Vehicle control methods and vehicles

Abstract

This application discloses a vehicle control method and a vehicle, relating to the field of vehicle chassis technology. The method includes: acquiring dynamic parameters including auxiliary parameters and measured vehicle posture corresponding to a first posture parameter; generating a second posture parameter corresponding to the vehicle posture during operation based on the dynamic parameters; obtaining a confidence coefficient characterizing the reliability of the first posture parameter based on the first and second posture parameters; and fusing the first and second posture parameters based on the confidence coefficient and auxiliary parameters to obtain a target posture parameter. By fusing the first and second posture parameters, the accuracy of the obtained target posture parameter is improved, thereby enhancing the effectiveness of vehicle control based on the target posture parameter.

Description

Technical Field

[0001] This application relates to the field of vehicle chassis technology, and more particularly to a vehicle control method and a vehicle. Background Technology

[0002] When a vehicle turns, axle load transfer occurs, causing a change in the vehicle's lateral tilt. This lateral tilt also affects the occupants, impacting their riding experience. To address this, vehicles equipped with fully active suspension compensate for the angular changes caused by axle load transfer by applying active suspension forces, thus achieving zero lateral tilt. The active suspension roll control force needs to be calculated based on the vehicle's lateral acceleration. If the lateral acceleration is greater than the true value, the vehicle will exhibit a negative roll angle; if it is less, a positive roll angle. If there is a phase difference between the lateral acceleration and the true value, the vehicle will be unable to suppress body yaw and lateral drift, causing the control deviation to continuously increase, the control output to exceed limits, and ultimately leading to loss of vehicle control. Therefore, the accuracy and timeliness of the lateral acceleration determine the effectiveness of roll control. Summary of the Invention

[0003] In view of the above problems, this application provides a vehicle control method and vehicle that overcomes or at least partially solves the above problems, and the technical solution is as follows: A vehicle control method includes: acquiring dynamic parameters during vehicle operation; the dynamic parameters including auxiliary parameters and a first attitude parameter corresponding to the vehicle's posture; the first attitude parameter being acquired through data acquisition; generating a second attitude parameter corresponding to the vehicle's posture during operation based on the dynamic parameters; acquiring a confidence coefficient based on the first attitude parameter and the second attitude parameter; the confidence coefficient being used to characterize the reliability of the first attitude parameter; and fusing the first attitude parameter and the second attitude parameter based on the confidence coefficient and the auxiliary parameters to obtain a target attitude parameter for vehicle control based on the target attitude parameter.

[0004] In practical applications, the roll control force of active suspension needs to be calculated based on the lateral acceleration of the vehicle body. Lateral acceleration can be obtained by measuring it with an IMU (Inertial Measurement Unit). However, the lateral acceleration obtained by IMU measurement has large noise and the amplitude fluctuation is much greater than the true value itself. In addition, lateral acceleration can also be calculated based on the dynamic parameters of the vehicle during operation. However, in the process of calculating lateral acceleration based on the dynamic parameters of the vehicle during operation, since it is based on the test calibration road surface, the calculated lateral acceleration is not accurate enough when the actual road surface of the vehicle is driving is significantly different from the test calibration road surface. Based on this, the vehicle control method provided in this application fuses the first attitude parameters obtained from IMU measurements and the second attitude parameters calculated from the dynamic parameters during vehicle operation to obtain the target attitude parameters for vehicle control. This avoids the large noise and amplitude fluctuation of the lateral acceleration obtained from a single IMU measurement, and also avoids the inability of the lateral acceleration calculated solely from the dynamic parameters to adapt to the current road surface, thereby improving the accuracy of the obtained target attitude parameters. In the specific execution process, after acquiring auxiliary parameters, first attitude parameters obtained from IMU measurements, and second attitude parameters generated from dynamic parameters, a confidence coefficient characterizing the reliability of the first attitude parameters can be obtained first based on the first and second attitude parameters. Then, based on the confidence coefficient and auxiliary parameters, the first and second attitude parameters are fused to obtain the target attitude parameters. In this way, the confidence coefficient is determined by combining the measured first attitude parameters and the generated second attitude parameters, thereby improving the accuracy and effectiveness of the determined confidence coefficient. Furthermore, by introducing the confidence coefficient characterizing the reliability of the first attitude parameters during the fusion process, the effectiveness of the fusion process is improved, and the matching degree between the obtained target attitude parameters and the actual operating scenario is enhanced.

[0005] Optionally, generating the second attitude parameters corresponding to the vehicle's attitude during operation based on the dynamic parameters includes: obtaining the axle side stiffness based on the vehicle speed included in the auxiliary parameters; generating a second lateral acceleration based on the Kalman algorithm, the axle side stiffness, the auxiliary parameters, and the first attitude parameters; generating a second yaw rate based on the axle side stiffness, the auxiliary parameters, and the first attitude parameters; and determining the second lateral acceleration and the second yaw rate as the second attitude parameters.

[0006] In this optional implementation, during the process of generating the second attitude parameters corresponding to the vehicle's attitude during operation based on dynamic parameters, the axle side stiffness corresponding to the vehicle speed is first obtained. Then, on the one hand, a second lateral acceleration is generated based on the Kalman algorithm, axle side stiffness, auxiliary parameters, and the first attitude parameters. On the other hand, a second yaw rate is generated based on the axle side stiffness, auxiliary parameters, and the first attitude parameters. The second lateral acceleration and the second yaw rate are then determined as the second attitude parameters. In this way, by first obtaining the axle side stiffness from the vehicle speed, the nonlinear behavior of the vehicle is simulated to a great extent, improving the accuracy of the obtained axle side stiffness. Secondly, by generating the second lateral acceleration and the second yaw rate as the two attitude parameters corresponding to the vehicle attitude, the comprehensiveness and effectiveness of the obtained second attitude parameters are improved.

[0007] Optionally, generating the second lateral acceleration based on the Kalman algorithm, the axial side stiffness, the auxiliary parameters, and the first attitude parameters includes: generating a centroid sideslip angle based on the Kalman algorithm, the axial side stiffness, the auxiliary parameters, and the first attitude parameters; and generating the second lateral acceleration based on the centroid sideslip angle, the vehicle speed, and the first yaw rate in the first attitude parameters.

[0008] In this optional implementation, during the process of generating the second lateral acceleration based on the Kalman algorithm, axial side stiffness, auxiliary parameters, and first attitude parameters, the center of gravity sideslip angle is first generated based on the Kalman algorithm, axial side stiffness, auxiliary parameters, and first attitude parameters. Then, the second lateral acceleration is generated based on the center of gravity sideslip angle, vehicle speed, and the first yaw rate in the first attitude parameters. In this way, the center of gravity sideslip angle is calculated based on the Kalman algorithm, thereby improving the accuracy of the calculated center of gravity sideslip angle.

[0009] Optionally, generating the centroid sideslip angle based on the Kalman algorithm, the axial side stiffness, the auxiliary parameters, and the first attitude parameters includes: generating a first state transition matrix and a first input matrix based on the axial side stiffness and the auxiliary parameters, and generating a first input vector based on the auxiliary parameters; generating a first predicted state vector based on the first state transition matrix, the first input matrix, and the first input vector; correcting the first predicted state vector based on the first attitude parameters and the auxiliary parameters to obtain a first target state vector; and obtaining the centroid sideslip angle based on the first target state vector.

[0010] In this optional implementation, during the generation of the centroid sideslip angle, a first state transition matrix and a first input matrix are first generated based on the axial sideslip stiffness and auxiliary parameters, and a first input vector is generated based on the auxiliary parameters. Then, a first predicted state vector is generated based on the first state transition matrix, the first input matrix, and the first input vector. The first predicted state vector is then corrected based on the first attitude parameters and auxiliary parameters to obtain a first target state vector, thereby improving the effectiveness of the obtained target state vector. Finally, based on the effective first target state vector, a more accurate and effective centroid sideslip angle is obtained.

[0011] Optionally, the step of correcting the first predicted state vector based on the first attitude parameters and the auxiliary parameters to obtain the first target state vector includes: obtaining a first auxiliary matrix, and obtaining a first observation parameter based on the first attitude parameters; the first observation parameter includes a first observation and a first observation matrix; generating a first intermediate vector based on the first auxiliary matrix, the first observation matrix, and a first noise covariance matrix included in the auxiliary parameters; and correcting the first predicted state vector based on the first intermediate vector and the first observation parameters to obtain the first target state vector.

[0012] In this optional implementation, during the process of correcting the first predicted state vector based on the first attitude parameters and auxiliary parameters to obtain the first target state vector, a first auxiliary matrix is ​​obtained, and a first observation parameter is obtained based on the first attitude parameters. Then, a first intermediate vector is generated based on the first auxiliary matrix, the first observation matrix in the first observation parameters, and the first noise covariance matrix contained in the auxiliary parameters. The first predicted state vector is then corrected based on the first intermediate vector and the first observation parameters to obtain the first target state vector. In this way, the first attitude parameters are introduced to correct the first predicted state vector, thereby improving the matching degree between the obtained first target state vector and the vehicle attitude.

