Compensation Method for Lateral Acceleration of Vehicle, Radar, Device and Storage Medium

Abstract

An embodiment of the present invention discloses a method for compensating the lateral acceleration of a vehicle. Specifically, it includes: calculating at least two of the first to fourth lateral accelerations; and determining the final lateral acceleration of the vehicle based on at least two of the first to fourth lateral accelerations; wherein, the first lateral acceleration is obtained based on a self-learning method, the second lateral acceleration is obtained based on the lateral acceleration collected by a lateral acceleration sensor and the temperature of the lateral acceleration sensor, the third lateral acceleration is obtained based on the wheel speed of the vehicle and the road surface adhesion coefficient, and the fourth lateral acceleration is obtained based on the steering angle and wheel speed of the vehicle. By the method of the embodiment of the present invention, the accuracy of the lateral acceleration can be improved.

Description

Technical Field

[0001] The present invention relates to the compensation technology of vehicle lateral acceleration, and particularly to a method, a radar, a device and a storage medium for compensating the lateral acceleration of a vehicle. Background Art

[0002] In a vehicle, the lateral acceleration of the vehicle can be measured by an acceleration sensor. However, the lateral acceleration measured by the acceleration sensor may have errors and needs to be compensated. Summary of the Invention

[0003] Embodiments of the present invention provide a method, a radar, a device and a storage medium for compensating the lateral acceleration of a vehicle, which can improve the accuracy of the lateral acceleration.

[0004] A method for compensating the lateral acceleration of a vehicle according to an embodiment of the present invention includes: calculating at least two of the first to fourth lateral accelerations; and determining the final lateral acceleration of the vehicle based on at least two of the first to fourth lateral accelerations; wherein, the first lateral acceleration is obtained based on a self-learning method, the second lateral acceleration is obtained based on the lateral acceleration collected by a lateral acceleration sensor and the temperature of the lateral acceleration sensor, the third lateral acceleration is obtained based on the wheel speed of the vehicle and the road surface adhesion coefficient, and the fourth lateral acceleration is obtained based on the steering angle and the wheel speed of the vehicle.

[0005] Wherein, the method further includes: determining whether to activate the lateral acceleration compensation function based on the driving state of the vehicle; and when the lateral acceleration compensation function is activated, performing the step of calculating at least two of the first to fourth lateral accelerations.

[0006] Wherein, the driving state of the vehicle is defined by the road surface adhesion coefficient, the wheel speed, the left and right wheel speed difference of the front axle and the steering angle, and when the road surface adhesion coefficient, the wheel speed, the left and right wheel speed difference of the front axle and the steering angle are all within their respective preset ranges, the lateral acceleration compensation function is activated.

[0007] Wherein, the step of calculating the first lateral acceleration includes: when the vehicle enters a predetermined driving condition, collecting multiple values of the lateral acceleration sensor, and taking the average value of the multiple values as the first lateral acceleration; wherein, the predetermined driving condition includes: the driving mileage of the vehicle is greater than a mileage threshold, the vehicle speed of the vehicle is greater than a vehicle speed threshold, and the vehicle is in a straight driving state.

[0008] Among them, the step of calculating the second lateral acceleration includes: determining in advance the conversion relationship between the acquisition value and temperature of the lateral acceleration sensor and the second lateral acceleration in a calibrated manner; and calculating the second lateral acceleration based on the conversion relationship.

[0009] Among them, the step of calculating the third lateral acceleration includes: obtaining the wheel speed and road surface adhesion coefficient of the vehicle; and inputting the wheel speed and road surface adhesion coefficient into a pre-trained neural network model to calculate the third lateral acceleration.

[0010] Among them, the step of calculating the fourth lateral acceleration includes: obtaining the wheel speed and steering angle of the vehicle; estimating an initial value of the fourth lateral acceleration based on the wheel speed and steering angle by using the kinematic formula and Kalman filter of the vehicle; calculating a median value of the fourth lateral acceleration based on the initial value of the fourth lateral acceleration by using an MPC model; and when the median value of the fourth lateral acceleration is within a predetermined threshold range of the acquisition value of the lateral acceleration sensor, outputting the median value of the fourth lateral acceleration as the fourth lateral acceleration.

