Modeling method and device of vehicle posture sensor model, vehicle and storage medium

Abstract

This application discloses a modeling method, apparatus, vehicle, and storage medium for a vehicle attitude sensor model. The method includes: solving for the differences in longitudinal acceleration, lateral acceleration, and / or yaw rate between the inertial navigation system and the automotive-grade sensor; obtaining the variation law of the difference between the actual error value and the identification error value as a function of the longitudinal acceleration value of the automotive-grade sensor based on the differences in longitudinal acceleration, lateral acceleration, and / or yaw rate; and calculating the longitudinal acceleration value, lateral acceleration value, and / or yaw rate data considering the steady-state deviation and noise deviation of the automotive-grade sensor based on the variation law, in order to model the vehicle attitude sensor model. This solves the technical problem in related technologies where the accuracy limitations of automotive sensors prevent the obtained data from closely approximating the true values, resulting in low accuracy in the calculation results of the vehicle attitude.

Description

Technical Field

[0001] This application relates to the field of vehicle stability control technology, and in particular to a modeling method, device, vehicle, and storage medium for a vehicle attitude sensor model. Background Technology

[0002] Vehicle attitude information is a crucial parameter in vehicle active safety control systems. It primarily includes longitudinal acceleration, lateral acceleration, and yaw rate. The accuracy of signals from actual vehicle attitude sensors is affected by various factors, such as occupant weight and occupant position within the cabin. Furthermore, cost limitations of automotive sensors result in somewhat insufficient measurement accuracy. Directly applying these measurement signals to vehicle stability control systems would inevitably lead to malfunctions, thus impacting vehicle safety. Therefore, the accuracy of vehicle attitude information has a significant impact on overall vehicle handling stability and driving safety control systems; accurately obtaining vehicle attitude information is fundamental to ensuring the effectiveness of active safety control systems.

[0003] In related technologies, the main methods for accurately acquiring vehicle attitude are: estimation methods based on data fusion. These methods establish different models using signals from different sensors across multiple dimensions to obtain expressions for the same index in different forms. Then, based on the data fusion method, the multi-dimensional data is fused to obtain vehicle attitude information from the comprehensive data.

[0004] However, the accuracy assumptions in related technologies rely on the premise that one of the data in the multidimensional data is closer to the true value. Due to the precision limitations of automotive sensors, the obtained data cannot accurately obtain vehicle attitude information, which needs to be improved. Summary of the Invention

[0005] This application provides a modeling method, device, vehicle, and storage medium for a vehicle attitude sensor model, in order to solve the technical problem in the related art that the accuracy of the vehicle attitude calculation results is low because the accuracy of the vehicle sensors is limited, which makes it impossible to guarantee that the obtained data is close to the real value.

[0006] The first aspect of this application provides a modeling method for a vehicle attitude sensor model, wherein the vehicle attitude sensor includes an automotive-grade sensor and an inertial navigation device. The method includes the following steps: solving for the difference in longitudinal acceleration, the difference in lateral acceleration, and / or the difference in yaw rate between the inertial navigation device and the automotive-grade sensor; obtaining the variation law of the difference between the actual error value and the identification error value as a function of the longitudinal acceleration value, lateral acceleration value, and / or yaw rate value of the automotive-grade sensor based on the difference in longitudinal acceleration, the difference in lateral acceleration, and / or the difference in yaw rate; and calculating the longitudinal acceleration value, lateral acceleration value, and / or yaw rate data considering the steady-state deviation and noise deviation of the automotive-grade sensor based on the variation law, so as to model the vehicle attitude sensor model.

[0007] Optionally, in one embodiment of this application, wherein,

[0008] The formula for calculating the difference in longitudinal acceleration is:

[0009] Δ ax =a·x 3 +b·x 2 +c·x+d,

[0010] Where a, b, c, and d are the parameters to be identified, with the longitudinal acceleration difference as the ordinate and the longitudinal acceleration value of the automotive-grade sensor as the abscissa.

[0011] Furthermore, the formula for calculating the difference in lateral acceleration is:

[0012] Δ ay =a·x 3 +b·x 2 +c·x+d,

[0013] Where a, b, c, and d are the parameters to be identified, with the difference in lateral acceleration as the vertical axis and the lateral acceleration value of the automotive-grade sensor as the horizontal axis.

[0014] Furthermore, the formula for calculating the difference in yaw angular velocity is:

[0015] Δ yaw =a·x 3 +b·x 2 +c·x+d,

[0016] Where a, b, c, and d are the parameters to be identified, with the difference in yaw rate as the vertical axis and the yaw rate of the automotive-grade sensor as the horizontal axis.

[0017] Optionally, in one embodiment of this application, obtaining the variation law of the difference between the actual error value and the identification error value with the longitudinal acceleration value, the lateral acceleration value and / or the yaw rate value of the automotive-grade sensor based on the difference of the longitudinal acceleration, the difference of the lateral acceleration and / or the difference of the yaw rate includes: based on the difference, calculating the root mean square error of the difference between the actual error value and the identification error value under different longitudinal accelerations in segments, and performing data fitting based on the root mean square error to obtain the variation law of the longitudinal acceleration value, the variation law of the lateral acceleration value and / or the variation law of the yaw rate value.

