A parameter identification method for autonomous vehicle motion model

Abstract

An embodiment of the present invention relates to a parameter identification method for a motion model of an autonomous vehicle, the method comprising: determining a model state motion equation of the autonomous vehicle motion model; constructing an identification system based on the model state motion equation; performing offline parameter identification equation analysis based on the identification system equation; performing online recursive parameter identification equation analysis based on the identification system equation; identifying a preset parameter identification mode; when in the offline identification mode, performing offline parameter identification on the parameters of the identification system equation using a first offline identification equation and updating the identification system equation; when in the online identification mode, performing online parameter identification on the parameters of the identification system equation using a first online recursive identification equation group and updating the identification system equation. The present invention not only solves the problem of not updating model parameters in conventional optimization estimation, but also provides both offline and online parameter identification processing methods for autonomous vehicles.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a parameter identification method for an autonomous driving vehicle motion model. Background Art

[0002] When controlling an autonomous vehicle, it performs state estimation based on a pre-defined motion model and controls parameters such as the steering wheel angle based on the state estimation results. The general process of state estimation involves first determining the state transfer function using the state motion equations of the motion model. The state transfer function then determines the transformation relationship between the observed and state variables. Based on this transformation relationship, an error equation is constructed between the state estimate and the true value. The corresponding optimization objective function is then constructed based on this error equation. Solving the optimization objective function determines a series of transformation parameters between the observed and state estimates. These transformation parameters are also called the motion model's transformation parameters. Finally, the determined model parameters are used to estimate the state variables given the known observed variables. In practice, we have found that conventional optimization estimation methods often do not change once the model parameters are determined. However, the actual vehicle motion transformation relationship can vary with vehicle age, road conditions, and other factors, and the corresponding model parameters will also change. Using the same set of model parameters for state estimation, as is done in conventional optimization estimation, will inevitably lead to increased estimation errors, posing a risk to vehicle safety. Summary of the Invention

[0003] The purpose of the present invention is to address the shortcomings of the existing technology and provide a parameter identification method, electronic device and computer-readable storage medium for the motion model of an autonomous driving vehicle. Through the present invention, it can not only solve the problem of not updating the model parameters in conventional optimization estimation, but also provide autonomous driving vehicles with both offline and online parameter identification processing methods, thereby achieving the purpose of improving the safe driving of autonomous driving vehicles.

[0004] To achieve the above objectives, a first aspect of an embodiment of the present invention provides a method for identifying parameters of a motion model of an autonomous driving vehicle, the method comprising:

[0005] Determine the model state motion equations for the autonomous vehicle motion model;

[0006] Constructing an identification system according to the state motion equation of the model to obtain a corresponding identification system equation;

[0007] Performing offline parameter identification equation analysis according to the identification system equation to obtain a corresponding first offline identification equation;

[0008] Performing an online recursive parameter identification equation group analysis according to the identification system equation to obtain a corresponding first online recursive identification equation group;

[0009] Identify a preset parameter identification mode; the parameter identification mode includes an offline identification mode and an online identification mode;

[0010] When the parameter identification mode is an offline identification mode, performing offline parameter identification on the parameters of the identification system equation using the first offline identification equation to obtain an updated first estimated parameter matrix;

[0011] When the parameter identification mode is an online identification mode, performing online parameter identification on the parameters of the identification system equation using the first online recursive identification equation group to obtain the latest second estimated parameter matrix;

[0012] The identified system equations are updated using the latest estimated parameter matrix.

[0013] Preferably, the model state motion equation for determining the motion model of the autonomous driving vehicle specifically includes:

[0014] Determine the dynamic model equations of the autonomous driving vehicle as

[0015]

[0016] The longitudinal axis of the autonomous vehicle is the x-axis, the lateral axis is the y-axis, and the axis perpendicular to the x-axis and y-axis is the z-axis; m is the vehicle mass, is the acceleration along the y-axis, V x is the vehicle longitudinal velocity, ψ is the vehicle yaw angle, is the yaw angular velocity, is the yaw angular acceleration, is the centripetal acceleration, F yf is the front wheel lateral force, F yr is the rear wheel lateral force, I z is the moment of inertia of the vehicle around the z axis, l f is the vertical distance from the center of mass of the car to the front axle, l r is the vertical distance from the center of mass of the vehicle to the rear axle;