[0013] Optionally, obtaining the confidence coefficient based on the first attitude parameters and the second attitude parameters includes: calculating the acceleration deviation based on the first lateral acceleration and the second lateral acceleration, and calculating the angular velocity deviation based on the first yaw rate and the second yaw rate; determining a coefficient acquisition strategy based on the acceleration deviation and the angular velocity deviation; and obtaining the confidence coefficient based on the coefficient acquisition strategy.

[0014] In practical applications, when the angular velocity deviation is small, the confidence level of the first noise covariance is considered low. At this time, the second attitude parameter is more reliable, and the noise of the second attitude parameter is smaller, which can greatly reduce the noise influence in the IMU measurement process. Based on this, in this optional embodiment, in the process of obtaining the confidence coefficient based on the first attitude parameter and the second attitude parameter, on the one hand, the acceleration deviation is calculated based on the first lateral acceleration and the second lateral acceleration; on the other hand, the angular velocity deviation is calculated based on the first yaw angular velocity and the second yaw angular velocity. Then, the coefficient acquisition strategy is determined by combining the angular velocity deviation and the acceleration deviation, and the confidence coefficient is obtained according to the coefficient acquisition strategy. In this way, the coefficient acquisition strategy is determined by combining the angular velocity deviation and the acceleration deviation, avoiding the coincidental phenomenon of angular velocity error coupling under conditions such as low-adhesion double-line shift, improving the effectiveness of the determined coefficient acquisition strategy, and thus improving the effectiveness of the confidence coefficient obtained according to the coefficient acquisition strategy.

[0015] Optionally, determining the coefficient acquisition strategy based on the acceleration deviation and the angular velocity deviation includes: acquiring a preset angular velocity deviation threshold and a preset acceleration deviation threshold; detecting whether the acceleration deviation is greater than the preset acceleration deviation threshold and whether the angular velocity deviation is greater than the preset angular velocity deviation threshold; if yes, determining the coefficient acquisition strategy as a first acquisition strategy; if no, determining the coefficient acquisition strategy as a second acquisition strategy.

[0016] In this optional implementation, during the process of determining the coefficient acquisition strategy based on acceleration deviation and angular velocity deviation, a preset angular velocity deviation threshold and a preset acceleration deviation threshold are introduced. If the acceleration deviation is greater than the preset acceleration deviation threshold and the angular velocity deviation is greater than the preset angular velocity deviation threshold, the coefficient acquisition strategy is determined to be the first acquisition strategy; otherwise, the coefficient acquisition strategy is determined to be the second acquisition strategy. In this way, angular velocity and acceleration are double-judged to avoid introducing abnormal second attitude parameters.

[0017] Optionally, obtaining the confidence coefficient based on the coefficient acquisition strategy includes: if the coefficient acquisition strategy is a first acquisition strategy, obtaining the smallest coefficient in the preset coefficient list as the confidence coefficient; if the coefficient acquisition strategy is a second acquisition strategy, querying the corresponding coefficient from the preset coefficient list according to the angular velocity deviation as the confidence coefficient.

[0018] In practical applications, the smaller the angular velocity deviation, the less accurate the second attitude parameter is, and the more trust should be placed on the first attitude parameter. Correspondingly, the confidence coefficient used to characterize the reliability of the first attitude parameter should be larger. Based on this, in this optional implementation, the angular velocity deviation in the preset coefficient list is negatively correlated with the corresponding coefficient. In the process of obtaining the execution degree coefficient based on the coefficient acquisition strategy, the coefficient can be queried in the preset coefficient list based on the coefficient acquisition strategy to obtain the confidence coefficient. In the specific execution process, if the coefficient acquisition strategy is the first acquisition strategy, the smallest coefficient in the preset coefficient list is obtained as the confidence coefficient. If the coefficient acquisition strategy is the second acquisition strategy, the corresponding coefficient is queried from the preset coefficient list according to the angular velocity deviation as the confidence coefficient. Thus, if the acceleration deviation is greater than the preset acceleration deviation threshold and the angular velocity deviation is greater than the preset angular velocity deviation threshold, the smallest coefficient in the preset coefficient list is obtained as the confidence coefficient. Otherwise, the coefficient corresponding to the angular velocity deviation in the preset coefficient list is obtained as the confidence coefficient. In this way, the accuracy of the determined confidence coefficient is improved, thereby improving the effectiveness of the confidence coefficient in representing the credibility of the first attitude parameter.

[0019] Optionally, the step of fusing the first attitude parameters and the second attitude parameters based on the confidence coefficient and the auxiliary parameters to obtain the target attitude parameters includes: determining weighting parameters based on the confidence coefficient and the auxiliary parameters; and fusing the first attitude parameters and the second attitude parameters based on the weighted Kalman algorithm and the weighting parameters to obtain the target attitude parameters.

[0020] In this optional implementation, during the process of fusing the first attitude parameters and the second attitude parameters based on the confidence coefficient and auxiliary parameters to obtain the target attitude parameters, weighting parameters can be determined first based on the confidence coefficient and auxiliary parameters. Then, the first attitude parameters and the second attitude parameters can be fused based on the weighted Kalman algorithm and the weighting parameters to obtain the target attitude parameters. In this way, the introduction of weighting parameters and the weighted Kalman algorithm to fuse the first attitude parameters and the second attitude parameters improves the effectiveness of the fusion process, thereby improving the accuracy of the obtained target attitude parameters, avoiding jumps in the target attitude parameters, and improving robustness.

[0021] Optionally, determining the weighting parameter based on the confidence coefficient and the auxiliary parameter includes: obtaining the second noise covariance matrix in the auxiliary parameter; and calculating the product of the second noise covariance matrix and the confidence coefficient as the weighting parameter.

[0022] In this alternative implementation, during the process of determining the weighting parameters based on the confidence coefficient and auxiliary parameters, the second noise covariance matrix in the auxiliary parameters can be obtained first, and then the product of the second noise covariance matrix and the confidence coefficient can be calculated as the weighting parameter, thereby improving the effectiveness of the calculated weighting parameters.

[0023] Optionally, the step of fusing the first attitude parameters and the second attitude parameters based on the weighted Kalman algorithm and the weighting parameters to obtain the target attitude parameters includes: generating a state transition matrix; determining input data and a second observation parameter based on the second attitude parameters and the first attitude parameters; generating initial attitude parameters based on the state transition matrix and the input data; and correcting the initial attitude parameters based on the second observation parameter and the weighting parameters to obtain the target attitude parameters.

[0024] This optional implementation, in the process of fusing first and second attitude parameters based on a weighted Kalman algorithm and weighted parameters to obtain target attitude parameters, firstly generates a state transition matrix and determines input data and second observation parameters based on the second and first attitude parameters. Then, based on the state transition matrix and input data, initial attitude parameters are generated. Next, the initial attitude parameters are corrected based on the second observation parameters and weighted parameters to obtain the target attitude parameters. Thus, the weighted parameters are used as weighted parameters for the weighted Kalman algorithm, and the first and second attitude parameters are fused based on the weighted Kalman algorithm. This achieves weighted fusion of the first and second attitude parameters, improving the effectiveness and accuracy of the obtained target attitude parameters, and thus improving the effectiveness of vehicle control based on the target attitude parameters.

[0025] Optionally, the vehicle control based on the target attitude parameters includes: generating a target roll moment based on the target attitude parameters; and performing vehicle control based on the target roll moment.

[0026] In this optional implementation, during the process of vehicle control based on target attitude parameters, a target roll moment is generated based on the target attitude parameters, and then vehicle control is performed based on the target roll moment, thereby achieving effective vehicle control.

[0027] A vehicle control device, the device comprising: The parameter acquisition module is used to acquire dynamic parameters during vehicle operation; the dynamic parameters include auxiliary parameters and a first attitude parameter corresponding to the vehicle attitude; the first attitude parameter is obtained by measurement. The parameter generation module is used to generate second attitude parameters corresponding to the vehicle attitude during the vehicle operation based on the dynamic parameters. The coefficient acquisition module is used to acquire a confidence coefficient based on the first attitude parameter and the second attitude parameter; the confidence coefficient is used to characterize the reliability of the first attitude parameter. The fusion processing module is used to perform fusion processing on the first attitude parameters and the second attitude parameters based on the confidence coefficient and the auxiliary parameters to obtain target attitude parameters, so as to perform vehicle control based on the target attitude parameters.

[0028] A vehicle that includes a control device as described above.