[0018] Optionally, in one embodiment of this application, wherein,

[0019] The fitting formula for the variation law of the longitudinal acceleration value is:

[0020]

[0021] The fitting formula for the variation law of the lateral acceleration value is:

[0022]

[0023] The fitting formula for the variation law of the pendulum angular velocity value is:

[0024]

[0025] Among them, a1, b1, c1, d1, e1, and f1 are the parameters to be identified.

[0026] Optionally, in one embodiment of this application, wherein,

[0027] The formula for calculating the longitudinal acceleration value is:

[0028] a x =a x_test +Δ ax (a x_test )+N(0,σ ax ),

[0029] The formula for calculating the lateral acceleration value is as follows:

[0030] a y =a y_test +Δ ay (a y_test )+N(0,σ ay ),

[0031] The formula for calculating the yaw rate data is as follows:

[0032]

[0033] Among them, a x_test For the longitudinal acceleration information measured by the sensor, a y_test Lateral acceleration information measured by the sensor. N(·) represents the yaw rate measured by the sensor, and N(·) is a standard normally distributed random variable.

[0034] A second aspect of this application provides a modeling apparatus for a vehicle attitude sensor model, wherein the vehicle attitude sensor includes an automotive-grade sensor and an inertial navigation device, and the apparatus includes:

[0035] The first calculation module is used to solve for the difference in longitudinal acceleration, lateral acceleration, and / or yaw rate between the inertial navigation device and the automotive-grade sensor; the second calculation module is used to obtain the variation law of the difference between the actual error value and the identification error value with the longitudinal acceleration value, lateral acceleration value, and / or yaw rate value of the automotive-grade sensor based on the difference in longitudinal acceleration, lateral acceleration value, and / or yaw rate value; and the modeling module is used to calculate the longitudinal acceleration value, lateral acceleration value, and / or yaw rate data considering the steady-state deviation and noise deviation of the automotive-grade sensor based on the variation law, so as to model the vehicle attitude sensor model.

[0036] Optionally, in one embodiment of this application, wherein,

[0037] The formula for calculating the difference in longitudinal acceleration is:

[0038] Δ ax =a·x 3 +b·x 2 +c·x+d,

[0039] Where a, b, c, and d are the parameters to be identified, with the longitudinal acceleration difference as the ordinate and the longitudinal acceleration value of the automotive-grade sensor as the abscissa.

[0040] Furthermore, the formula for calculating the difference in lateral acceleration is:

[0041] Δ ay =a·x 3 +b·x 2 +c·x+d,

[0042] Where a, b, c, and d are the parameters to be identified, with the difference in lateral acceleration as the vertical axis and the lateral acceleration value of the automotive-grade sensor as the horizontal axis.

[0043] Furthermore, the formula for calculating the difference in yaw angular velocity is:

[0044] Δyaw =a·x 3 +b·x 2 +c·x+d,

[0045] Where a, b, c, and d are the parameters to be identified, with the difference in yaw rate as the vertical axis and the yaw rate of the automotive-grade sensor as the horizontal axis.

[0046] Optionally, in one embodiment of this application, the second calculation module includes: a calculation unit, configured to calculate the mean square error of the difference between the actual error value and the identification error value under different longitudinal accelerations in segments based on the difference, and to perform data fitting based on the mean square error to obtain the variation law of the longitudinal acceleration value, the variation law of the lateral acceleration value and / or the variation law of the swing angular velocity value.

[0047] Optionally, in one embodiment of this application, wherein,

[0048] The fitting formula for the variation law of the longitudinal acceleration value is:

[0049]

[0050] The fitting formula for the variation law of the lateral acceleration value is:

[0051]

[0052] The fitting formula for the variation law of the pendulum angular velocity value is:

[0053]

[0054] Among them, a1, b1, c1, d1, e1, and f1 are the parameters to be identified.

[0055] Optionally, in one embodiment of this application, wherein,

[0056] The formula for calculating the longitudinal acceleration value is:

[0057] a x =a x_test +Δ ax (a x_test )+N(0,σ ax ),

[0058] The formula for calculating the lateral acceleration value is as follows:

[0059] a y =a y_test +Δ ay (a y_test )+N(0,σ ay ),

[0060] The formula for calculating the yaw rate data is as follows:

[0061]

[0062] Among them, a x_test For the longitudinal acceleration information measured by the sensor, a y_test Lateral acceleration information measured by the sensor. N(·) represents the yaw rate measured by the sensor, and N(·) is a standard normally distributed random variable.

[0063] A third aspect of this application provides a vehicle, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle attitude sensor model modeling method as described in the above embodiments.

[0064] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described modeling method for a vehicle attitude sensor model.