[0011] A radar according to an embodiment of the present invention includes: a plurality of acceleration compensation modules respectively configured to calculate first to fourth lateral accelerations; and a fusion module configured to determine a final lateral acceleration of the vehicle based on at least two of the first to fourth lateral accelerations; wherein, the first lateral acceleration is obtained based on a self-learning method, the second lateral acceleration is obtained based on the lateral acceleration collected by a lateral acceleration sensor and the temperature of the lateral acceleration sensor, the third lateral acceleration value is obtained based on the wheel speed and road surface adhesion coefficient of the vehicle, and the fourth lateral acceleration is obtained based on the steering angle and wheel speed of the vehicle.

[0012] A computer device according to an embodiment of the present invention includes: a processor; and a memory configured to store executable instructions of the processor; wherein, the processor is configured to execute the executable instructions to implement the method according to an embodiment of the present invention.

[0013] A computer-readable storage medium according to an embodiment of the present invention stores a computer program thereon, and the computer program includes executable instructions, and when the executable instructions are executed by a processor, the method according to an embodiment of the present invention is implemented.

[0014] Advantages of the embodiments of the present invention:

[0015] In the embodiments of the present invention, multiple lateral acceleration values are calculated in multiple ways (or from multiple perspectives), and the final lateral acceleration value of the vehicle is determined by synthesizing the multiple lateral acceleration values, so that the accuracy of the lateral acceleration can be improved. Description of the Drawings

[0016] Other details and advantages of the present invention will become apparent from the following detailed description. It should be understood that the following drawings are merely schematic and thus should not be considered as limiting the present invention. The following will be described in detail with reference to the drawings, where:

[0017] Figure 1 is a schematic flowchart of an embodiment of a method for compensating the lateral acceleration of a vehicle according to the present invention;

[0018] Figure 2 is a schematic flowchart of another embodiment of a method for compensating the lateral acceleration of a vehicle according to the present invention;

[0019] Figure 3 is Figure 1 a schematic flowchart of an embodiment for calculating the first lateral acceleration in step S12 in

[0020] Figure 4A is Figure 1 a schematic flowchart of an embodiment for calculating the second lateral acceleration in step S12 in

[0021] Figure 4B is a schematic structural diagram of an embodiment of a test bench according to an embodiment of the present invention;

[0022] Figure 5 is Figure 1 a schematic flowchart of an embodiment for calculating the third lateral acceleration in step S12 in

[0023] Figure 6 is Figure 1 a schematic flowchart of an embodiment for calculating the fourth lateral acceleration in step S12 in

[0024] Figure 7 is a schematic structural diagram of an embodiment of a radar according to an embodiment of the present invention. Detailed Embodiments

[0025] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not used to limit the present invention.

[0026] In the description of the present invention, it should be understood that the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. Moreover, the terms "first", "second", etc. are applicable to distinguish similar objects and do not necessarily have to be used to describe a specific order or sequence. It should be understood that such terms can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein.

[0027] The method for compensating the lateral acceleration of a vehicle according to an embodiment of the present invention can be applied to radar products of vehicles, such as millimeter-wave radars, lidars, etc., to support functions such as adaptive cruise control (ACC). In addition, the compensation method according to an embodiment of the present invention can also be applied to other electronic devices in the vehicle to calculate the lateral acceleration of the vehicle. In this embodiment, the radar or other electronic devices do not directly use the lateral acceleration value obtained from the lateral acceleration sensor in the vehicle, but instead recalculate multiple lateral acceleration values through various methods and from various angles, and then fuse these lateral acceleration values to obtain a final lateral acceleration with high precision. Among them, the number of fused lateral acceleration values can be reasonably set according to factors such as the current working condition of the vehicle and the accuracy requirements of the application.

[0028] Specifically, this embodiment can compensate the lateral acceleration based on a self-learning method. The influence of temperature on the lateral acceleration sensor can be considered to compensate the lateral acceleration value. The lateral acceleration can be compensated based on the wheel speed and road surface adhesion coefficient of the vehicle by using a neural network model. And the lateral acceleration can be compensated based on the steering angle and wheel speed of the vehicle by using vehicle kinematic formulas, Kalman filtering, and MPC (Model Predictive Control) models. Through these compensations in various ways and from various angles, the finally obtained lateral acceleration can have higher accuracy.