[0029] A vehicle includes a memory for storing a computer program; and a processor for executing the computer program to implement the steps of any of the above-described vehicle control methods.

[0030] An electronic device includes: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of any of the above-described vehicle control methods.

[0031] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-described vehicle control methods.

[0032] A computer program product includes a computer program that, when executed by a processor, implements the steps of any of the above-described vehicle control methods.

[0033] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0034] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a schematic diagram of the implementation environment of a vehicle control method provided in an embodiment of this application; Figure 2 This is an illustrative flowchart of a vehicle control method provided in an embodiment of this application. Figure 1 ; Figure 3 This is a diagram showing the relationship between vehicle speed and axle side stiffness provided in an embodiment of this application; Figure 4 This is a graph showing the correspondence between angular velocity deviation and confidence coefficient provided in an embodiment of this application; Figure 5 This is an illustrative flowchart of a vehicle control method provided in an embodiment of this application. Figure 2 ; Figure 6 This is a schematic structural diagram of a vehicle control device provided in an embodiment of this application; Figure 7 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application. Detailed Implementation

[0035] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0036] In practical applications, fully active suspension vehicles can compensate for the attitude angle changes caused by axle load transfer during vehicle steering by applying active suspension forces. The active suspension force, i.e., the active suspension roll control force, can be calculated based on the lateral acceleration of the vehicle body. Specifically, lateral acceleration can be obtained through IMU measurement or calculated from the vehicle's dynamic parameters. However, the acquisition noise of lateral acceleration obtained by IMU measurement is relatively large and the amplitude fluctuation is far greater than the amplitude fluctuation of the true value. The lateral acceleration calculated from the vehicle's dynamic parameters will be incorrect on special road surfaces and cannot be flexibly matched to different road surfaces.

[0037] To address this, the vehicle control method provided in this embodiment first obtains dynamic parameters, including auxiliary parameters and first attitude parameters corresponding to the vehicle's posture obtained through measurement, during vehicle operation. Then, based on the dynamic parameters, it generates second attitude parameters corresponding to the vehicle's posture during operation, thereby obtaining attitude parameters in two dimensions: the first attitude parameters obtained through measurement and the second attitude parameters obtained through prediction. Next, it fuses the first and second attitude parameters to obtain target attitude parameters, which are then used for vehicle control. This method, by fusing the first and second attitude parameters to obtain the target attitude parameters, avoids the large noise and amplitude fluctuations exceeding the true value in lateral acceleration measurements obtained from a single IMU, and also avoids the inability to flexibly match different road surfaces when calculated solely from dynamic parameters, thus improving the accuracy of the obtained target attitude parameters. Specifically, in the process of fusing the first attitude parameters and the second attitude parameters, a confidence coefficient can be obtained first based on the first attitude parameters and the second attitude parameters to characterize the credibility of the first attitude parameters. Then, the first attitude parameters and the second attitude parameters are fused based on the confidence coefficient and auxiliary parameters. In this way, by introducing the confidence coefficient, the effectiveness of parameter fusion is further improved and the accuracy of the obtained target attitude parameters is improved.

[0038] like Figure 1 As shown, Figure 1 This is a schematic diagram of the implementation environment of a vehicle control method provided in an embodiment of this application. The implementation environment includes: a vehicle controller 101 and a suspension control system 102. The vehicle controller 101 can be a terminal installed on the vehicle, used to acquire dynamic parameters including auxiliary parameters and first attitude parameters corresponding to the vehicle attitude obtained by measurement, and based on the dynamic parameters, generate second attitude parameters corresponding to the vehicle attitude during vehicle operation, and then obtain a confidence coefficient to characterize the reliability of the first attitude parameters according to the first attitude parameters and the second attitude parameters; finally, based on the confidence coefficient and auxiliary parameters, perform fusion processing on the first attitude parameters and the second attitude parameters to obtain the target attitude parameters. The suspension control system 102 can be used to control the vehicle's suspension system based on target attitude parameters in order to achieve vehicle control.

[0039] Furthermore, the implementation environment of this vehicle control method may also include only: a suspension control system 102, which is used to acquire dynamic parameters including auxiliary parameters and first attitude parameters corresponding to the vehicle attitude obtained by measurement, and based on the dynamic parameters, generate second attitude parameters corresponding to the vehicle attitude during vehicle operation, and then obtain a confidence coefficient to characterize the reliability of the first attitude parameters according to the first attitude parameters and the second attitude parameters; finally, based on the confidence coefficient and auxiliary parameters, perform fusion processing on the first attitude parameters and the second attitude parameters to obtain target attitude parameters and control the vehicle's suspension system based on the target attitude parameters.

[0040] like Figure 2 As shown, Figure 2 This is an illustrative flowchart of a vehicle control method provided in an embodiment of this application. Figure 1 The method includes: Step 201: Obtain the dynamic parameters of the vehicle during operation.

[0041] In practice, dynamic parameters are acquired during vehicle operation. In this embodiment, the dynamic parameters include parameters describing the specific structure and / or motion state of the vehicle itself. Optionally, the dynamic parameters include vehicle structural dynamic parameters and / or vehicle motion dynamic parameters.

[0042] Among them, vehicle structural dynamic parameters include dynamic parameters that are fixed after the vehicle leaves the factory and are determined by the body / chassis results; for example, vehicle structural dynamic parameters include at least one of the following: vehicle weight, distance of center of gravity from the front axle, distance of center of gravity from the rear axle, and moment of inertia about the Z-axis.

[0043] Vehicle motion dynamics parameters include dynamic parameters that change in real time during vehicle operation and reflect the dynamic motion state of the vehicle; for example, vehicle motion dynamics parameters include at least one of the following: first yaw rate, first lateral acceleration, vehicle speed, front axle angle, and rear axle angle.

[0044] In addition, the dynamic parameters may also include pre-configured parameters for subsequent processing, such as a second noise covariance matrix, a first noise covariance matrix, and / or a software runtime. The second and first noise covariance matrices can be 2×2 diagonal covariance matrices or 1×1 scalars, and the software runtime can be 0.01 seconds. It should be noted that the second noise covariance matrix and the software runtime can be configured according to the actual scenario, and this embodiment does not impose any limitations on them. The second noise covariance matrix may include a first sub-noise covariance matrix and a second sub-noise covariance matrix, wherein the first sub-noise covariance matrix can be a 2×2 diagonal covariance matrix, and the second sub-noise covariance matrix can be a 1×1 scalar; this embodiment does not impose any limitations on them. The first noise covariance matrix can be the observation noise covariance matrix.

[0045] It should be noted that among the above dynamic parameters, the first yaw rate and the first lateral acceleration can be defined as the first attitude parameters corresponding to the vehicle attitude; the first attitude parameters can be obtained through IMU measurement; the remaining parameters can be auxiliary parameters. That is, the first attitude parameters corresponding to the vehicle attitude include the first yaw rate and / or the first lateral acceleration, and the parameters other than the first attitude parameters in the dynamic parameters are auxiliary parameters. Optionally, the dynamic parameters include auxiliary parameters and the first attitude parameters corresponding to the vehicle attitude. In this embodiment, the first attitude parameters can be obtained through IMU measurement. The vehicle attitude in this embodiment includes the vehicle's current attitude, such as the steering attitude.

[0046] Step 202: Based on the dynamic parameters, generate the second attitude parameters corresponding to the vehicle's attitude during operation.

[0047] In practice, after obtaining the dynamic parameters during vehicle operation, a second attitude parameter corresponding to the vehicle's attitude during operation is generated based on the dynamic parameters. In this way, both the first attitude parameter obtained by IMU measurement and the second attitude parameter calculated based on the dynamic parameters are obtained.

[0048] When the first attitude parameters include the first lateral acceleration and / or the first yaw rate, the second attitude parameters may also include the second lateral acceleration and / or the second yaw rate.

[0049] In the specific execution process, during the generation of the second attitude parameters corresponding to the vehicle's attitude during operation based on the dynamic parameters, the second lateral acceleration and the second yaw rate during vehicle operation can be generated based on the dynamic parameters. The following provides a detailed explanation of the process of generating the second attitude parameters corresponding to the vehicle's attitude during operation based on the dynamic parameters.

[0050] (1) Obtain the corresponding axle side stiffness based on the vehicle speed included in the auxiliary parameters.

[0051] In the specific execution process, the corresponding axle side stiffness is first obtained based on the vehicle speed included in the auxiliary parameters. In this embodiment, the axle side stiffness includes the front axle side stiffness and the rear axle side stiffness.

[0052] In practice, different vehicle speeds result in different steering characteristics, and the axle side stiffness during steering has a non-linear relationship with vehicle speed. Therefore, a pre-configured correspondence between vehicle speed and axle side stiffness can be established. After obtaining the dynamic parameters, the axle side stiffness corresponding to the vehicle speed can be obtained from this correspondence. Specifically, the pre-configured correspondence between vehicle speed and axle side stiffness can be obtained through user experiments; this embodiment does not limit this specific configuration.