[0065] This application's embodiments employ an empirical modeling method to solve for the differences in longitudinal acceleration, lateral acceleration, and / or yaw rate between the inertial navigation system and automotive-grade sensors. This allows for the acquisition of the variation between the actual error value and the identification error value as a function of the longitudinal acceleration value of the automotive-grade sensor. Furthermore, it calculates the longitudinal acceleration, lateral acceleration, and / or yaw rate data, considering the steady-state bias and noise bias of the automotive-grade sensor, to establish a vehicle attitude sensor model. This method is simple to operate, effectively reducing data processing workload. It considers the influence of sensor steady-state bias and noise bias, resulting in a high-accuracy model that can provide a parameter basis for vehicle stability control and active safety control systems. Therefore, it solves the technical problem in related technologies where the accuracy limitations of automotive sensors prevent the obtained data from closely approximating the true values, leading to low accuracy in the calculated vehicle attitude.

[0066] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0067] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0068] Figure 1 This is a flowchart of a modeling method for a vehicle attitude sensor model according to an embodiment of this application;

[0069] Figure 2 This is a schematic diagram illustrating the principle of a modeling method for a vehicle attitude sensor model according to an embodiment of this application;

[0070] Figure 3 This is a flowchart of a modeling method for a vehicle attitude sensor model according to an embodiment of this application;

[0071] Figure 4 This is a schematic diagram of a modeling device for a vehicle attitude sensor model provided according to an embodiment of this application;

[0072] Figure 5 This is a structural schematic diagram of a vehicle provided according to an embodiment of this application. Detailed Implementation

[0073] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0074] The following description, with reference to the accompanying drawings, details a modeling method, apparatus, vehicle, and storage medium for a vehicle attitude sensor model according to embodiments of this application. Addressing the technical problem mentioned in the background section where the accuracy limitations of automotive sensors prevent the obtained data from closely approximating the true values, resulting in low accuracy in the calculation of vehicle attitude, this application provides a modeling method for a vehicle attitude sensor model. This method employs an empirical modeling approach to solve for the differences in longitudinal acceleration, lateral acceleration, and / or yaw rate between the inertial navigation system and the automotive-grade sensor. This allows for the acquisition of the variation law between the actual error value and the identification error value as a function of the longitudinal acceleration value of the automotive-grade sensor. Furthermore, it calculates the longitudinal acceleration, lateral acceleration, and / or yaw rate data, considering the steady-state deviation and noise deviation of the automotive-grade sensor, to establish a vehicle attitude sensor model. The method is simple to operate, effectively reduces data processing workload, considers the influence of sensor steady-state deviation and noise deviation, and boasts high model accuracy, providing a parameter basis for vehicle stability control and active safety control systems. This solves the technical problem in related technologies where the accuracy limitations of automotive sensors prevent the obtained data from closely approximating the true values, resulting in low accuracy in the calculation of the vehicle's attitude.

[0075] Specifically, Figure 1 This is a flowchart illustrating a modeling method for a vehicle attitude sensor model provided in an embodiment of this application.

[0076] like Figure 1As shown, the modeling method for the vehicle attitude sensor model includes automotive-grade sensors and inertial navigation equipment. The method includes the following steps:

[0077] In step S101, the differences in longitudinal acceleration, lateral acceleration, and / or yaw rate between the inertial navigation device and the automotive-grade sensor are calculated.

[0078] In actual implementation, the embodiments of this application can be performed before solving for the difference in longitudinal acceleration, the difference in lateral acceleration, and / or the difference in yaw rate between the inertial navigation device and the automotive-grade sensor.

[0079] For example, such as Figure 2 As shown in the embodiments of this application, a high-precision inertial navigation device, an automotive-grade attitude sensor, and corresponding data acquisition equipment can be prepared in advance. The high-precision inertial navigation device and the automotive-grade attitude sensor are arranged according to... Figure 2 The arrangement shown is fixed, with the z-axis of the two sensors coinciding. Figure 2 As shown, translation along the x-axis and y-axis and rotation along the z-axis are applied, while data from a high-precision inertial navigation system and a vehicle attitude sensor are collected simultaneously.

[0080] When a translational motion is applied along the x-axis, the embodiments of this application can collect longitudinal acceleration data from high-precision inertial navigation equipment and automotive-grade sensors;

[0081] When a translational motion is applied along the y-axis, the embodiments of this application can acquire lateral acceleration data from high-precision inertial navigation equipment and automotive-grade sensors;

[0082] When rotation along the z-axis is applied, embodiments of this application can acquire yaw rate data from high-precision inertial navigation equipment and automotive-grade sensors.

[0083] Furthermore, embodiments of this application can, based on the data collected by the aforementioned devices, solve for the differences in longitudinal acceleration, lateral acceleration, and / or yaw rate between the inertial navigation device and the automotive-grade sensor.

[0084] Optionally, in one embodiment of this application, wherein,

[0085] The formula for calculating the difference in longitudinal acceleration is:

[0086] Δ ax =a·x 3 +b·x 2 +c·x+d,

[0087] Where a, b, c, and d are the parameters to be identified, with the longitudinal acceleration difference as the ordinate and the longitudinal acceleration value of the automotive-grade sensor as the abscissa.