[0017] Determine the tire side slip model equations of the autonomous driving vehicle as follows:

[0018]

[0019] Among them, C f is the front wheel cornering stiffness, C r is the rear wheel cornering stiffness, δ is the front wheel steering angle, θ vf is the front wheel speed angle, θ vr is the rear wheel speed angle;

[0020] Substituting the tire cornering model equations into the dynamic model equations, the first state motion equation is obtained as follows:

[0021]

[0022] The yaw rate With the vehicle longitudinal speed V x , the corresponding relationship of road curvature ρ And the corresponding relationship between the front wheel steering angle δ, the steering wheel angle swa, and the steering gear ratio R Substituting the first state motion equation into the second state motion equation is:

[0023]

[0024] The second state motion equation is determined as the model state motion equation output of the autonomous vehicle motion model.

[0025] Preferably, the identification system is constructed according to the model state motion equation to obtain the corresponding identification system equation, specifically including:

[0026] The model state motion equation is converted into a first function group consisting of two transfer functions:

[0027]

[0028] Among them, the vehicle lateral acceleration is the vehicle lateral velocity V y The first-order derivative of The first derivative of the road curvature ρ Right now

[0029] Performing Laplace transform on the first function group to obtain the second function group is:

[0030]

[0031] Where s is the Laplace transform factor;

[0032] The continuous transfer function of the road curvature ρ and the steering wheel angle swa obtained by the second function group is:

[0033]

[0034] Performing z-transform on the continuous transfer function yields a discrete transfer function:

[0035]

[0036] Where z is the z transformation factor, and the parameters b0, b1, b2, a1, and a2 are the transfer function parameters;

[0037] The discrete transfer function is used as the constructed identification system equation, and the identification system equation is determined to be a second-order system, and the parameters b0, b1, b2, a1 and a2 are regarded as parameters to be identified of the identification system equation.

[0038] Preferably, performing offline parameter identification equation analysis according to the identification system equation to obtain a corresponding first offline identification equation specifically includes:

[0039] The identification system equation is converted into a second-order difference equation to obtain the corresponding first difference equation:

[0040] ρ k +a1ρ k-1 +a2ρ k-2 =b0swa k +b1swa k-1 +b2swa k-2 ;

[0041] Where k-1 is the moment before time k, k-2 is the moment before time k-1; ρ k , ρ k-1 , ρ k-2 are the road curvatures at time k, k-1, and k-2 respectively; swa k 、swa k-1 、swa k-2 are the steering wheel angles at time k, k-1, and k-2 respectively;

[0042] The road curvature expression at time k obtained from the first differential equation is:

[0043]

[0044] Convert the road curvature expression into a matrix relationship expression for

[0045]

[0046] in, is the input and output data matrix at time k, is the identification parameter matrix at time k;

[0047] The error equation is constructed as

[0048] Based on the error equation, the corresponding objective function J is constructed in the minimum mean square error way:

[0049] n is the total number of sampling moments;

[0050] set up is the full-time road curvature matrix, is the full-time input and output data matrix, For the full-time identification parameter matrix, use A, Φ, The objective function J is converted to obtain the first conversion formula:

[0051]

[0052] When the first conversion formula reaches the minimum value, the first derivative of the first conversion formula is 0. Thus, the full-time identification parameter matrix that makes the first conversion formula reach the minimum value is obtained The expression is

[0053] The expression Output as the first offline identification equation.