[0029] The following will describe the solution of the embodiment of the present invention in detail with reference to the accompanying drawings.

[0030] As Figure 1 shown, it is a schematic flowchart of an embodiment of the method for compensating the lateral acceleration of a vehicle according to the present invention. The method flow includes:

[0031] Step S12: Calculate at least two of the first to fourth lateral accelerations. And

[0032] Step S14: Determine the final lateral acceleration value of the vehicle based on at least two of the first to fourth lateral accelerations.

[0033] Among them, before step S12, it can be first determined whether the lateral acceleration compensation function is activated. Only when this function is activated, the method flows shown in steps S12 and S14 are executed.

[0034] Specifically, as Figure 2 shown, in step S10, the driving state of the vehicle is obtained. Among them, the driving state of the vehicle can be defined by parameters such as the road surface adhesion coefficient, wheel speed, left and right wheel speed difference of the front axle, and steering angle. Among them, the road surface adhesion coefficient can be calculated based on a pre-established vehicle dynamics model based on the road surface - tire. For example, by inputting the real-time wheel speed and steering angle into this vehicle dynamics model, the road surface adhesion coefficient can be calculated. Among them, signals such as wheel speed and steering angle can be collected through corresponding sensors in the vehicle.

[0035] Next, in step S11, it is determined whether the vehicle is in a predetermined driving state. If so, the lateral acceleration compensation function is activated, for example, setting the lateral acceleration compensation flag bit. If not, the lateral acceleration compensation function is not activated, and the driving state of the vehicle is continuously monitored. Among them, the vehicle being in a predetermined driving state can be, for example, that the road surface adhesion coefficient, wheel speed, left and right wheel speed difference of the front axle, and steering angle are all within their respective preset ranges. For example, as shown in Table 1, A, B, C, and D all represent specific ranges, which can be set manually based on experience. When the adhesion coefficient, wheel speed, left and right wheel speed difference of the front axle, and steering angle are respectively within ranges A1, B1, C1, and D1, the compensation function is activated. Among them, in this embodiment, by setting the ranges of the adhesion coefficient, wheel speed, left and right wheel speed difference of the front axle, and steering angle, it can be ensured that in some abnormal driving states (for example, when the vehicle is in extreme testing), the acceleration compensation function is not activated, while in normal driving states, the acceleration compensation function is activated. Among them, a decision tree model can be used to determine whether the vehicle is in a predetermined driving state.

[0036] Table 1:

[0037] Number Adhesion coefficient Wheel speed Wheel speed difference Steering angle Activation flag 1 A1 B1 C1 D1 Activate 2 A2 B2 C2 D2 Inactivate … … … … … …

[0038] Among them, in step S12, the first lateral acceleration can be calculated based on a self-learning method. Specifically, as Figure 3 shown, it is a schematic flow diagram of an embodiment of the method for calculating the first lateral acceleration. In Figure 3In this case, in step S30, the driving condition of the vehicle is obtained. In step S32, when the vehicle enters a predetermined driving condition, multiple values of the lateral acceleration sensor are collected. And, in step S34, the average value of the collected multiple values is calculated to be used as the first lateral acceleration. Among them, the predetermined driving condition can be, for example: the driving mileage of the vehicle is greater than the mileage threshold, the vehicle speed of the vehicle is greater than the vehicle speed threshold, and the vehicle is driving straight. Among them, the magnitudes of the mileage threshold and the vehicle speed threshold can be preset in advance. In this embodiment, when the vehicle is in a specific driving condition, the lateral acceleration sensor itself is used to compensate the lateral acceleration, so it is a self-learning method.

[0039] Among them, in step S12, the calculation of the second lateral acceleration value mainly considers the influence of temperature on the accuracy of the lateral acceleration sensor. Specifically, as Figure 4A shown, it is a schematic flowchart of an embodiment of the method for calculating the second lateral acceleration value. As Figure 4A shown, in step S40, by means of calibration (test), the conversion relationship between the collected value and temperature of the lateral acceleration sensor and the second lateral acceleration is determined in advance. Then, in step S42, based on the pre-determined conversion relationship, the second lateral acceleration value is calculated.