[0088] Furthermore, the formula for calculating the difference in lateral acceleration is:

[0089] Δ ay =a·x 3 +b·x 2 +c·x+d,

[0090] Where a, b, c, and d are the parameters to be identified, with the difference in lateral acceleration as the vertical axis and the lateral acceleration value of the automotive-grade sensor as the horizontal axis.

[0091] And, the formula for calculating the difference in yaw angular velocity is:

[0092] Δ yaw =a·x 3 +b·x 2 +c·x+d,

[0093] Where a, b, c, and d are the parameters to be identified, with the difference in yaw rate as the vertical axis and the yaw rate of the automotive-grade sensor as the horizontal axis.

[0094] Specifically, in this embodiment of the application, the difference in longitudinal acceleration between the high-precision inertial navigation device and the automotive-grade sensor can be calculated using the following formula:

[0095] Δ ax =a·x 3 +b·x 2 +c·x+d,

[0096] Where a, b, c, and d are the parameters to be identified. The longitudinal acceleration difference is used as the vertical axis and the longitudinal acceleration value of the automotive-grade sensor is used as the horizontal axis to identify a, b, c, and d.

[0097] The embodiments of this application can calculate the difference in lateral acceleration between high-precision inertial navigation equipment and automotive-grade sensors based on the following formula:

[0098] Δ ay =a·x 3 +b·x 2 +c·x+d,

[0099] Where a, b, c, and d are the parameters to be identified. The lateral acceleration difference is used as the vertical axis and the lateral acceleration value of the automotive-grade sensor is used as the horizontal axis to identify a, b, c, and d.

[0100] The difference between the yaw rate of a high-precision inertial navigation device and an automotive-grade sensor can be calculated using the following formula:

[0101] Δ yaw =a·x 3 +b·x2 +c·x+d,

[0102] Where a, b, c, and d are the parameters to be identified. The difference in yaw rate is used as the vertical axis and the yaw rate of the automotive-grade sensor is used as the horizontal axis to identify a, b, c, and d.

[0103] In step S102, the variation law of the difference between the actual error value and the identification error value with the longitudinal acceleration value, lateral acceleration value and / or yaw rate value of the automotive-grade sensor is obtained based on the difference in longitudinal acceleration, lateral acceleration and / or yaw rate.

[0104] As one possible implementation method, the embodiments of this application can solve the error noise variation law based on the difference in longitudinal acceleration, the difference in lateral acceleration and / or the difference in yaw rate obtained by the above steps. It takes into account the influence of sensor steady-state deviation and noise deviation, and the model has high accuracy, which can provide a parameter basis for vehicle stability control and active safety control system.

[0105] Optionally, in one embodiment of this application, obtaining the variation law of the difference between the actual error value and the identification error value with the longitudinal acceleration value, the lateral acceleration value and / or the yaw rate value of the automotive-grade sensor based on the difference of longitudinal acceleration, the difference of lateral acceleration and / or the difference of yaw rate includes: based on the difference, solving the mean square error of the difference between the actual error value and the identification error value in segments under different longitudinal accelerations, and performing data fitting based on the mean square error to obtain the variation law of longitudinal acceleration value, the variation law of lateral acceleration value and / or the variation law of yaw rate value.

[0106] Specifically, in this application embodiment, the difference between the actual error value and the identified error value can be calculated based on the error value obtained, and the variation law of the longitudinal acceleration value of the automotive-grade sensor can be obtained. The mean square error between the actual error value and the identified error value under different longitudinal accelerations can be calculated piecewise, and the data can be fitted based on the mean square error to obtain the variation law of the longitudinal acceleration value.

[0107] This application embodiment can use the identified error value as a basis to solve the variation law of the difference between the actual error value and the identified error value with the lateral acceleration value of the automotive-grade sensor, solve the mean square error between the actual error value and the identified error value under different lateral accelerations in segments, and perform data fitting based on the mean square error to obtain the variation law of the lateral acceleration value.

[0108] This application embodiment can use the identified error value as a basis to solve the variation law of the difference between the actual error value and the identified error value with the yaw rate value of the automotive-grade sensor, solve the mean square error between the actual error value and the identified error value under different yaw rates in segments, and perform data fitting based on the mean square error to obtain the variation law of the yaw rate value.

[0109] Optionally, in one embodiment of this application, wherein,

[0110] The fitting formula for the variation law of longitudinal acceleration value is:

[0111]

[0112] The fitting formula for the variation of lateral acceleration values ​​is:

[0113]

[0114] The fitting formula for the variation law of the pendulum angular velocity value is:

[0115]

[0116] Among them, a1, b1, c1, d1, e1, and f1 are the parameters to be identified.

[0117] In actual implementation, the embodiments of this application can fit the variation law of the longitudinal acceleration value using the following formula:

[0118]

[0119] Among them, a1, b1, c1, d1, e1, and f1 are the parameters to be identified.

