the filter, and use the updated predicted covariance matrix as the original covariance matrix corresponding to the filter at the next moment.

[0093] In this embodiment, assuming that the preset identity matrix is ​​I, the prediction covariance matrix P' can be updated according to the following formula:

[0094] P"=(I-KH)P'

[0095] Among them, P” is the updated prediction covariance matrix. After obtaining the updated prediction covariance matrix P”, P” can be used as the original covariance matrix corresponding to the filter at the next moment to facilitate the calibration of the chassis parameters of the target vehicle at the next moment.

[0096] The technical solution provided by the embodiment of the present invention obtains the initial state quantity of the target vehicle, determines the predicted positioning fusion result of the target vehicle at the current moment according to the initial state quantity, calculates the partial derivative of each component in the initial state quantity according to the predicted positioning fusion result, obtains the state transfer matrix of the filter, predicts the initial state quantity in the filter according to the state transfer matrix to obtain the predicted state quantity, obtains the original covariance matrix of the filter at the current moment, predicts the original covariance matrix according to the state transfer matrix and a preset noise matrix to obtain the predicted covariance matrix, obtains the Kalman gain preset in the filter, updates the predicted state quantity in the filter according to the Kalman gain, the predicted state quantity, and the positioning measurement information of the target vehicle at the current moment, obtains the updated chassis parameters of the target vehicle at the current moment, updates the predicted covariance matrix according to the Kalman gain, the preset unit matrix and the preset observation matrix, and uses the updated predicted covariance matrix as the original covariance matrix corresponding to the filter at the next moment. The technical means can improve the robustness of the calibration results of the chassis parameters of the target vehicle.

[0097] Figure 4 This is a structural diagram of a vehicle chassis parameter calibration device provided by an embodiment of the present invention, wherein the vehicle chassis parameter calibration device includes: a first acquisition module 410, a first prediction module 420, a second prediction module 430 and a second acquisition module 440.

[0098] The first acquisition module is used to obtain the initial state quantity of the target vehicle; the initial state quantity includes the original chassis parameters of the target vehicle and the original positioning result of the target vehicle;

[0099] A first prediction module is used to determine the prediction positioning fusion result of the target vehicle at the current moment according to the initial state quantity;

[0100] A second prediction module is used to predict the initial state quantity according to the prediction positioning fusion result at the current moment to obtain a predicted state quantity;

[0101] The second acquisition module is used to update the predicted state quantity according to the predicted state quantity and the positioning measurement information of the target vehicle at the current moment, so as to obtain the updated chassis parameters of the target vehicle at the current moment.

[0102] The technical solution provided by the embodiment of the present invention obtains the initial state quantity of the target vehicle; the initial state quantity includes the original chassis parameters of the target vehicle and the original positioning result of the target vehicle, determines the predicted positioning fusion result of the target vehicle at the current moment based on the initial state quantity, predicts the initial state quantity based on the predicted positioning fusion result at the current moment to obtain the predicted state quantity, updates the predicted state quantity based on the predicted state quantity and the positioning measurement information of the target vehicle at the current moment, and obtains the updated chassis parameters of the target vehicle at the current moment. This technical solution can improve the robustness of the chassis parameter calibration result and the reliability of the vehicle positioning fusion result.

[0103] Based on the above embodiment, the original positioning result is acquired based on the positioning sensor of the target vehicle.

[0104] The first prediction module 420 includes:

[0105] a correction unit, configured to correct the wheel speed and steering information of the target vehicle at a current moment according to the original chassis parameters;

[0106] A correction information processing unit is used to determine a predicted positioning fusion result of the target vehicle at the current moment based on the corrected wheel speed and steering information and the original positioning result of the target vehicle;

[0107] A model building unit, configured to build a vehicle dynamics model matching the target vehicle;

[0108] The model processing unit is used to determine the predicted positioning fusion result of the target vehicle at the current moment based on the vehicle dynamics model, the corrected wheel speed and steering information, and the original positioning result of the target vehicle.