[0040] Specifically, first, a lateral acceleration sensor temperature test is carried out, and the test bench can refer to Figure 4B shown. In Figure 4B , 1 is the bench installation and fixing platform, 2 is the environmental chamber, 3 and 4 are the left and right hub motors, 5 is the rotating shaft, and 6 is the differential gearbox and acceleration sensor integration module. During calibration, the left and right hub motors 3 and 4 simulate the rotation of the wheels, the differential gearbox can simulate the speed difference between the left and right wheels in a turning condition, the environmental chamber 2 can simulate different external temperatures, and the acceleration sensor includes a vehicle acceleration sensor and a high-precision acceleration sensor. During the calibration process, the difference between the two acceleration sensors in the specified condition at different temperatures will be recorded as the acceleration sensor compensation value (compensation value = high-precision acceleration sensor value - vehicle acceleration sensor value), so as to obtain the compensation value relationship of the corresponding acceleration value at different temperatures, and fit the corresponding polynomial relationship. The formula is as follows:

[0041] Latoffset2 = f(LatAcc) + f(Temp) + Ca;

[0042] In the formula, Latoffset2 is the compensated lateral acceleration, LatAcc is the lateral acceleration collected by the vehicle sensor, Temp is the sensor temperature, and Ca is the acceleration compensation constant.

[0043] In addition, if the sensor temperature cannot be directly collected, it can be calculated from the external temperature. That is, considering the actual installation state of the acceleration sensor and the influence of the vehicle speed, the relationship between the sensor temperature and the room temperature can be obtained through experimental data as follows:

[0044] Temp=f(t)+f(Vehspd)+Ct;

[0045] In the formula, t is the external temperature, Vehspd is the vehicle speed, and Ct is the temperature constant.

[0046] Among them, in step S12, the calculation of the third lateral acceleration value is based on the wheel speed and the adhesion coefficient, and is calculated through a neural network model. Specifically, as Figure 5 shown, it is a flowchart of an embodiment of the method for calculating the third lateral acceleration value. In Figure 5 , in step S50, the wheel speed and the road surface adhesion coefficient of the vehicle are obtained. Among them, the wheel speed can be obtained from the wheel speed sensor, and the road surface adhesion coefficient can be obtained based on the aforementioned vehicle dynamics model based on the road surface - tire. In step S52, the wheel speed and the road surface adhesion coefficient are input into a pre - trained neural network model (such as, a BP neural network model) to calculate the third lateral acceleration. Among them, the training data of the BP neural network can be sourced from vehicle tests, and this network model can be continuously optimized, for example, based on the difference between the third lateral acceleration value and the final fused lateral acceleration value to continuously optimize and improve the accuracy.

[0047] Among them, in step S12, the fourth lateral acceleration value is calculated based on the steering angle and the vehicle speed, in combination with the vehicle kinematic formula, the Kalman filter, and the MPC model. Specifically, as Figure 6 shown, it is a flowchart of an embodiment of the method for calculating the fourth lateral acceleration value. As Figure 6As shown, in step S60, the wheel speed and steering angle of the vehicle are obtained. For example, the wheel speed and steering angle signals can be collected by relevant sensors. In step S62, based on the wheel speed and steering angle, the initial value of the fourth lateral acceleration is estimated using the vehicle kinematic formula and the Kalman filter. Among them, the wheel speed and steering angle can be substituted into the vehicle kinematic formula to calculate the lateral acceleration value, and then the calculated value is filtered using the Kalman filter to obtain the initial value of the lateral acceleration. Then, in step S64, based on the initial value, the median value of the fourth lateral acceleration is calculated using the MPC model. Then, in step S66, it is determined whether the difference between the median value of the fourth lateral acceleration and the acceleration collected by the lateral acceleration sensor is within a predetermined threshold range. If it is, the median value of the fourth lateral acceleration is used as the fourth lateral acceleration output. If it is not within the predetermined threshold range, the MPC model is optimized based on the difference between the median value of the fourth lateral acceleration and the acceleration collected by the lateral acceleration sensor, and the fourth lateral acceleration is not output.