[0120] The variation law of lateral acceleration value can be fitted using the following formula in the embodiments of this application:

[0121]

[0122] Among them, a1, b1, c1, d1, e1, and f1 are the parameters to be identified.

[0123] The variation law of the pendulum angular velocity value can be fitted using the following formula in the embodiments of this application:

[0124]

[0125] Among them, a1, b1, c1, d1, e1, and f1 are the parameters to be identified.

[0126] In step S103, longitudinal acceleration, lateral acceleration and / or yaw rate data are calculated based on the variation law, taking into account the steady-state deviation and noise deviation of the automotive-grade sensor, in order to model the vehicle attitude sensor model.

[0127] As one possible approach, embodiments of this application can calculate the error noise variation law obtained from the above steps, and combine it with longitudinal acceleration values, lateral acceleration values ​​and / or yaw rate data of steady-state deviation and noise deviation of automotive-grade sensors to realize the modeling of vehicle attitude sensor models. The method is simple to operate, can effectively reduce the workload of data processing, considers the influence of sensor steady-state deviation and noise deviation, and has high model accuracy, which can provide a parameter basis for vehicle stability control and active safety control systems.

[0128] Optionally, in one embodiment of this application, wherein,

[0129] The formula for calculating the longitudinal acceleration value is:

[0130] a x =a x_test +Δ ax (a x_test )+N(0,σ ax ),

[0131] The formula for calculating the lateral acceleration value is:

[0132] a y =a y_test +Δ ay (a y_test )+N(0,σ ay ),

[0133] The formula for calculating yaw rate data is:

[0134]

[0135] Among them, a x_test For the longitudinal acceleration information measured by the sensor, a y_test Lateral acceleration information measured by the sensor. N(·) represents the yaw rate measured by the sensor, and N(·) is a standard normally distributed random variable.

[0136] Furthermore, in the embodiments of this application, the accurate longitudinal acceleration value considering the steady-state deviation and noise deviation of automotive-grade sensors can be calculated according to the following formula:

[0137] a x =a x_test +Δ ax (a x_test )+N(0,σax ),

[0138] The embodiments of this application can calculate the accurate lateral acceleration value, taking into account the steady-state deviation and noise deviation of automotive-grade sensors, according to the following formula:

[0139] a y =a y_test +Δ ay (a y_test )+N(0,σ ay ),

[0140] The embodiments of this application can calculate the accurate yaw rate data, taking into account the steady-state deviation and noise deviation of automotive-grade sensors, according to the following formula:

[0141]

[0142] Combination Figure 2 and Figure 3 As shown, the working principle of the modeling method for the vehicle attitude sensor model of this application embodiment is explained in detail with an example.

[0143] like Figure 3 As shown, where, Figure 3 The sequence of steps shown is merely an example; embodiments of this application may include the following steps:

[0144] Step S301: Calculate the accurate longitudinal acceleration value based on steady-state deviation and noise deviation.

[0145] In actual implementation, the embodiments of this application, based on steady-state deviation and noise deviation, calculate the accurate longitudinal acceleration value, which may include the following steps:

[0146] S1: Equipment Installation. In this embodiment, a high-precision inertial navigation device and an automotive-grade attitude sensor can be installed according to… Figure 2 Install as required, set up the corresponding data acquisition equipment, apply translation along the x-axis, and simultaneously acquire longitudinal acceleration data from the high-precision inertial navigation equipment and automotive-grade sensors.

[0147] S2: Calculate the difference in longitudinal acceleration between the high-precision inertial navigation device and the automotive-grade sensor. This embodiment of the application can be based on the formula Δ... ax =a·x 3 +b·x 2 The solution is obtained by solving for +c·x+d, where a, b, c, and d are the parameters to be identified. The longitudinal acceleration difference is used as the vertical axis and the longitudinal acceleration value of the automotive-grade sensor is used as the horizontal axis to identify a, b, c, and d.

[0148] S3: Solve for the variation law of error noise. This embodiment of the application can, based on the identified error value, solve for the variation law of the difference between the actual error value and the identified error value with the longitudinal acceleration value of the automotive-grade sensor. It can also solve for the root mean square error between the actual error value and the identified error value under different longitudinal accelerations, according to the formula... Perform data fitting, where a1, b1, c1, d1, e1, and f1 are the parameters to be identified.

[0149] S4: The embodiments of this application can be based on formula a x =a x_test +Δ ax (a x_test )+N(0,σ ax The accurate longitudinal acceleration value was calculated, taking into account the steady-state deviation and noise deviation of the automotive-grade sensor.

[0150] Step S302: Calculate the accurate lateral acceleration value based on steady-state deviation and noise deviation.