[0054] Preferably, performing online recursive parameter identification equation group analysis based on the identification system equation to obtain the corresponding first online recursive identification equation group specifically includes:

[0055] The input and output data matrix obtained by analyzing the identification system equation The error equation err k Construct the identification parameter matrix The linear recursion equation is

[0056]

[0057] Among them, the gain matrix

[0058] The obtained linear recursion equation and the corresponding gain matrix K k And its parameter matrix P k And the input and output data matrix The first online recursive identification equation group is

[0059]

[0060] Preferably, the step of performing offline parameter identification on the parameters of the identification system equation using the first offline identification equation to obtain the latest first estimated parameter matrix specifically includes:

[0061] Get the latest n+2 groups of first collected data group D i , 1≤i≤n+2; the first collected data group D iIncluding first feedback steering wheel angle First feedback longitudinal velocity and the first feedback yaw rate

[0062] The first feedback longitudinal velocity and the first feedback yaw rate According to the yaw rate With the vehicle longitudinal speed V x , the corresponding relationship of road curvature ρ Calculate the corresponding first feedback road curvature And the first feedback road curvature arrive Construct the corresponding first full-time road curvature matrix

[0063] The first feedback road curvature and the first feedback steering wheel angle Construct the corresponding first input and output data matrix The first feedback road curvature and the first feedback steering wheel angle Construct the corresponding first input and output data matrix By analogy, the first feedback road curvature and the first feedback steering wheel angle Construct the corresponding first input and output data matrix The first input output data matrix arrive Constitute the corresponding first full-time input and output data matrix

[0064] The first full-time road curvature matrix A * and the first full-time input-output data matrix Φ * Substitute into the first offline identification equation Calculate the corresponding first full-time identification parameter matrix And extract the first full-time identification parameter matrix The first identification parameter matrix in as the first estimated parameter matrix to date.

[0065] Preferably, the step of performing online parameter identification on the parameters of the identification system equation using the first online recursive identification equation group to obtain the latest second estimated parameter matrix specifically includes:

[0066] Step 71: Obtain the second feedback steering wheel angle swa' at the current moment jj , the second feedback longitudinal speed V' x,j and the second feedback yaw rate And obtain the second feedback road curvature ρ' at time j-1 and j-2 j-1 ,ρ' j-2 and the second feedback steering wheel angle swa' j-1 、swa' j-2 ; and obtain the preset first initial parameter matrix P' k,0 As the latest first parameter matrix P k-1 , obtain the preset second initial identification parameter matrix As the latest second identification parameter matrix And initialize the iteration counter;

[0067] Step 72: The second feedback longitudinal velocity V' x,j and the second feedback yaw rate According to the yaw rate With the vehicle longitudinal speed V x , the corresponding relationship of road curvature ρ Calculate the latest second road curvature ρ k ;

[0068] Step 73: The second feedback road curvature ρ' j-1 ,ρ' j-2 , and the second feedback steering wheel angle swa' j 、swa' j-1 、swa' j-2 , substitute into the first online recursive identification equations The expression gets the latest second input and output data matrix

[0069] Step 74: The latest first parameter matrix P k-1 , the second input and output data matrix Substitute K into the first online recursive identification equations k The expression is calculated to get the latest first gain matrix K k ;

[0070] Step 75: The latest first gain matrix K k , the second input and output data matrix The first parameter matrix P k-1 Substitute P into the first online recursive identification equations k The expression is calculated to get the latest first parameter matrix P k ;

[0071] Step 76: The latest second identification parameter matrix The first gain matrix K k , the second input and output data matrix Second road curvature ρ k , substitute into the first online recursive identification equations The expression is calculated to obtain the latest second identification parameter matrix

[0072] Step 77, calculate the current error and determining whether the current error err is less than a specified threshold; if so, proceeding to step 80; if not, proceeding to step 78;

[0073] Step 78, adding 1 to the count value of the iteration counter; and determining whether the latest count value has exceeded the specified threshold; if not, proceeding to step 79; if so, going to step 80;

[0074] Step 79: The current first parameter matrix P k As the latest first parameter matrix P k-1 , the second identification parameter matrix As the latest second identification parameter matrix And return to step 74 to continue iteration;

[0075] Step 80: The latest second identification parameter matrix is output as the second estimated parameter matrix.

[0076] A second aspect of an embodiment of the present invention provides an electronic device, including: a memory, a processor, and a transceiver;

[0077] The processor is configured to be coupled to the memory, read and execute instructions in the memory, so as to implement the method steps described in the first aspect above;

[0078] The transceiver is coupled to the processor, and the processor controls the transceiver to send and receive messages.

[0079] A third aspect of an embodiment of the present invention provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a computer, the computer executes the instructions of the method described in the first aspect.