[0053] For example, the relationship between vehicle speed and axle side stiffness is as follows: Figure 3 As shown, after obtaining the vehicle speed v, it can be based on, as follows: Figure 3 The diagram showing the relationship between vehicle speed and axle lateral stiffness reveals the front axle lateral stiffness Kf and rear axle lateral stiffness Kr corresponding to vehicle speed v.

[0054] (2) Based on the Kalman algorithm, axial side stiffness, auxiliary parameters and first attitude parameters, the second lateral acceleration is generated.

[0055] Specifically, in the process of generating the second lateral acceleration based on the Kalman algorithm, axial side stiffness, auxiliary parameters, and first attitude parameters, the centroid sideslip angle can be generated first based on the Kalman algorithm, axial side stiffness, auxiliary parameters, and first attitude parameters. Then, the second lateral acceleration can be generated based on the centroid sideslip angle, vehicle speed, and the first yaw rate in the first attitude parameters.

[0056] In the process of generating the centroid sideslip angle based on the Kalman algorithm, axial side stiffness, auxiliary parameters, and first attitude parameters, the process can first generate a first state transition matrix and a first input matrix based on the axial side stiffness and auxiliary parameters, and generate a first input vector based on the auxiliary parameters; generate a first predicted state vector based on the first state transition matrix, the first input matrix, and the first input vector; correct the first predicted state vector based on the first attitude parameters and auxiliary parameters to obtain a first target state vector; and obtain the centroid sideslip angle based on the first target state vector.

[0057] Specifically, in the process of correcting the first predicted state vector based on the first attitude parameters and auxiliary parameters to obtain the first target state vector, a first auxiliary matrix can be obtained first, and first observation parameters can be obtained based on the first attitude parameters; a first intermediate vector can be generated based on the first auxiliary matrix, the first observation matrix, and the first noise covariance matrix contained in the auxiliary parameters; and the first predicted state vector can be corrected based on the first intermediate vector and the first observation parameters to obtain the first target state vector. The first observation parameters may include the first observation quantity and the first observation matrix.

[0058] Specifically, in the process of generating the centroid sideslip angle, a first state transition matrix and a first input matrix are generated based on the axial sideslip stiffness and auxiliary parameters. A first input vector is generated based on the auxiliary parameters. A first observation matrix and a first observation are generated, and a first state vector is defined. Then, based on the first input matrix, the first input vector, and the first state transition matrix, a first predicted state vector is generated. A first auxiliary matrix is ​​generated based on the first state transition matrix, the first target auxiliary vector from the previous time step, and the first sub-noise covariance matrix in the auxiliary parameters. A first intermediate vector is generated based on the first auxiliary matrix, the first observation matrix, and the first noise covariance matrix contained in the auxiliary parameters. The first predicted state vector is corrected based on the first intermediate vector, the first observation, and the first observation matrix to obtain the first target state vector. The centroid sideslip angle is obtained from the first target state vector. In addition, the first auxiliary matrix can also be corrected based on the first intermediate vector and the first observation matrix to obtain the first target auxiliary matrix.

[0059] Specifically, in the process of generating the state transition matrix based on the axial side stiffness and auxiliary parameters, the state transition matrix can be generated based on a two-degree-of-freedom model. Specifically, the axial side stiffness and auxiliary parameters can be input into the two-degree-of-freedom model to generate the matrix and obtain the state transition matrix. Specifically, the state transition matrix can be a 2×2 matrix, which can be composed of a first transition parameter, a second transition parameter, a third transition parameter, and a fourth transition parameter. In the process of calculating the first transfer parameter, the negative value of the ratio of the sum of the front axle lateral stiffness and the rear axle lateral stiffness to the product of the vehicle mass and the vehicle speed can be used as the first transfer parameter. For example, if the front axle lateral stiffness is Kf, the rear axle lateral stiffness is Kr, the vehicle mass is m, and the vehicle speed is V, then the first transfer parameter is:

[0060] In the process of calculating the second transfer parameter, the difference between the product of the front axle lateral stiffness and the distance from the center of gravity to the front axle and the product of the rear axle lateral stiffness and the distance from the center of gravity to the rear axle can be calculated first. Then, the product of the vehicle mass and the square of the vehicle speed can be calculated. Finally, the negative value of the difference between the ratio of the above difference and the above product and the preset value can be calculated as the second transfer parameter. For example, if the distance from the center of mass to the front axis is 'a' and the distance from the center of mass to the rear axis is 'b', and the preset value is 1, then the second transfer parameter is:

[0061] In the process of calculating the third transfer parameter, the difference between the product of the front axle lateral stiffness and the distance from the center of mass to the front axle and the product of the rear axle lateral stiffness and the distance from the center of mass to the rear axle can be calculated, and then the negative value of the ratio of the above difference to the moment of inertia about the Z-axis can be used as the third transfer parameter. For example, if the moment of inertia about the Z-axis is Izz, then the third transfer parameter is:

[0062] In the process of calculating the fourth transfer parameter, the difference between the product of the front axle lateral stiffness and the square of the distance from the center of gravity to the front axle and the product of the rear axle lateral stiffness and the square of the distance from the center of gravity to the rear axle can be calculated. Then, the product of the moment of inertia about the Z-axis and the vehicle speed can be calculated. Finally, the negative value of the ratio of the above difference to the product can be used as the fourth transfer parameter. For example, the fourth transfer parameter is:

[0063] After obtaining the above four parameters, a first state transition matrix can be generated based on the first transition parameter, the second transition parameter, the third transition parameter, the fourth transition parameter, and the auxiliary parameter. Specifically, an initial first state transition matrix can be generated first based on the first transition parameter, the second transition parameter, the third transition parameter, and the fourth transition parameter. Then, the product of the software running cycle in the auxiliary parameter and the initial first state transition matrix is ​​calculated as the first state transition matrix.

[0064] For example, if the software execution cycle is dt, then the first state transition matrix can be:

[0065] In the process of generating the first input matrix based on the axial side stiffness and auxiliary parameters, the first input parameters and the second input parameters can be calculated, and then the first input matrix can be generated based on the first input parameters, the second input parameters and the auxiliary parameters.

[0066] In calculating the first input parameter, the ratio of the front axle lateral stiffness to the product of the vehicle mass and the vehicle speed can be used as the first input parameter. For example, the first input parameter is:

[0067] In calculating the second input parameter, the ratio of the rear axle lateral stiffness to the product of the vehicle mass and vehicle speed can be used as the second input parameter. For example, the second input parameter is:

[0068] Based on the calculation of the first and second input parameters, the product of the software running cycle and the matrix composed of the first and second input parameters can be used as the first input matrix.

[0069] For example, the first input matrix is:

[0070] In the specific execution process, during the generation of the first input vector based on the auxiliary parameters, the first input vector can be generated based on the front axle angle and the rear axle angle in the auxiliary parameters. Specifically, the transpose of the matrix formed by the front axle angle and the rear axle angle can be used as the first input vector. For example, if the front axle angle is αf and the rear axle angle is αr, the first input vector can be... .

[0071] Specifically, in the process of generating the first observation matrix and the first observation measure, a matrix composed of 0 and preset values ​​can be generated as the first observation matrix, and the first yaw rate can be used as the first observation measure; for example, the first observation matrix First observation ;in, This is the first yaw rate.

[0072] In addition, a first state vector is defined. , where β is the centroid sideslip angle and r is the fusion yaw rate.

[0073] After obtaining the first state transition matrix, the first input matrix, the first input vector, the first observation matrix, and the first observation, and defining the first state vector, the first predicted state vector is first generated based on the first input matrix, the first input vector, and the first state transition matrix. Then, the first predicted state vector is corrected according to the first observation matrix and the first observation to obtain the first target state vector. The centroid offset angle is obtained from the first target state vector.

[0074] In the process of generating the first predicted state vector based on the first input matrix, the first input vector, and the first state transition matrix, the product of the sum of the first state transition matrix and the identity matrix and the first predicted state vector of the previous time step (historical first predicted state vector) can be calculated, and the sum of the products of the first input matrix and the first input vector can be used as the first predicted state vector of the current time step.

[0075] For example, the first predicted state vector at time n:

[0076] Where I is the identity matrix.

[0077] In the specific execution process, in addition to generating the first predicted state vector, a first auxiliary matrix can also be generated to assist in the correction of the first predicted state vector. Specifically, the first auxiliary matrix can be calculated as the sum of the product of the first state transition matrix, the first auxiliary matrix of the previous time step (historical first auxiliary matrix), and the transpose of the first state transition matrix, and the first sub-noise covariance matrix.