[0109] The second prediction module 430 includes:

[0110] A transfer matrix determination unit is used to obtain a state transfer matrix corresponding to the filter by calculating partial derivatives of each component in the initial state quantity according to the predicted positioning fusion result at the current moment;

[0111] a transfer matrix processing unit, configured to predict an initial state quantity in the filter according to a state transfer matrix corresponding to the filter to obtain a predicted state quantity;

[0112] A covariance matrix acquisition unit, used to obtain the original covariance matrix of the filter at the current moment;

[0113] The covariance matrix prediction unit is used to predict the original covariance matrix according to the state transfer matrix and the preset noise matrix corresponding to the filter to obtain a predicted covariance matrix.

[0114] The second acquisition module 440 includes:

[0115] a Kalman gain acquisition unit, configured to acquire a Kalman gain preset in the filter, and update the predicted state quantity in the filter based on the Kalman gain, the predicted state quantity, and the positioning measurement information of the target vehicle at the current moment, to obtain updated chassis parameters of the target vehicle at the current moment;

[0116] a chassis parameter processing unit, configured to use the updated chassis parameters as original chassis parameters of the target vehicle at a next moment;

[0117] A prediction covariance matrix updating unit is used to update the prediction covariance matrix according to the Kalman gain, the preset unit matrix and the preset observation matrix corresponding to the filter, and use the updated prediction covariance matrix as the original covariance matrix corresponding to the filter at the next moment;

[0118] The positioning fusion result acquisition unit is used to obtain the positioning fusion result of the target vehicle at the current moment based on the updated predicted state quantity; and use the positioning fusion result at the current moment as the original positioning result in the initial state quantity of the target vehicle at the next moment.

[0119] The above device can execute the methods provided by all the above embodiments of the present invention, and has the corresponding functional modules and beneficial effects of executing the above methods. For technical details not fully described in the embodiments of the present invention, please refer to the methods provided by all the above embodiments of the present invention.

[0120] Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention 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 (such as helmets, glasses, watches, etc.) 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 invention described and / or claimed herein.

[0121] like Figure 5As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0122] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0123] In addition to the above-mentioned method and device, the embodiment of the present disclosure may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the chassis parameter calibration method provided by the embodiment of the present disclosure.

[0124] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the vehicle chassis parameter calibration method.

[0125] In some embodiments, the vehicle chassis parameter calibration method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the vehicle chassis parameter calibration method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the vehicle chassis parameter calibration method in any other appropriate manner (for example, by means of firmware).

[0126] Various embodiments of the systems and techniques described 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), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes 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.

[0127] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0128] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0129] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device 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 electronic device. 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).

[0130] The systems and techniques described herein can 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 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 can 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), a blockchain network, and the Internet.

[0131] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0132] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0133] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A vehicle chassis parameter calibration method, characterized in that: Used for real-time online calibration of vehicle chassis parameters, including: Obtaining the initial state quantity of the target vehicle; the initial state quantity includes the original chassis parameters of the target vehicle and the original positioning result of the target vehicle; the original chassis parameters of the target vehicle include the chassis speed coefficient and the chassis steering coefficient; Correcting the wheel speed and steering information of the target vehicle at the current moment according to the original chassis parameters; Constructing a vehicle dynamics model matching the target vehicle; Determining a predicted positioning fusion result of the target vehicle at the current moment based on the vehicle dynamics model, the corrected wheel speed and steering information, and the original positioning result of the target vehicle; Predicting the initial state quantity according to the prediction positioning fusion result at the current moment to obtain a predicted state quantity; According to the predicted state quantity and the positioning measurement information of the target vehicle at the current moment, the predicted state quantity is updated to obtain the updated chassis parameters of the target vehicle at the current moment; The prediction and positioning fusion result of the target vehicle at the current moment is determined by the following formula: x'=x+k v v can dt*cosθ y'=y+k v v can dt*sinθ Among them, x', y' and θ' are the predicted positioning fusion results at the current moment, x' is the predicted east position of the target vehicle at the current moment, y' is the predicted north position of the target vehicle at the current moment, θ' is the predicted heading of the target vehicle at the current moment, dt is the time interval between the current moment and the previous moment, L is the wheelbase of the target vehicle, k v v can is the corrected wheel speed, k0β+k1 is the corrected steering, and x, y, and θ are the original positioning results of the target vehicle.

2. The method according to claim 1, characterized in that The original positioning result is acquired based on the positioning sensor of the target vehicle.