[0151] In actual implementation, the embodiments of this application, based on steady-state deviation and noise deviation, calculate the accurate longitudinal acceleration value, which may include the following steps:

[0152] S1: Equipment Installation. In this embodiment, a high-precision inertial navigation device and an automotive-grade attitude sensor can be installed according to… Figure 2 Install as required, set up the corresponding data acquisition equipment, apply translation along the y-axis, and simultaneously acquire lateral acceleration data from the high-precision inertial navigation equipment and automotive-grade sensors.

[0153] S2: Calculate the difference in lateral acceleration between the high-precision inertial navigation device and the automotive-grade sensor. This embodiment of the application can be based on the formula Δ... ay =a·x 3 +b·x 2 The solution is obtained by solving for +c·x+d, where a, b, c, and d are the parameters to be identified. The lateral acceleration difference is used as the vertical axis and the lateral acceleration value of the automotive-grade sensor is used as the horizontal axis to identify a, b, c, and d.

[0154] S3: Solve for the variation law of error noise. This embodiment of the application can, based on the identified error value, solve for the variation law of the difference between the actual error value and the identified error value with the lateral acceleration value of the automotive-grade sensor, and solve for the root mean square error between the actual error value and the identified error value under different lateral accelerations piecewise, according to the formula... Perform data fitting, where a1, b1, c1, d1, e1, and f1 are the parameters to be identified.

[0155] S4: The embodiments of this application can be based on formula a y =a y_test +Δ ay (a y_test )+N(0,σ ay The accurate lateral acceleration value was calculated, taking into account the steady-state deviation and noise deviation of the automotive-grade sensor.

[0156] Step S303: Calculate the accurate yaw rate value based on steady-state deviation and noise deviation.

[0157] In actual implementation, the embodiments of this application, based on steady-state deviation and noise deviation, calculate the accurate yaw rate value, which may include the following steps:

[0158] S1: Equipment Installation. In this embodiment, a high-precision inertial navigation device and an automotive-grade attitude sensor can be installed according to… Figure 2 Install as required, set up the corresponding data acquisition equipment, apply translation along the z-axis, and simultaneously acquire yaw rate data from the high-precision inertial navigation equipment and automotive-grade sensors.

[0159] S2: Calculate the difference in yaw rate between the high-precision inertial navigation device and the automotive-grade sensor. This embodiment of the application can be based on the formula Δ... yaw =a·x 3 +b·x 2 The solution is obtained by solving for +c·x+d, where a, b, c, and d are the parameters to be identified. The yaw rate difference is used as the vertical axis and the yaw rate of the automotive-grade sensor is used as the horizontal axis to identify a, b, c, and d.

[0160] S3: Solve for the variation law of error noise. This embodiment of the application can, based on the identified error value, solve for the variation law of the difference between the actual error value and the identified error value with the yaw rate value of the automotive-grade sensor, and solve for the root mean square error between the actual error value and the identified error value under different yaw rates, according to the formula... Perform data fitting, where a1, b1, c1, d1, e1, and f1 are the parameters to be identified.

[0161] S4: The embodiments of this application can be based on the formula The accurate yaw rate value was calculated, taking into account the steady-state deviation and noise deviation of the automotive-grade sensor.

[0162] The vehicle attitude sensor modeling method proposed in this application employs an empirical modeling approach to solve for the differences in longitudinal acceleration, lateral acceleration, and / or yaw rate between the inertial navigation system and the automotive-grade sensor. This allows for the acquisition of the variation law between the actual error value and the identification error value as a function of the longitudinal acceleration value of the automotive-grade sensor. Furthermore, it calculates the longitudinal acceleration, lateral acceleration, and / or yaw rate data, taking into account the steady-state deviation and noise deviation of the automotive-grade sensor, to establish the vehicle attitude sensor model. This method is simple to operate, effectively reducing data processing workload. It considers the influence of sensor steady-state deviation and noise deviation, resulting in high model accuracy and providing a parameter basis for vehicle stability control and active safety control systems. Therefore, it solves the technical problem in related technologies where the accuracy limitations of automotive sensors prevent the obtained data from closely approximating the true values, leading to low accuracy in the calculation results of the vehicle attitude.

[0163] Next, the modeling apparatus for a vehicle attitude sensor model according to an embodiment of this application is described with reference to the accompanying drawings.

[0164] Figure 4 This is a block diagram of a modeling device for a vehicle attitude sensor model according to an embodiment of this application.

[0165] like Figure 4 As shown, the modeling device 10 for the vehicle attitude sensor model includes: wherein the vehicle attitude sensor includes automotive-grade sensors and inertial navigation equipment, and the device includes: a first calculation module 100, a second calculation module 200 and a modeling module 300.

[0166] Specifically, the first calculation module 100 is used to solve for the difference in longitudinal acceleration, the difference in lateral acceleration, and / or the difference in yaw rate between the inertial navigation device and the automotive-grade sensor.

[0167] The second calculation module 200 is used to obtain the variation law of the difference between the actual error value and the identification error value with the longitudinal acceleration value, lateral acceleration value and / or yaw rate value of the automotive-grade sensor, based on the difference in longitudinal acceleration, lateral acceleration and / or yaw rate.