[0078] For example, the first sub-noise covariance matrix is ​​Q1, and the first auxiliary matrix at the current time n is... .

[0079] Based on obtaining the first predicted state vector and the first auxiliary matrix, and in the process of correcting the first predicted state vector according to the first observation matrix and the first observation to obtain the first target state vector, the first intermediate vector can be generated first based on the first auxiliary matrix, the first observation matrix and the first noise covariance matrix contained in the auxiliary parameters. Then, the first predicted state vector can be corrected based on the first intermediate vector, the first observation and the first observation matrix to obtain the first target state vector. In the specific process of generating the first intermediate vector based on the first auxiliary matrix, the first observation matrix, and the first noise covariance matrix contained in the auxiliary parameters, the product of the first auxiliary matrix and the transpose of the first observation matrix can be calculated, as well as the inverse operation of the product of the first observation matrix, the first auxiliary matrix, and the transpose of the first observation matrix and the sum of the first noise covariance matrix can be calculated to obtain the inverse operation result. Then, the product of the product of the first auxiliary matrix and the transpose of the first observation matrix and the inverse operation result is calculated as the first intermediate vector.

[0080] For example, the first noise covariance matrix is ​​R, and the first intermediate vector is:

[0081] After generating the first intermediate vector, in the process of correcting the first predicted state vector based on the first intermediate vector, the first observation, and the first observation matrix to obtain the first target state vector, the product of the difference between the first observation and the product of the first observation matrix and the first predicted state vector and the first intermediate vector can be calculated first, and then the sum of the first predicted state vector and the product can be calculated as the first target state vector.

[0082] For example, the first target state vector is:

[0083] In practical implementation, since the first state vector is defined as described above... After obtaining the first target state vector, the first value in the first target state vector can be read as the centroid sideslip angle.

[0084] It should be noted that in this embodiment, the first auxiliary matrix can be the prior covariance matrix at the current time; and the first intermediate vector can be the Kalman gain at the current time.

[0085] It should also be noted that, based on obtaining the first target state vector, since the generation of the first target state vector at the next moment requires the use of the first auxiliary matrix at the previous moment, the first auxiliary matrix can also be corrected based on the first intermediate vector and the first observation matrix to obtain the first target auxiliary matrix, so that the first target auxiliary matrix can be used as the historical first auxiliary matrix to participate in the generation of the first target state vector at the next moment.

[0086] Specifically, in the process of correcting the first auxiliary matrix based on the first intermediate vector and the first observation matrix to obtain the first target auxiliary matrix, the product of the difference between the identity matrix and the product of the first intermediate vector and the first observation matrix and the first auxiliary matrix can be calculated as the first target auxiliary matrix.

[0087] For example, the first target auxiliary matrix is:

[0088] The above describes in detail the process of generating the centroid sideslip angle based on the Kalman algorithm, axial side stiffness, auxiliary parameters, and first attitude parameters. Specifically, a first state transition matrix, a first input matrix, a first input vector, a first observation matrix, and a first observation can be generated first based on the axial side stiffness, auxiliary parameters, and first attitude parameters, and a first state vector can be defined. Then, a first target state vector can be generated based on the first state transition matrix, the first input matrix, the first input vector, the first observation matrix, and the first observation based on the Kalman algorithm, and the centroid sideslip angle can be obtained from the first target state vector.

[0089] That is: in the process of generating the centroid sideslip angle, the following is defined and generated: First state vector:

[0090] First input vector:

[0091] First state transition matrix:

[0092] First input matrix:

[0093] First observation matrix:

[0094] First observation:

[0095] In the forecasting phase: First predicted state vector:

[0096] First auxiliary matrix:

[0097] During the revision phase: First intermediate vector:

[0098] First target state vector:

[0099] First objective auxiliary matrix:

[0100] It should be noted that the first target state vector corresponding to each time step can be generated.

[0101] In practice, after obtaining the sideslip angle of the center of gravity, the second lateral acceleration can be generated based on the sideslip angle of the center of gravity, the vehicle speed, and the first yaw rate in the first attitude parameters.

[0102] Specifically, in the process of generating the second lateral acceleration based on the sideslip angle of the center of gravity, the vehicle speed, and the first yaw rate in the first attitude parameters, the sum of the differentials of the first yaw rate and the sideslip angle of the center of gravity can be calculated first, and then the product of the vehicle speed and the sum of the differentials of the first yaw rate and the sideslip angle of the center of gravity can be calculated as the second lateral acceleration.

[0103] For example, the second lateral acceleration is:

[0104] in, This represents the differential of β.

[0105] (3) Based on the axial side stiffness, auxiliary parameters and first attitude parameters, the second yaw rate is generated.

[0106] In the specific execution process, during the generation of the second yaw rate based on the lateral stiffness, auxiliary parameters, and first attitude parameters, the difference between the product of the front axle lateral stiffness and the distance from the center of gravity to the front axle and the product of the rear axle lateral stiffness and the distance from the center of gravity to the rear axle can be calculated. Then, the product of the difference and the lateral angle of the center of gravity is calculated. The ratio of this product to the moment of inertia about the Z-axis is then used as the first value. Next, the difference between the product of the front axle lateral stiffness and the square of the distance from the center of gravity to the front axle and the product of the rear axle lateral stiffness and the square of the distance from the center of gravity to the rear axle is calculated. Then, the product of this difference and the second yaw rate at the previous moment is calculated. The ratio of this product to the product of the moment of inertia about the Z-axis and the vehicle speed is then used as the second value. The sum of the negative values ​​of the first and second values ​​is used as the third value. Finally, the sum of the product of the software running cycle and the third value and the second yaw rate at the previous moment is used as the second yaw rate at the current moment.

[0107] For example, the second yaw angular velocity is:

[0108] (4) The second lateral acceleration and the second yaw rate are determined as the second attitude parameters.

[0109] In practice, after generating the second lateral acceleration and the second yaw rate, the second lateral acceleration and the second yaw rate are determined as the second attitude parameters.

[0110] Step 203: Obtain the confidence coefficient based on the first attitude parameters and the second attitude parameters.

[0111] In specific implementation, after obtaining the first attitude parameter and the second attitude parameter as described above, a confidence coefficient is obtained from both the first attitude parameter and the second attitude parameter to characterize the reliability of the first attitude parameter. Optionally, the confidence coefficient is used to characterize the reliability of the first attitude parameter.

[0112] In the specific execution process, when obtaining the confidence coefficient based on the first and second attitude parameters, the acceleration deviation and angular velocity deviation can be calculated based on the first and second attitude parameters, and then the confidence coefficient can be obtained based on the acceleration deviation and angular velocity deviation. In this way, the confidence coefficient is obtained from two dimensions: acceleration deviation and angular velocity deviation, which improves the effectiveness and accuracy of the obtained confidence coefficient and avoids introducing second attitude parameters with large errors.

[0113] The following is a detailed description of an optional implementation method for obtaining the confidence coefficient based on the first attitude parameters and the second attitude parameters provided in this embodiment.

[0114] Step 203-1: Calculate the acceleration deviation based on the first lateral acceleration and the second lateral acceleration, and calculate the angular velocity deviation based on the first yaw rate and the second yaw rate.

[0115] In specific implementation, during the process of calculating the acceleration deviation based on the first lateral acceleration and the second lateral acceleration, the absolute value of the difference between the first lateral acceleration and the second lateral acceleration can be calculated as the acceleration deviation; during the process of calculating the angular velocity deviation based on the first yaw rate and the second yaw rate, the absolute value of the difference between the first yaw rate and the second yaw rate can be calculated as the angular velocity deviation.

[0116] For example, the first yaw rate is r, and the second yaw rate is... Then the angular velocity deviation can be calculated as follows: .

[0117] For example, the first lateral acceleration is The second lateral acceleration is Then the acceleration deviation can be calculated as: .

[0118] It should be noted that the above is the calculation process for acceleration deviation and angular velocity deviation at a single moment. In the actual execution process, the above method can be used to calculate acceleration deviation and angular velocity deviation for each moment. The calculation method is similar to the above calculation process. Please refer to the above relevant content for calculation. This embodiment does not limit it here.

[0119] Step 203-2: Determine the coefficient acquisition strategy based on the acceleration deviation and angular velocity deviation.

[0120] Specifically, in the process of determining the coefficient acquisition strategy based on acceleration deviation and angular velocity deviation, the first acquisition strategy can be determined if both the acceleration deviation and angular velocity deviation are greater than the preset acceleration deviation threshold and the angular velocity deviation are greater than the preset angular velocity deviation threshold; otherwise, the second acquisition strategy can be determined.