3. The method according to claim 1, characterized in that According to the prediction positioning fusion result at the current moment, the initial state quantity is predicted to obtain the predicted state quantity, including: According to the prediction positioning fusion result at the current moment, partial derivatives are obtained for each component in the initial state quantity to obtain the state transfer matrix corresponding to the filter; According to the state transfer matrix corresponding to the filter, the initial state quantity is predicted in the filter to obtain the predicted state quantity.

4. The method according to claim 3, characterized in that The predicted state quantity is updated according to the predicted state quantity and the positioning measurement information of the target vehicle at the current moment to obtain the updated chassis parameters of the target vehicle at the current moment, including: Obtaining a Kalman gain preset in the filter; According to the Kalman gain, the predicted state quantity, and the positioning measurement information of the target vehicle at the current moment, the predicted state quantity is updated in the filter to obtain the updated chassis parameters of the target vehicle at the current moment.

5. The method according to claim 4, characterized in that After predicting the initial state quantity based on the predicted positioning fusion result at the current moment to obtain the predicted state quantity, the method further includes: Obtaining the original covariance matrix of the filter at the current moment; Predicting the original covariance matrix according to the state transfer matrix and the preset noise matrix corresponding to the filter to obtain a predicted covariance matrix; After updating the predicted state quantity according to the predicted state quantity and the positioning measurement information of the target vehicle at the current moment to obtain the updated chassis parameters of the target vehicle at the current moment, the method further includes: The predicted covariance matrix is ​​updated according to the Kalman gain, the preset unit matrix and the preset observation matrix corresponding to the filter, and the updated predicted covariance matrix is ​​used as the original covariance matrix corresponding to the filter at the next moment.

6. The method according to claim 1, characterized in that After obtaining the updated chassis parameters of the target vehicle at the current moment, the following steps are also included: The updated chassis parameters are used as the original chassis parameters of the target vehicle at the next moment.

7. The method according to claim 1, characterized in that After updating the predicted state quantity according to the predicted state quantity and the positioning measurement information of the target vehicle at the current moment, the method further includes: According to the updated predicted state quantity, the positioning fusion result of the target vehicle at the current moment is obtained; The positioning fusion result at the current moment is used as the original positioning result in the initial state quantity of the target vehicle at the next moment.

8. A vehicle chassis parameter calibration device, characterized in that: Used for real-time online calibration of vehicle chassis parameters, including: The first acquisition module is used to obtain the initial state quantity of the target vehicle; the initial state quantity includes the original chassis parameters of the target vehicle and the original positioning result of the target vehicle; the original chassis parameters of the target vehicle include the chassis speed coefficient and the chassis steering coefficient; The first prediction module includes: a correction unit, configured to correct the wheel speed and steering information of the target vehicle at a current moment according to the original chassis parameters; A model building unit, configured to build a vehicle dynamics model matching the target vehicle; a model processing unit, configured to determine a predicted positioning fusion result of the target vehicle at a current moment based on the vehicle dynamics model, the corrected wheel speed and steering information, and the original positioning result of the target vehicle; A second prediction module is used to predict the initial state quantity according to the prediction positioning fusion result at the current moment to obtain a predicted state quantity; a second acquisition module, configured to update the predicted state quantity according to the predicted state quantity and the positioning measurement information of the target vehicle at the current moment, and obtain updated chassis parameters of the target vehicle at the current moment; The prediction and positioning fusion result of the target vehicle at the current moment is determined by the following formula: x'=x+k v v can dt*cosθ y'=y+k v v can dt*sinθ Among them, x', y' and θ' are the predicted positioning fusion results at the current moment, x' is the predicted east position of the target vehicle at the current moment, y' is the predicted north position of the target vehicle at the current moment, θ' is the predicted heading of the target vehicle at the current moment, dt is the time interval between the current moment and the previous moment, L is the wheelbase of the target vehicle, k v v can is the corrected wheel speed, k0β+k1 is the corrected steering, and x, y, and θ are the original positioning results of the target vehicle.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the vehicle chassis parameter calibration method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: A computer program is stored thereon, characterized in that when the program is executed by a processor, the vehicle chassis parameter calibration method according to any one of claims 1 to 7 is implemented.

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