[0048] Among them, in step S14, the first to fourth lateral accelerations can be fused based on a weighted fusion method. For example, the weight coefficients of the first to fourth lateral accelerations can be set as: W1, W2, W3, W4, where the weight coefficients satisfy W1 + W2 + W3 + W4 = 1. The weight coefficients corresponding to the first to fourth lateral accelerations can be adjusted according to design requirements and application scenarios. For example, in some scenarios, only the second and third lateral accelerations may exist, then W2 and W3 can be adjusted. The fused lateral acceleration value Latoffset_merge satisfies the following formula: Latoffset_merge = LatOffset1 * W1 + LatOffset2 * W2 + LatOffset3 * W3 + LatOffset4 * W4. LatOffset1 to 4 represent the first to fourth lateral accelerations respectively.

[0049] In addition, after obtaining the fused lateral acceleration, a limit processing can be further performed on the lateral acceleration, that is, if the fused lateral acceleration is greater than the preset maximum lateral acceleration, it is limited to the maximum lateral acceleration; if it is less than the preset minimum lateral acceleration, it is limited to the minimum lateral acceleration.

[0050] As Figure 7As shown in the figure, it is a schematic structural diagram of an embodiment of the radar of the present invention. The radar 7 includes: first to fourth acceleration compensation modules 70 to 73, which are respectively used to calculate first to fourth lateral acceleration values; and an acceleration fusion module 74, which is used to determine the final lateral acceleration of the vehicle based on at least two of the first to fourth lateral acceleration values. Among them, the first lateral acceleration is obtained based on a self-learning method, the second lateral acceleration is obtained based on the lateral acceleration value collected by a lateral acceleration sensor and the temperature of the lateral acceleration sensor, the third lateral acceleration is obtained based on the wheel speed of the vehicle and the road surface adhesion coefficient, and the fourth lateral acceleration is obtained based on the steering angle and wheel speed of the vehicle.

[0051] In addition, an embodiment of the present invention also provides a computer device, including: a processor; and a memory for storing executable instructions of the processor; wherein, the processor is configured to execute the executable instructions to implement the method of the embodiment of the present invention.

[0052] In addition, an embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. The computer program includes executable instructions, and when the executable instructions are executed by a processor, the method of the embodiment of the present invention is implemented.

[0053] The descriptions of the above storage medium, device or equipment embodiments are similar to the descriptions of the above method embodiments, and have beneficial effects similar to those of the method embodiments. For the technical details not disclosed in the storage medium, device or method embodiments of the present application, please refer to the descriptions of the method embodiments of the present application for understanding.

[0054] The above-mentioned processor may be at least one of an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a central processing unit (CPU), a controller, a microcontroller, a microprocessor, etc. It can be understood that the electronic device implementing the above processor function may also be others, and the embodiments of the present application do not make specific limitations.

[0055] The above computer storage medium / memory can be a read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), ferromagnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM), etc.; it can also be various terminals including one or any combination of the above memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc.

[0056] It should be noted that the above description is only an example and not a limitation of the present invention. In other embodiments of the present invention, the method may have more, fewer, or different steps, and the relationships such as the order, inclusion, and functions between the steps may be different from those described and illustrated. For example, usually multiple steps can be combined into a single step, and a single step can also be split into multiple steps. For those of ordinary skill in the art, without creative efforts, the sequence changes of the steps are also within the protection scope of the present invention.

[0057] The technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.), or a processor, or a microcontroller to execute all or part of the steps of the methods described in various embodiments of the present invention.

[0058] Those of ordinary skill in the art can understand that all or part of the steps to implement the above method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments.

[0059] Although the present invention has been disclosed above in preferred embodiments, the present invention is not limited thereto. Any person skilled in the art, without departing from the spirit and scope of the present invention, makes various changes and modifications, which should be included within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the scope defined by the claims.