[0121] In one optional implementation of this embodiment, in the process of determining the coefficient acquisition strategy based on the acceleration deviation and angular velocity deviation, a preset angular velocity deviation threshold and a preset acceleration deviation threshold are first obtained. Then, it is detected whether the acceleration deviation is greater than the preset acceleration deviation threshold and whether the angular velocity deviation is greater than the preset angular velocity deviation threshold. If so, the coefficient acquisition strategy is determined to be the first acquisition strategy; if not, the coefficient acquisition strategy is determined to be the second acquisition strategy.

[0122] For example, if the preset acceleration deviation threshold is 0.4 meters per second squared and the preset angular velocity deviation threshold is 0.5 radians per second, the coefficient acquisition strategy is determined to be the first acquisition strategy if the acceleration deviation is greater than 0.4 and the angular velocity deviation is greater than 0.5; otherwise, the coefficient acquisition strategy is determined to be the second acquisition strategy.

[0123] Step 203-3: Obtain the confidence coefficient based on the coefficient acquisition strategy.

[0124] In practical implementation, after determining the coefficient acquisition strategy, the confidence coefficient is obtained based on the strategy. During execution, in the process of obtaining the confidence coefficient based on the coefficient acquisition strategy, the coefficient can be queried in a preset coefficient list according to the strategy to obtain the confidence coefficient. In this embodiment, the smaller the confidence coefficient, the lower the noise uncertainty of the first attitude parameter in the fusion process is judged; similarly, the larger the confidence coefficient, the higher the noise uncertainty of the first attitude parameter in the fusion process is judged. It should be noted that the confidence coefficient in this embodiment is inversely proportional to the reliability of the first attitude parameter.

[0125] The preset coefficient list in this embodiment includes a pre-configured list storing the correspondence between angular velocity deviation and coefficients. In practical applications, a larger angular velocity deviation indicates a higher reliability of the second lateral acceleration. This suggests the vehicle may be on a low-friction surface or under other abnormal conditions, in which case the first attitude parameter should be trusted more. Conversely, a smaller angular velocity deviation indicates a lower reliability of the first attitude parameter and a higher corresponding confidence coefficient. For example, the correspondence between angular velocity deviation and confidence coefficient is as follows: Figure 4 As shown, it can be done through... Figure 4 The graph showing the correspondence between angular velocity deviation and confidence coefficient (preset coefficient list) is used to obtain the confidence coefficient.

[0126] In the specific execution process, if the coefficient acquisition strategy is the first acquisition strategy, the smallest coefficient in the preset coefficient list can be obtained as the confidence coefficient. If the coefficient acquisition strategy is the second acquisition strategy, the corresponding coefficient can be queried from the preset coefficient list based on the angular velocity deviation as the confidence coefficient.

[0127] In other words, during the process of obtaining confidence coefficients based on acceleration deviation and angular velocity deviation, if both the acceleration deviation and angular velocity deviation are greater than a preset acceleration deviation threshold and a preset angular velocity deviation threshold, the smallest coefficient in the preset coefficient list is used as the confidence coefficient; otherwise, the coefficient corresponding to the angular velocity deviation in the preset coefficient list is used as the confidence coefficient. This avoids coefficient jumps under low-adhesion double-tracking conditions and prevents the introduction of abnormal second attitude parameters.

[0128] For example, if the acceleration deviation is greater than a preset acceleration deviation threshold and the angular velocity deviation is greater than a preset angular velocity deviation threshold, the smallest coefficient in the preset coefficient list is read as the confidence coefficient λ; otherwise, the coefficient corresponding to the angular velocity deviation in the preset coefficient list is read as the confidence coefficient λ.

[0129] Step 204: Based on the confidence coefficient and auxiliary parameters, the first attitude parameters and the second attitude parameters are fused to obtain the target attitude parameters, so as to perform vehicle control based on the target attitude parameters.

[0130] In practice, based on the confidence coefficient used to characterize the credibility of the first attitude parameter, the first attitude parameter and the second attitude parameter can be fused based on the confidence coefficient and auxiliary parameters to obtain the target attitude parameter.

[0131] In practice, a weighted Kalman algorithm can be used to fuse the first and second attitude parameters to obtain the target attitude parameters. Specifically, in the process of fusing the first and second attitude parameters using the weighted Kalman algorithm to obtain the target attitude parameters, the weighting parameters can first be determined based on the confidence coefficient and auxiliary parameters. Then, the first and second attitude parameters are fused based on the Kalman algorithm and the weighting parameters to obtain the target attitude parameters.

[0132] Specifically, in determining the weighting parameters based on the confidence coefficient and auxiliary parameters, the second sub-noise covariance matrix from the auxiliary parameters can be obtained, and the product of the second sub-noise covariance matrix and the confidence coefficient can be calculated as the weighting parameter. For example, the weighting parameter F = λ × Q². It should be noted that, when configuring a second noise covariance matrix as described above, in determining the weighting parameters, the second noise covariance matrix from the auxiliary parameters can be obtained, and the product of the second noise covariance matrix and the confidence coefficient can be calculated as the weighting parameter.

[0133] After determining the weighting parameters, the first attitude parameters and the second attitude parameters are fused based on the weighted Kalman algorithm and the weighting parameters to obtain the target attitude parameters. In one optional implementation of this embodiment, during the fusion of the first and second attitude parameters based on the weighted Kalman algorithm and weighting parameters to obtain the target attitude parameters, a state transition matrix is ​​first generated. Input data and second observation parameters are then determined based on the second and first attitude parameters. Initial attitude parameters are generated based on the state transition matrix and the input data. The initial attitude parameters are then corrected based on the second observation parameters and the weighting parameters to obtain the target attitude parameters. In this embodiment, the target attitude parameter can be the target lateral acceleration. The state transition matrix here can be the second state transition matrix. The input data can include a second input vector and a second input matrix, and the second observation parameters can include a second observation and a second observation matrix.

[0134] In the specific execution process, the second input matrix, the second input vector, the second observation matrix, the second observation and the second state transition matrix can be generated first, and the second state vector can be defined. Then, based on the second state transition matrix, the second input matrix and the second input vector, the initial lateral acceleration is generated. Then, the initial lateral acceleration is corrected based on the second observation matrix, the second observation and the weighting parameters to obtain the target lateral acceleration.

[0135] Specifically, the software runtime can be used as the second input matrix. In addition, the software runtime can also be used as the second observation matrix, the second lateral acceleration can be used as the second input vector, 0 can be used as the second state transition matrix, the first lateral acceleration can be used as the second observation, and the target lateral acceleration can be defined as the second state vector.

[0136] For example, definition and generation: Second state vector:

[0137] Second input vector:

[0138] Second state transition matrix:

[0139] Second input matrix:

[0140] Second observation matrix:

[0141] Second observation:

[0142] In the process of generating the initial lateral acceleration based on the second state transition matrix, the second input matrix, and the second input vector, the product of the sum of the second state transition matrix and the identity matrix and the target lateral acceleration (historical lateral acceleration) at the previous moment, and the product of the second input matrix and the second input vector, can be calculated as the initial lateral acceleration at the current moment.

[0143] For example, the initial lateral acceleration at time n is:

[0144] In the specific execution process, in addition to generating the initial lateral acceleration, a second auxiliary matrix can also be generated to assist in correcting the initial lateral acceleration. Specifically, the second state transition matrix can be calculated by multiplying the second auxiliary matrix of the previous time step (historical second auxiliary matrix) and the transpose of the second state transition matrix, and then summing the product with the second sub-noise covariance matrix.

[0145] For example, the second sub-noise covariance matrix is ​​Q2, and the second auxiliary matrix at the current time n is:

[0146] Based on the initial lateral acceleration and the second auxiliary matrix, and after correcting the initial attitude parameters according to the second observation parameters and weighted parameters to obtain the target attitude parameters, a second intermediate vector can be generated first based on the second auxiliary matrix, the second observation vector and the weighted parameters. Then, the initial lateral acceleration can be corrected based on the second intermediate vector, the second observation, and the second observation matrix to obtain the target lateral acceleration.

[0147] In the specific process of generating the second intermediate vector based on the second auxiliary matrix, the second observation vector, and the weighting parameters, the product of the second auxiliary matrix and the transpose of the second observation matrix can be calculated, as well as the inverse operation of the product of the second observation matrix, the second auxiliary matrix, and the transpose of the second observation matrix and the sum of the weighting parameters can be calculated to obtain the inverse operation result. Then, the product of the product of the second auxiliary matrix and the transpose of the second observation matrix and the inverse operation result can be calculated as the second intermediate vector.

[0148] For example, the second intermediate vector is:

[0149] After generating the second intermediate vector, in the process of correcting the initial lateral acceleration based on the second intermediate vector, the second observation, and the second observation matrix to obtain the target lateral acceleration, we can first calculate the product of the difference between the product of the second observation and the product of the second observation matrix and the initial lateral acceleration and the second intermediate vector, and then calculate the sum of the initial lateral acceleration and the product as the target lateral acceleration.