[0080] The embodiment of the present invention provides a parameter identification method, electronic device and computer-readable storage medium for an autonomous vehicle motion model. First, a conventional dynamic model and tire side slip model are used as references to customize a model state motion equation of an improved motion model with road curvature ρ as an observation and steering wheel angle swa as a state variable. Then, a discrete transfer function is analyzed based on the model state motion equation, and the analyzed discrete transfer function is regarded as a second-order identification system equation, thereby determining the set of model parameters to be identified. Then, based on this identification system equation, two parameter identification equations (offline identification equation and online recursive identification equation group) are analyzed, which are respectively applicable to two parameter identification scenarios (offline parameter identification scenario and online parameter identification scenario). Then, corresponding parameter identification processing flows are given for the two parameter identification scenarios. Through the present invention, the problem of not updating model parameters in conventional optimization estimation is solved, and two offline and online parameter identification processing methods are provided for autonomous vehicles, thereby improving the safety of autonomous vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 A schematic diagram of a parameter identification method for an autonomous driving vehicle motion model provided in Example 1 of the present invention;

[0082] Figure 2 This is a structural diagram of an electronic device provided in Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0083] To make the objectives, technical solutions, and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the embodiments described herein are merely some, rather than all, of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0084] A method for identifying parameters of a motion model of an autonomous driving vehicle is provided in a first embodiment of the present invention. Figure 1 A schematic diagram of a parameter identification method for an autonomous driving vehicle motion model provided in the first embodiment of the present invention is shown. The method mainly includes the following steps:

[0085] Step 1, determining the model state motion equation of the autonomous driving vehicle motion model;

[0086] Here, the current step is to customize the model state motion equation of an improved motion model with the road curvature ρ as the observation quantity and the steering wheel angle swa as the state quantity based on the conventional dynamic model and the tire side slip model;

[0087] Specifically including: Step 11, determining the dynamic model equations of the autonomous driving vehicle as

[0088]

[0089] Among them, the longitudinal axis of the autonomous vehicle is the x-axis, the lateral axis is the y-axis, and the axis perpendicular to x and y is the z-axis; m is the vehicle mass, is the acceleration along the y-axis, V x is the vehicle longitudinal velocity, ψ is the vehicle yaw angle, is the yaw angular velocity, is the yaw angular acceleration, is the centripetal acceleration, F yf is the front wheel lateral force, F yr is the rear wheel lateral force, I z is the moment of inertia of the vehicle around the z axis, l f is the vertical distance from the center of mass of the car to the front axle, l r is the vertical distance from the center of mass of the vehicle to the rear axle;

[0090] Here, the above-mentioned dynamic model equations of the autonomous driving vehicle are conventionally known vehicle dynamics equations. The relevant implementation can be obtained by querying the public vehicle dynamics model, and no further details are given here.

[0091] Step 12: Determine the tire side slip model equations of the autonomous vehicle as

[0092]

[0093] Among them, C f is the front wheel cornering stiffness, C r is the rear wheel cornering stiffness, δ is the front wheel steering angle, θ vf is the front wheel speed angle, θ vr is the rear wheel speed angle;

[0094] Here, the so-called front and rear wheel velocity angles refer to the angles between the front and rear wheel position velocity vectors and the vehicle's longitudinal x-axis, respectively. The above tire cornering model equations are conventionally known tire model equations. The relevant implementation can be obtained by querying publicly available tire models and will not be further described here.

[0095] Step 13: Substitute the tire sideways model equations into the dynamic model equations to obtain the first state motion equation:

[0096]

[0097] Here, the tire side slip model equations are substituted into the dynamic model equations, and the acceleration along the y-axis is Considered as The yaw angular acceleration Considered as The above-mentioned first-state motion equation can be obtained. The step-by-step derivation process will not be described here one by one;

[0098] Step 14: convert the yaw rate and the vehicle longitudinal velocity V x , the corresponding relationship of road curvature ρ And the corresponding relationship between the front wheel steering angle δ, the steering wheel angle swa, and the steering gear ratio R Substituting the first state motion equation into the second state motion equation is

[0099]

[0100] Here, the known correspondence between the front wheel steering angle δ, the steering wheel angle swa, and the steering gear ratio R is calculated. Substituting the first state motion equation into the first state motion equation, the motion model corresponding to the first state motion equation is improved, that is, the improved motion model is improved to an improved motion model with the road curvature ρ as the observation quantity and the steering wheel angle swa as the state quantity, and thus the second state motion equation corresponding to the current improved motion model is obtained;

[0101] Step 15: Determine the second state motion equation as the model state motion equation output of the autonomous driving vehicle motion model.