[0168] Modeling module 300 is used to calculate longitudinal acceleration, lateral acceleration and / or yaw rate data based on the variation law, taking into account the steady-state deviation and noise deviation of automotive-grade sensors, in order to model the vehicle attitude sensor model.

[0169] Optionally, in one embodiment of this application, wherein,

[0170] The formula for calculating the difference in longitudinal acceleration is:

[0171] Δ ax =a·x3 +b·x 2 +c·x+d,

[0172] Where a, b, c, and d are the parameters to be identified, with the longitudinal acceleration difference as the ordinate and the longitudinal acceleration value of the automotive-grade sensor as the abscissa.

[0173] Furthermore, the formula for calculating the difference in lateral acceleration is:

[0174] Δ ay =a·x 3 +b·x 2 +c·x+d,

[0175] Where a, b, c, and d are the parameters to be identified, with the difference in lateral acceleration as the vertical axis and the lateral acceleration value of the automotive-grade sensor as the horizontal axis.

[0176] And, the formula for calculating the difference in yaw angular velocity is:

[0177] Δ yaw =a·x 3 +b·x 2 +c·x+d,

[0178] Where a, b, c, and d are the parameters to be identified, with the difference in yaw rate as the vertical axis and the yaw rate of the automotive-grade sensor as the horizontal axis.

[0179] Optionally, in one embodiment of this application, the second computing module 200 includes a computing unit.

[0180] The calculation unit is used to calculate the mean square error between the actual error value and the identification error value under different longitudinal accelerations in segments based on the difference, and to perform data fitting based on the mean square error to obtain the variation law of longitudinal acceleration value, the variation law of lateral acceleration value and / or the variation law of swing angular velocity value.

[0181] Optionally, in one embodiment of this application, wherein,

[0182] The fitting formula for the variation law of longitudinal acceleration value is:

[0183]

[0184] The fitting formula for the variation of lateral acceleration values ​​is:

[0185]

[0186] The fitting formula for the variation law of the pendulum angular velocity value is:

[0187]

[0188] Among them, a1, b1, c1, d1, e1, and f1 are the parameters to be identified.

[0189] Optionally, in one embodiment of this application, wherein,

[0190] The formula for calculating the longitudinal acceleration value is:

[0191] a x =a x_test +Δ ax (a x_test )+N(0,σ ax ),

[0192] The formula for calculating the lateral acceleration value is:

[0193] a y =a y_test +Δ ay (a y_test )+N(0,σ ay ),

[0194] The formula for calculating yaw rate data is:

[0195]

[0196] Among them, a x_test For the longitudinal acceleration information measured by the sensor, a y_test Lateral acceleration information measured by the sensor. N(·) represents the yaw rate measured by the sensor, and N(·) is a standard normally distributed random variable.

[0197] It should be noted that the explanation of the above-described modeling method for the vehicle attitude sensor model also applies to the modeling device for the vehicle attitude sensor model in this embodiment, and will not be repeated here.

[0198] The modeling apparatus for the vehicle attitude sensor model proposed in this application can employ an empirical modeling method to solve for the differences in longitudinal acceleration, lateral acceleration, and / or yaw rate between the inertial navigation device and the automotive-grade sensor. This allows for the acquisition of the variation law between the actual error value and the identification error value as a function of the longitudinal acceleration value of the automotive-grade sensor. Furthermore, it calculates the longitudinal acceleration, lateral acceleration, and / or yaw rate data considering the steady-state deviation and noise deviation of the automotive-grade sensor, thus establishing a vehicle attitude sensor model. The method is simple to operate, effectively reducing data processing workload. It considers the influence of sensor steady-state deviation and noise deviation, resulting in high model accuracy and providing a parameter basis for vehicle stability control and active safety control systems. Therefore, it solves the technical problem in related technologies where the accuracy limitations of automotive sensors prevent the obtained data from closely approximating the true values, leading to low accuracy in the calculation results of the vehicle attitude.

[0199] Figure 5 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include:

[0200] The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0201] When the processor 502 executes the program, it implements the modeling method for the vehicle attitude sensor model provided in the above embodiments.

[0202] Furthermore, the vehicle also includes:

[0203] Communication interface 503 is used for communication between memory 501 and processor 502.

[0204] The memory 501 is used to store computer programs that can run on the processor 502.

[0205] The memory 501 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0206] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0207] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0208] Processor 502 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0209] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described modeling method for the vehicle attitude sensor model.