[0150] For example, the target's lateral acceleration is:

[0151] It should be noted that, in this embodiment, the second auxiliary matrix can be the prior covariance matrix at the current time; and the second intermediate vector can be the Kalman gain at the current time.

[0152] It should also be noted that, based on the obtained target lateral acceleration, since the generation of the target lateral acceleration at the next moment requires the use of the second auxiliary matrix from the previous moment, the second auxiliary matrix can also be corrected based on the second intermediate vector and the second observation matrix to obtain the second target auxiliary matrix. This second target auxiliary matrix can then be used as the historical second auxiliary matrix to participate in the generation of the target lateral acceleration at the next moment.

[0153] Specifically, in the process of correcting the second auxiliary matrix based on the second intermediate vector and the second observation matrix to obtain the second target auxiliary matrix, the product of the difference between the identity matrix and the product of the second intermediate vector and the second observation matrix and the second auxiliary matrix can be calculated as the second target auxiliary matrix.

[0154] For example, the auxiliary matrix for the second objective is:

[0155] That is, in the prediction stage: Initial lateral acceleration:

[0156] Second auxiliary matrix:

[0157] During the revision phase: Second intermediate vector:

[0158] Target lateral acceleration:

[0159] Second objective auxiliary matrix:

[0160] The process of obtaining the target attitude parameters has been explained in detail above. After obtaining the target attitude parameters, vehicle control can be performed based on them, thereby achieving zero roll control of the vehicle.

[0161] Specifically, in the process of vehicle control based on target attitude parameters, a target roll moment can first be generated based on the target attitude parameters, and then vehicle control can be performed based on the target roll moment. Specifically, in the process of vehicle control based on target lateral acceleration, the target roll moment required by the vehicle can be calculated based on the target lateral acceleration, and then the target roll moment can be distributed to the actuators of the four active suspensions of the vehicle. The actuators are input with actual suspension forces, thereby achieving vehicle control.

[0162] In the process of calculating the target roll moment required for the vehicle based on the target lateral acceleration, the product of the target lateral acceleration, the vehicle mass, and the height of the vehicle's center of gravity can be calculated as the target roll moment.

[0163] In the process of distributing the target roll moment to the actuators of the four active suspensions of the vehicle, the target roll moment can first be divided into the front axle target roll moment and the rear axle target roll moment with the axle side stiffness as the weight. Then, the front axle target roll moment and the rear axle target roll moment are distributed according to the vehicle's steering direction.

[0164] like Figure 5 As shown, Figure 5 This is an illustrative flowchart of a vehicle control method provided in an embodiment of this application. Figure 2 The method includes the following steps: Step 501: Obtain the auxiliary parameters and the first attitude parameters corresponding to the vehicle's attitude during the vehicle's operation.

[0165] Step 502: Obtain the axle side stiffness based on the vehicle speed included in the auxiliary parameters.

[0166] Step 503: Generate the centroid sideslip angle based on the Kalman algorithm, axial side stiffness, auxiliary parameters, and first attitude parameters.

[0167] Step 504: Generate the second lateral acceleration based on the centroid sideslip angle, vehicle speed, and the first yaw rate in the first attitude parameters.

[0168] Step 505: Determine the acceleration deviation based on the second lateral acceleration and the first lateral acceleration in the first attitude parameters.

[0169] Step 506: Generate the second yaw rate based on the axial side stiffness, auxiliary parameters, and the first yaw rate of the previous moment among the first attitude parameters.

[0170] Step 507: Determine the angular velocity deviation based on the second yaw rate and the first yaw rate in the first attitude parameters.

[0171] Optionally, the first yaw rate in the first attitude parameters is the first yaw rate at the same moment as the second yaw rate.

[0172] It should be noted that steps 503 to 505 may be executed before steps 506 to 507, or after steps 506 to 507, or steps 503 to 505 and steps 506 to 507 may be executed simultaneously. This embodiment does not limit the execution order of steps 503 to 505 and steps 506 to 507.

[0173] Step 508: Search the preset coefficient list based on the angular velocity deviation and acceleration deviation to obtain the confidence coefficient.

[0174] Step 509: Determine the weighting parameters based on the confidence coefficient and the second noise covariance matrix in the auxiliary parameters.

[0175] Step 510: Based on the weighted Kalman algorithm and weighted parameters, the first attitude parameters and the second attitude parameters are fused to obtain the target lateral acceleration.

[0176] In practice, after obtaining the target lateral acceleration, vehicle control can be performed based on the target lateral acceleration.

[0177] It should be noted that any one or more of steps 501 to 510 can be combined with any one or more of steps 201 to 204 to form a new implementation method according to the needs of implementation and deployment. In addition, any one or more technical features can be selected in steps 501 to 510 and combined with any one or more technical features provided in steps 201 to 204 to form a new implementation method according to the actual deployment needs. Alternatively, any one or more technical features in steps 501 to 510 can be replaced with any one or more technical features provided in steps 201 to 204 to form a new implementation method according to the actual deployment needs. These will not be elaborated on here.

[0178] like Figure 6 As shown, Figure 6 A schematic structural diagram of a vehicle control device provided for embodiments of this application, the device comprising: The parameter acquisition module 601 is used to acquire dynamic parameters during vehicle operation; the dynamic parameters include auxiliary parameters and a first attitude parameter corresponding to the vehicle attitude; the first attitude parameter is obtained by measurement. The parameter generation module 602 is used to generate second attitude parameters corresponding to the vehicle attitude during the vehicle operation based on the dynamic parameters. The coefficient acquisition module 603 is used to acquire a confidence coefficient based on the first attitude parameter and the second attitude parameter; the confidence coefficient is used to characterize the credibility of the first attitude parameter. The fusion processing module 604 is used to perform fusion processing on the first attitude parameters and the second attitude parameters based on the confidence coefficient and the auxiliary parameters to obtain target attitude parameters, so as to perform vehicle control based on the target attitude parameters.

[0179] Optionally, the parameter generation module 602 is specifically used to: obtain the axle side stiffness based on the vehicle speed included in the auxiliary parameters; generate a second lateral acceleration based on the Kalman algorithm, the axle side stiffness, the auxiliary parameters, and the first attitude parameters; generate a second yaw rate based on the axle side stiffness, the auxiliary parameters, and the first attitude parameters; and determine the second lateral acceleration and the second yaw rate as the second attitude parameters.

[0180] Optionally, when generating the second lateral acceleration based on the Kalman algorithm, the axial side stiffness, the auxiliary parameters, and the first attitude parameters, the parameter generation module 602 is specifically used to: generate a centroid sideslip angle based on the Kalman algorithm, the axial side stiffness, the auxiliary parameters, and the first attitude parameters; and generate the second lateral acceleration based on the centroid sideslip angle, the vehicle speed, and the first yaw rate in the first attitude parameters.

[0181] Optionally, the parameter generation module 602, when generating the centroid sideslip angle based on the Kalman algorithm, the axial side stiffness, the auxiliary parameters, and the first attitude parameters, specifically performs the following steps: generating a first state transition matrix and a first input matrix based on the axial side stiffness and the auxiliary parameters, and generating a first input vector based on the auxiliary parameters; generating a first predicted state vector based on the first state transition matrix, the first input matrix, and the first input vector; correcting the first predicted state vector based on the first attitude parameters and the auxiliary parameters to obtain a first target state vector; and obtaining the centroid sideslip angle based on the first target state vector.

[0182] Optionally, when the parameter generation module 602 corrects the first predicted state vector based on the first attitude parameters and the auxiliary parameters to obtain the first target state vector, it specifically performs the following steps: obtaining a first auxiliary matrix, and obtaining a first observation parameter based on the first attitude parameters; the first observation parameter includes a first observation and a first observation matrix; generating a first intermediate vector based on the first auxiliary matrix, the first observation matrix, and a first noise covariance matrix included in the auxiliary parameters; and correcting the first predicted state vector based on the first intermediate vector and the first observation parameters to obtain the first target state vector.

[0183] Optionally, the coefficient acquisition module 603 is specifically used to: calculate the acceleration deviation based on the first lateral acceleration and the second lateral acceleration, and calculate the angular velocity deviation based on the first yaw rate and the second yaw rate; determine the coefficient acquisition strategy based on the acceleration deviation and the angular velocity deviation; and acquire the confidence coefficient based on the coefficient acquisition strategy.

[0184] Optionally, when determining the coefficient acquisition strategy based on the acceleration deviation and the angular velocity deviation, the coefficient acquisition module 603 is specifically configured to: acquire a preset angular velocity deviation threshold and a preset acceleration deviation threshold; detect whether the acceleration deviation is greater than the preset acceleration deviation threshold and whether the angular velocity deviation is greater than the preset angular velocity deviation threshold; if yes, determine the coefficient acquisition strategy as a first acquisition strategy; if no, determine the coefficient acquisition strategy as a second acquisition strategy.