[0102] Step 2: construct an identification system based on the model state motion equation to obtain the corresponding identification system equation;

[0103] Here, the current step performs discrete transfer function analysis based on the model state motion equation of the improved motion model, and regards the analyzed discrete transfer function as a second-order identification system equation, thereby determining the model parameter set to be identified;

[0104] Specifically comprising: step 21, converting the model state motion equation into a first function group consisting of two transfer functions:

[0105]

[0106] Among them, the vehicle lateral acceleration is the vehicle lateral velocity V y The first-order derivative of The first derivative of the road curvature ρ Right now

[0107]

[0108]

[0109] Here, the vehicle lateral acceleration express Take the first derivative of the road curvature ρ express The second state motion equation can be decomposed into two and The expressions of thus constitute the first function group;

[0110] Step 22, perform Laplace transform on the first function group to obtain the second function group:

[0111]

[0112] Where s is the Laplace transform factor;

[0113] Here, the expression of Laplace transform can refer to the public Laplace transform technology implementation, and will not be further described here;

[0114] Step 23: The continuous transfer function between the road curvature ρ and the steering wheel angle swa is obtained from the second function group:

[0115]

[0116] Here, the two expressions of the second function group are combined to eliminate the vehicle lateral acceleration Then the above continuous transfer function can be obtained; through the above continuous transfer function, the correlation coefficient structure for the conversion between road curvature ρ and steering wheel angle swa can be summarized;

[0117] Step 24, perform z-transform on the continuous transfer function to obtain the discrete transfer function:

[0118]

[0119] Where z is the z transformation factor, and the parameters b0, b1, b2, a1, and a2 are the transfer function parameters;

[0120] Here, the purpose of converting the continuous transfer function into a discrete transfer function through z-transformation is to further clarify the coefficient structure of the transfer function. For z-transformation, reference can be made to the publicly available z-transformation technology implementation, which will not be further elaborated here.

[0121] In step 25 , the discrete transfer function is used as the constructed identification system equation, and the identification system equation is determined to be a second-order system, and the parameters b0 , b1 , b2 , a1 , and a2 are regarded as parameters to be identified in the identification system equation.

[0122] Here, the embodiment of the present invention uses the above-mentioned discrete transfer function as the motion transfer function of the improved motion model; and establishes a parameter identification system for the improved motion model based on the above-mentioned discrete transfer function, that is, using the above-mentioned discrete transfer function as the identification system equation of the current parameter identification system. The structure of the identification system equation clearly shows that it is a second-order system, and the parameters b0, b1, b2, a1, and a2 are the set of parameters to be identified for the current improved motion model. After determining the identification system equation and the set of parameters to be identified, two parameter identification equations (offline identification equation and online recursive identification equation group) can be determined through subsequent steps 3 and 4, respectively.

[0123] Step 3, performing offline parameter identification equation analysis based on the identification system equation to obtain the corresponding first offline identification equation;

[0124] Here, the current step is to resolve the offline identification equations of the two parameter identification equations based on the identification system equation output in step 2;

[0125] Specifically, step 31 is to convert the identification system equation into a second-order differential equation to obtain the corresponding first differential equation:

[0126] ρ k +a1ρ k-1 +a2ρ k-2 =b0swa k +b1swa k-1 +b2swa k-2 ;

[0127] Where k-1 is the moment before time k, k-2 is the moment before time k-1; ρ k , ρ k-1 , ρ k-2 are the road curvatures at time k, k-1, and k-2 respectively; swa k 、swa k-1 、swa k-2 are the steering wheel angles at time k, k-1, and k-2 respectively;

[0128] Step 32: The road curvature expression at time k is obtained from the first differential equation:

[0129]

[0130] Step 33: Convert the road curvature expression into a matrix relationship expression for

[0131]

[0132] in, is the input and output data matrix at time k, is the identification parameter matrix at time k;

[0133] Step 34, construct the error equation as

[0134] Step 35: Based on the error equation, the corresponding objective function J is constructed in the minimum mean square error method.