Claims

1. A method for compensating the lateral acceleration of a vehicle, characterized in that: include: calculating first to fourth lateral accelerations; as well as determining a final lateral acceleration of the vehicle based on the first to fourth lateral accelerations; The first lateral acceleration is obtained based on a self-learning method, the second lateral acceleration is obtained based on a lateral acceleration collected by a lateral acceleration sensor and a temperature of the lateral acceleration sensor, the third lateral acceleration is obtained based on a wheel speed and a road adhesion coefficient of the vehicle, and the fourth lateral acceleration is obtained based on a steering angle and a wheel speed of the vehicle; The step of calculating the fourth lateral acceleration comprises: Obtaining the wheel speed and steering angle of the vehicle; Based on the wheel speed and the steering angle, using the kinematic formula of the vehicle and a Kalman filter, an initial value of the fourth lateral acceleration is estimated; Based on the initial value of the fourth lateral acceleration, using the MPC model, calculating the median value of the fourth lateral acceleration; When the median value of the fourth lateral acceleration and the collected value of the lateral acceleration sensor are within a predetermined threshold range, the median value of the fourth lateral acceleration is output as the fourth lateral acceleration.

2. The method for compensating the lateral acceleration of a vehicle according to claim 1, characterized in that: The method further comprises: Based on the driving state of the vehicle, determining whether to activate the lateral acceleration compensation function; and The steps of calculating the first to fourth lateral accelerations are performed when the lateral acceleration compensation function is activated.

3. The method for compensating the lateral acceleration of a vehicle according to claim 2, characterized in that: The driving state of the vehicle is defined by the road adhesion coefficient, wheel speed, left and right wheel speed difference of the front axle and the steering angle, and when the road adhesion coefficient, wheel speed, left and right wheel speed difference of the front axle and the steering angle are all within their respective preset ranges, the lateral acceleration compensation function is activated.

4. The method for compensating the lateral acceleration of a vehicle according to any one of claims 1 to 3, characterized in that: The step of calculating the first lateral acceleration comprises: When the vehicle enters a predetermined driving condition, collecting multiple values ​​of the lateral acceleration sensor, and taking an average value of the multiple values ​​as a first lateral acceleration; The predetermined driving conditions include: the mileage of the vehicle is greater than a mileage threshold, the speed of the vehicle is greater than a speed threshold, and the vehicle is traveling in a straight line.

5. The method for compensating the lateral acceleration of a vehicle according to any one of claims 1 to 3, characterized in that: The step of calculating the second lateral acceleration comprises: Using a calibration method, predetermine the conversion relationship between the collected value and temperature of the lateral acceleration sensor and the second lateral acceleration; and Based on the conversion relationship, the second lateral acceleration is calculated.

6. The method for compensating the lateral acceleration of a vehicle according to any one of claims 1 to 3, characterized in that: The step of calculating the third lateral acceleration comprises: Obtaining the wheel speed and road adhesion coefficient of the vehicle; and The wheel speed and the road adhesion coefficient are input into a pre-trained neural network model to calculate the third lateral acceleration.

7. A radar for a vehicle, characterized in that: include: A plurality of acceleration compensation modules, respectively used for calculating the first to fourth lateral accelerations; as well as a fusion module, configured to determine a final lateral acceleration of the vehicle based on the first to fourth lateral accelerations; The first lateral acceleration is obtained based on a self-learning method, the second lateral acceleration is obtained based on a lateral acceleration collected by a lateral acceleration sensor and a temperature of the lateral acceleration sensor, the third lateral acceleration is obtained based on a wheel speed and a road adhesion coefficient of the vehicle, and the fourth lateral acceleration is obtained based on a steering angle and a wheel speed of the vehicle; The operation of calculating the fourth lateral acceleration includes: Obtaining the wheel speed and steering angle of the vehicle; Based on the wheel speed and the steering angle, using the kinematic formula of the vehicle and a Kalman filter, an initial value of the fourth lateral acceleration is estimated; Based on the initial value of the fourth lateral acceleration, using the MPC model, calculating the median value of the fourth lateral acceleration; When the median value of the fourth lateral acceleration and the collected value of the lateral acceleration sensor are within a predetermined threshold range, the median value of the fourth lateral acceleration is output as the fourth lateral acceleration.

8. A computer device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; The processor is configured to execute the executable instructions to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, the computer program comprising executable instructions, and when the executable instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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