[0185] Optionally, when the coefficient acquisition module 603 acquires the confidence coefficient based on the coefficient acquisition strategy, it is specifically used to: if the coefficient acquisition strategy is a first acquisition strategy, acquire the smallest coefficient in the preset coefficient list as the confidence coefficient; if the coefficient acquisition strategy is a second acquisition strategy, query the corresponding coefficient from the preset coefficient list according to the angular velocity deviation as the confidence coefficient.

[0186] Optionally, the fusion processing module 604 is specifically used to: determine weighting parameters based on the confidence coefficient and the auxiliary parameters; and perform fusion processing on the first attitude parameters and the second attitude parameters based on the weighted Kalman algorithm and the weighting parameters to obtain the target attitude parameters.

[0187] Optionally, when determining the weighting parameters based on the confidence coefficient and the auxiliary parameters, the fusion processing module 604 is specifically used to: obtain the second noise covariance matrix in the auxiliary parameters; and calculate the product of the second noise covariance matrix and the confidence coefficient as the weighting parameters.

[0188] Optionally, when the fusion processing module 604 performs fusion processing on the first attitude parameters and the second attitude parameters based on the weighted Kalman algorithm and the weighting parameters to obtain the target attitude parameters, it specifically performs the following steps: generating a state transition matrix; determining input data and a second observation parameter based on the second attitude parameters and the first attitude parameters; generating initial attitude parameters based on the state transition matrix and the input data; and correcting the initial attitude parameters based on the second observation parameter and the weighting parameter to obtain the target attitude parameters.

[0189] Optionally, the device is further configured to: generate a target roll moment based on the target attitude parameters; and perform vehicle control based on the target roll moment.

[0190] Regarding the apparatus in the above embodiments, the specific manner in which each unit performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0191] Figure 7 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application.

[0192] For example, such as Figure 7 As shown, the vehicle includes a memory 701 and a processor 702. The memory 701 stores executable program code 7011, and the processor 702 is used to call and execute the executable program code 7011 to perform a vehicle control method.

[0193] This embodiment can divide the vehicle into functional modules based on the above method example. For example, each function can be assigned to a separate module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods. When dividing each functional module according to its corresponding function, the vehicle may include: a parameter acquisition module, a parameter generation module, a coefficient acquisition module, and a fusion processing module, etc. It should be noted that all relevant content of each step involved in the above method embodiment can be referenced from the functional description of the corresponding functional module, and will not be repeated here.

[0194] The vehicle provided in this embodiment is used to execute the vehicle control method described above, and therefore can achieve the same effect as the above implementation method.

[0195] When using integrated units, the vehicle may include a processing module and a storage module. The processing module is used to control and manage the vehicle's movements. The storage module is used to support the vehicle in executing relevant program code and data.

[0196] The processing module may be a processor or a controller, which can implement or execute various exemplary logic blocks, modules, and circuits as disclosed in this application. The processor may also be a combination of computing functions, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc., and the storage module may be a memory.

[0197] Embodiments of this application also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the vehicle control method embodiments described above.

[0198] Embodiments of this application also provide a computer-readable storage medium storing a computer program configured to execute the steps in any of the vehicle control method embodiments described above when running.

[0199] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0200] Embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the vehicle control method embodiments described above.

[0201] Embodiments of this application also provide another computer program product, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the vehicle control method embodiments described above.

[0202] The beneficial effects of the above embodiments can be referred to the beneficial effects of the corresponding methods provided above, and will not be repeated here.

[0203] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0204] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0205] In the description of this application, it should be understood that if the terms "upper", "lower", "front", "rear", "left" and "right" are used to indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the position or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0206] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0207] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A vehicle control method, characterized in that, The method includes: The dynamic parameters of the vehicle during operation are acquired; the dynamic parameters include auxiliary parameters and first attitude parameters corresponding to the vehicle attitude; the first attitude parameters are obtained by measurement. Based on the dynamic parameters, a second attitude parameter corresponding to the vehicle attitude during the vehicle operation is generated; Calculate the acceleration deviation and angular velocity deviation based on the first attitude parameters and the second attitude parameters; If the acceleration deviation is greater than a preset acceleration deviation threshold and the angular velocity deviation is greater than a preset angular velocity deviation threshold, the coefficient acquisition strategy is determined to be the first acquisition strategy. Based on the first acquisition strategy, the smallest coefficient in the preset coefficient list is acquired as the confidence coefficient. If the acceleration deviation is less than or equal to the preset acceleration deviation threshold, and / or the angular velocity deviation is less than or equal to the preset angular velocity deviation threshold, the coefficient acquisition strategy is determined to be the second acquisition strategy. Based on the second acquisition strategy, the corresponding coefficient is queried from the preset coefficient list according to the angular velocity deviation as the confidence coefficient. Based on the confidence coefficient and the auxiliary parameters, the first attitude parameters and the second attitude parameters are fused to obtain the target attitude parameters, so as to generate the target roll moment based on the target attitude parameters, and to perform vehicle control based on the target roll moment.

2. The method according to claim 1, characterized in that, The step of generating second attitude parameters corresponding to the vehicle's attitude during operation based on the dynamic parameters includes: Based on the vehicle speed included in the auxiliary parameters, the axle side stiffness is obtained; Based on the Kalman algorithm, the axial side stiffness, the auxiliary parameters, and the first attitude parameters, a second lateral acceleration is generated; Based on the axial side stiffness, the auxiliary parameters, and the first attitude parameters, a second yaw rate is generated; The second lateral acceleration and the second yaw rate are determined as the second attitude parameters.

3. The method according to claim 2, characterized in that, The generation of the second lateral acceleration based on the Kalman algorithm, the axial side stiffness, the auxiliary parameters, and the first attitude parameters includes: Based on the Kalman algorithm, the axial side stiffness, the auxiliary parameters, and the first attitude parameters, the centroid sideslip angle is generated; The second lateral acceleration is generated based on the centroid sideslip angle, the vehicle speed, and the first yaw rate in the first attitude parameters.

4. The method according to claim 3, characterized in that, The process of generating the centroid sideslip angle based on the Kalman algorithm, the axial side stiffness, the auxiliary parameters, and the first attitude parameters includes: Based on the axial side stiffness and the auxiliary parameters, a first state transition matrix and a first input matrix are generated, and based on the auxiliary parameters, a first input vector is generated; Based on the first state transition matrix, the first input matrix, and the first input vector, a first predicted state vector is generated. Based on the first attitude parameters and the auxiliary parameters, the first predicted state vector is corrected to obtain the first target state vector; Based on the first target state vector, the centroid sideslip angle is obtained.

5. The method according to claim 4, characterized in that, The step of correcting the first predicted state vector based on the first attitude parameters and the auxiliary parameters to obtain the first target state vector includes: Obtain a first auxiliary matrix and obtain first observation parameters based on the first attitude parameters; the first observation parameters include a first observation measurement and a first observation matrix; A first intermediate vector is generated based on the first auxiliary matrix, the first observation matrix, and the first noise covariance matrix contained in the auxiliary parameters. Based on the first intermediate vector and the first observation parameters, the first predicted state vector is corrected to obtain the first target state vector.

6. The method according to claim 1, characterized in that, The step of calculating the acceleration deviation and angular velocity deviation based on the first attitude parameters and the second attitude parameters includes: The acceleration deviation is calculated based on the first lateral acceleration and the second lateral acceleration, and the angular velocity deviation is calculated based on the first yaw rate and the second yaw rate.

7. The method according to claim 1, characterized in that, The step of fusing the first attitude parameters and the second attitude parameters based on the confidence coefficient and the auxiliary parameters to obtain the target attitude parameters includes: The weighting parameters are determined based on the confidence coefficient and the auxiliary parameters. The first attitude parameters and the second attitude parameters are fused using the weighted Kalman algorithm and the weighting parameters to obtain the target attitude parameters.

8. The method according to claim 7, characterized in that, The step of determining the weighting parameters based on the confidence coefficient and the auxiliary parameters includes: Obtain the second noise covariance matrix from the auxiliary parameters; The product of the second noise covariance matrix and the confidence coefficient is calculated as the weighting parameter.

9. The method according to claim 7 or 8, characterized in that, The process of fusing the first attitude parameters and the second attitude parameters based on the weighted Kalman algorithm and the weighting parameters to obtain the target attitude parameters includes: Generate a state transition matrix, and determine input data and second observation parameters based on the second attitude parameters and the first attitude parameters; Based on the state transition matrix and the input data, initial attitude parameters are generated; The initial attitude parameters are corrected based on the second observation parameters and the weighted parameters to obtain the target attitude parameters.

10. A vehicle, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the steps of the vehicle control method as described in any one of claims 1 to 9.

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