[0135]

[0136] Where n is the total number of sampling moments;

[0137] Step 36, set is the full-time road curvature matrix, is the full-time input and output data matrix, For the full-time identification parameter matrix, use A, Φ, The first transformation formula obtained by transforming the objective function J is:

[0138]

[0139] Step 37, when the first conversion formula reaches the minimum value, the first derivative of the first conversion formula is made to be 0. Thus, the full-time identification parameter matrix that minimizes the first conversion formula is obtained. The expression is

[0140] Step 38, change the expression Output as the first offline identification equation.

[0141] Step 4, performing online recursive parameter identification equation group analysis based on the identification system equation to obtain the corresponding first online recursive identification equation group;

[0142] Here, the current step is to identify the system equation based on the output of step 2, combined with the input and output data matrix given in step 3 Identification parameter matrix Error equation err k The expression structure of the two parameter identification equations is analyzed to obtain the online recursive identification equation group;

[0143] Specifically, step 41 includes: analyzing the input and output data matrix obtained by identifying the system equation Error equation err k Constructing the identification parameter matrix The linear recursion equation is

[0144]

[0145] Among them, the gain matrix

[0146] Here, first combine the input and output data matrix given in step 3 Identification parameter matrix Error equation err k The expression structure of The linear recursive equation and determine the gain matrix K in the linear relationship k and K k The parameter matrix P k , a continuous recursive relationship can be established through this linear recursive equation; the matrix K k 、P k The matrix concept and structure derivation process of are consistent with the matrix concept and derivation process of the gain matrix K and the corresponding error matrix P of conventional filters (such as Kalman filters), so we will not go into details here.

[0147] Step 42: Based on the obtained linear recursion equation and the corresponding gain matrix K k And its parameter matrix P k And the input and output data matrix The first online recursive identification equation group is

[0148]

[0149] Here, the identification parameter matrix obtained in step 41 above is summarized as follows: Expression, gain matrix K k and K k The parameter matrix P k Expressions, and input and output data matrices The expressions constitute the first online recursive identification equations.

[0150] Step 5, identify the preset parameter identification mode; when the parameter identification mode is the offline identification mode, go to step 6; when the parameter identification mode is the online identification mode, go to step 7.

[0151] Here, after the above steps 1-4 of determining the model state motion equation, constructing the identification system equation, constructing the offline identification equation, and constructing the online recursive identification equation group, the corresponding parameter identification process can be selected by identifying the pre-set parameter identification mode parameters. The parameter identification mode here includes two modes: offline identification mode and online identification mode. The embodiment of the present invention allocates a corresponding parameter identification scenario (offline parameter identification scenario, online parameter identification scenario) to each autonomous driving vehicle by pre-setting the parameter identification mode. If it is the offline identification mode, go to step 6 to execute the corresponding offline parameter identification process and obtain the latest identified motion model parameter set. If it is the online identification mode, go to step 7 to execute the corresponding online parameter identification process and obtain the latest identified motion model parameter set.

[0152] Step 6: Use the first offline identification equation to perform offline parameter identification on the parameters of the identification system equation to obtain the latest first estimated parameter matrix; go to step 8;

[0153] Here, the current step is the offline parameter identification processing flow when the corresponding parameter identification mode is the offline identification mode. This process is mainly based on the latest historical data collected in batches and the first offline identification equation. The relevant full-time road curvature matrix A and full-time input and output data matrix Φ are then solved based on the above first offline identification equation to obtain the latest full-time identification parameter matrix And from the full-time identification parameter matrix Extract the nearest identification parameter matrix from Output as the latest first estimated parameter matrix;

[0154] Specifically including: Step 61, obtaining the latest n+2 groups of first collected data groups D i , 1≤i≤n+2;