[0210] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0211] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0212] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0213] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0214] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0215] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0216] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0217] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A modeling method for a vehicle attitude sensor model, characterized in that, The vehicle attitude sensor includes automotive-grade sensors and inertial navigation equipment, wherein the method includes the following steps: Solve for the differences in longitudinal acceleration, lateral acceleration, and / or yaw rate between the inertial navigation system and the automotive-grade sensor; The variation of the difference between the actual error value and the identification error value with the longitudinal acceleration value, lateral acceleration value, and / or yaw rate value of the automotive-grade sensor is obtained based on the difference in longitudinal acceleration, lateral acceleration, and / or yaw rate. Based on the aforementioned variation law, longitudinal acceleration, lateral acceleration, and / or yaw rate data of automotive-grade sensors are calculated, taking into account steady-state deviation and noise deviation, in order to model the vehicle attitude sensor model. The step of obtaining the variation law of the difference between the actual error value and the identification error value with the longitudinal acceleration value, the lateral acceleration value and / or the yaw rate value of the automotive-grade sensor based on the difference of the longitudinal acceleration, the difference of the lateral acceleration and / or the difference of the yaw rate includes: based on the difference, calculating the root mean square error of the difference between the actual error value and the identification error value under different longitudinal accelerations in segments, and performing data fitting based on the root mean square error to obtain the variation law of the longitudinal acceleration value, the variation law of the lateral acceleration value and / or the variation law of the yaw rate value.

2. The method according to claim 1, characterized in that, in, The formula for calculating the difference in longitudinal acceleration is: ， in, a , b , c , d The parameters to be identified are plotted with the longitudinal acceleration difference as the ordinate and the longitudinal acceleration value of the automotive-grade sensor as the abscissa. Furthermore, the formula for calculating the difference in lateral acceleration is: ， in, a , b , c , d The parameters to be identified are plotted with the difference in lateral acceleration as the vertical axis and the lateral acceleration value from the automotive-grade sensor as the horizontal axis. Furthermore, the formula for calculating the difference in yaw angular velocity is: ， in, a , b , c , d The parameters to be identified are plotted with the difference in yaw rate as the vertical axis and the yaw rate of the automotive-grade sensor as the horizontal axis.

3. The method according to claim 2, characterized in that, in, The fitting formula for the variation law of the longitudinal acceleration value is: ， The fitting formula for the variation law of the lateral acceleration value is: ， The fitting formula for the variation law of the yaw rate value is: ， in, These are the parameters to be identified.

4. The method according to claim 3, characterized in that, in, The formula for calculating the longitudinal acceleration value is: ， The formula for calculating the lateral acceleration value is as follows: ， The formula for calculating the yaw rate data is as follows: ， in, For longitudinal acceleration information measured by the sensor, Lateral acceleration information measured by the sensor. The yaw rate information measured by the sensor. It is a standard normally distributed random variable.

5. A modeling device for a vehicle attitude sensor model, characterized in that, The vehicle attitude sensor includes automotive-grade sensors and inertial navigation equipment, wherein the device includes: The first calculation module is used to solve for the difference in longitudinal acceleration, the difference in lateral acceleration, and / or the difference in yaw rate between the inertial navigation device and the automotive-grade sensor. The second calculation module is used to obtain the variation law of the difference between the actual error value and the identification error value with the longitudinal acceleration value, lateral acceleration value and / or yaw rate value of the automotive-grade sensor, based on the difference in longitudinal acceleration, the difference in lateral acceleration and / or the difference in yaw rate; and The modeling module is used to calculate the longitudinal acceleration value, lateral acceleration value and / or yaw rate data of the automotive-grade sensor based on the changing law, taking into account the steady-state deviation and noise deviation of the sensor, so as to model the vehicle attitude sensor model. The second calculation module includes a calculation unit, which is used to calculate the root mean square error of the difference between the actual error value and the identification error value under different longitudinal accelerations based on the difference, and to perform data fitting based on the root mean square error to obtain the variation law of the longitudinal acceleration value, the variation law of the lateral acceleration value and / or the variation law of the yaw rate value.

6. The apparatus according to claim 5, characterized in that, in, The formula for calculating the difference in longitudinal acceleration is: ， in, a , b , c , d The parameters to be identified are plotted with the longitudinal acceleration difference as the ordinate and the longitudinal acceleration value of the automotive-grade sensor as the abscissa. Furthermore, the formula for calculating the difference in lateral acceleration is: ， in, a , b , c , d The parameters to be identified are plotted with the difference in lateral acceleration as the vertical axis and the lateral acceleration value from the automotive-grade sensor as the horizontal axis. Furthermore, the formula for calculating the difference in yaw angular velocity is: ， in, a , b , c , d The parameters to be identified are plotted with the difference in yaw rate as the vertical axis and the yaw rate of the automotive-grade sensor as the horizontal axis.

7. The apparatus according to claim 6, characterized in that, in, The fitting formula for the variation law of the longitudinal acceleration value is: ， The fitting formula for the variation law of the lateral acceleration value is: ， The fitting formula for the variation law of the yaw rate value is: ， in, These are the parameters to be identified.

8. The apparatus according to claim 7, characterized in that, in, The formula for calculating the longitudinal acceleration value is: ， The formula for calculating the lateral acceleration value is as follows: ， The formula for calculating the yaw rate data is as follows: ， in, For longitudinal acceleration information measured by the sensor, Lateral acceleration information measured by the sensor. The yaw rate information measured by the sensor. It is a standard normally distributed random variable.

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