[0155] Among them, the first collected data group D i Including first feedback steering wheel angle First feedback longitudinal velocity and the first feedback yaw rate

[0156] Here, the first feedback steering wheel angle The first feedback longitudinal velocity is obtained from the vehicle's wire-controlled chassis module. and the first feedback yaw rate Obtained from the vehicle's positioning module; It should be noted that the reason why n+2 groups of first data collection groups D are collected during data collection is i , because when constructing the input and output data matrix Φ, based on the input and output data matrix The expression: The road curvature and square disk angle collection information of the first two moments need to be provided. To ensure that the vector length of the constructed full-time road curvature matrix A and the full-time input and output data matrix Φ matches the total number of sampling moments n of the objective function determined in step 35, it is necessary to collect data at two earlier time points (i.e., i=1 and i=2) based on n, thereby obtaining n+2 sets of first collected data sets D. i ;

[0157] Step 62: The first feedback longitudinal velocity and the first feedback yaw rate According to the yaw rate and the vehicle longitudinal velocity V x , the corresponding relationship of road curvature ρ Calculate the corresponding first feedback road curvature And the first feedback road curvature arrive Construct the corresponding first full-time road curvature matrix A * for

[0158]

[0159] Here, the first feedback road curvature Actually, it is The calculated feedback equivalent curvature; the first feedback road curvature arrive The first full-time road curvature matrix A formed by these n feedback equivalent curvatures * As the full-time road curvature matrix A in the first offline identification equation;

[0160] Step 64: The first feedback road curvature and the first feedback steering wheel angle Construct the corresponding first input and output data matrix The first feedback road curvature and the first feedback steering wheel angle Construct the corresponding first input and output data matrix By analogy, the first feedback road curvature and the first feedback steering wheel angle Construct the corresponding first input and output data matrix Output data matrix from the first input arrive Constitute the corresponding first full-time input and output data matrix Φ * for

[0161]

[0162] Here, it can be understood that the first input-output data matrix is ​​composed of the feedback steering wheel angle and feedback equivalent curvature information of the first collected data group D1, D2, and D3. The first input-output data matrix is ​​formed by the feedback steering wheel angle and feedback equivalent curvature information of the first collected data group D2, D3, and D4. By analogy, the first collected data group D n 、D n+1 、D n+2 The feedback steering wheel angle and feedback equivalent curvature information constitute the first input and output data matrix And the first input output data matrix arrive These n input and output data matrices constitute the first full-time input and output data matrix Φ * As the full-time input and output data matrix Φ in the first offline identification equation;

[0163] Step 65: The first full-time road curvature matrix A * and the first full-time input and output data matrix Φ * Substitute into the first offline identification equation Calculate the corresponding first full-time identification parameter matrix And extract the first full-time identification parameter matrix The first identification parameter matrix in As the latest first estimated parameter matrix; where,

[0164]

[0165] Here, the full-time road curvature matrix A in the first offline identification equation (the first full-time road curvature matrix A * ) and the full-time input and output data matrix Φ (the first full-time input and output data matrix Φ * ) Under the premise that these two parameters are known, solving the first offline identification equation will naturally give the full-time identification parameter matrix in the equation The solution is the first full-time identification parameter matrix Because the purpose of this identification is to obtain the latest identification parameter matrix That is the identification parameter matrix corresponding to time n+2, so from the first full-time identification parameter matrix Extract the first identification parameter matrix As the latest estimated parameter matrix, it is also the first estimated parameter matrix.

[0166] Step 7: Use the first online recursive identification equation group to perform online parameter identification on the parameters of the identification system equation to obtain the latest second estimated parameter matrix; go to step 8;

[0167] Here, the current step corresponds to the online parameter identification processing flow when the parameter identification mode is the online identification mode. This process mainly constructs the input and output data matrix related to the first online recursive identification equation group with the latest collected real data, and performs a cyclic iteration based on a pair of initial P matrices and identification parameter matrices to obtain the latest model parameter identification results at the current moment and output them as the latest second estimated parameter matrix;

[0168] Specifically including: Step 71, obtaining the second feedback steering wheel angle swa' at the current moment j j , the second feedback longitudinal speed V' x,j and the second feedback yaw rate And obtain the second feedback road curvature ρ' at time j-1 and j-2 j-1 ,ρ' j-2 and the second feedback steering wheel angle swa' j-1 、swa' j-2 ; and obtain the preset first initial parameter matrix P' k,0 As the latest first parameter matrix P k-1 , obtain the preset second initial identification parameter matrix As the latest second identification parameter matrix And initialize the iteration counter;

[0169] Here, the second feedback steering wheel angle swa' j is the real-time feedback information obtained from the vehicle's wire-controlled chassis module, and the second feedback longitudinal speed V' x,j and the second feedback yaw rate is the real-time feedback information obtained from the vehicle's positioning module. In the embodiment of the present invention, after each calculation of the corresponding feedback equivalent road curvature based on the current moment's feedback longitudinal velocity and feedback yaw angular velocity, the corresponding feedback equivalent road curvature and the corresponding feedback steering wheel angle are saved. Therefore, at time j, the second feedback road curvature ρ' at time j-1 and time j-2 can be obtained. j-1 ,ρ' j-2 and the second feedback steering wheel angle swa' j-1 、swa' j-2 , and the information of these two moments is obtained in order to construct the second input and output data matrix in the subsequent steps The first initial parameter matrix P' k,0 and the second initial identification parameter matrix is a pair of initial P matrix and identification parameter matrix. The subsequent steps of the embodiment of the present invention will use these two as initial values ​​for loop iteration. The iteration counter here is a counter used to limit the number of subsequent iterations. A specified threshold for indicating the maximum number of iterations is assigned to it. Each iteration is replaced by an increment operation. It is set to 0 by default during initialization.

[0170] Step 72: The second feedback longitudinal velocity V' x,j and the second feedback yaw rate According to the yaw rate and the vehicle longitudinal velocity V x , the corresponding relationship of road curvature ρ Calculate the latest second road curvature ρ k ;

[0171] Here, the second road curvature ρ k Actually, it is The calculated feedback equivalent curvature;

[0172] Step 73: The second feedback road curvature ρ' j-1 ,ρ' j-2 , and the second feedback steering wheel angle swa' j 、swa' j-1 、swa' j-2 , substitute into the first online recursive identification equation group The expression gets the latest second input and output data matrix

[0173] Here, we actually identify the equations in the first online recursive identification system. The expression is to convert the second feedback road curvature ρ' currently obtained in real time into j-1 ,ρ' j-2 , and the second feedback steering wheel angle swa' j 、swa' j-1 、swa' j-2 Substitute the input and output data matrix of the current moment j into it That is, the second input and output data matrix

[0174] Step 74: The latest first parameter matrix P k-1 , the second input and output data matrix Substitute K into the first online recursive identification equation group k The expression is calculated to get the latest first gain matrix K k ;

[0175] Step 75: The latest first gain matrix K k , the second input and output data matrix The first parameter matrix P k-1 Substitute P into the first online recursive identification equation group k The expression is calculated to get the latest first parameter matrix P k ;

[0176] Step 76: The latest second identification parameter matrix The first gain matrix K k , the second input and output data matrix Second road curvature ρ k , substitute into the first online recursive identification equation group The expression is calculated to obtain the latest second identification parameter matrix

[0177] Step 77, calculate the current error And determine whether the current error err is less than the specified threshold; if it is less than the specified threshold, go to step 80; if it is not less than the specified threshold, continue to step 78;

[0178] The specified threshold here is a preset error threshold. In the embodiment of the present invention, when the current error err is less than the specified error threshold, the current round of iteration is considered to be converged, and the current round of iteration is immediately terminated and the process is turned to step 80 to obtain the second identification parameter matrix. Output as the result of this round of iteration;

[0179] Step 78, adding 1 to the count value of the iteration counter; and determining whether the latest count value has exceeded the specified threshold; if not, proceed to step 79; if so, go to step 80;

[0180] The specified threshold here is a preset maximum number of iterations. In the embodiment of the present invention, when the latest count value exceeds the specified maximum number of iterations, even if the error has not converged, the current round of iteration is forced to end and the process goes to step 80 to obtain the second identification parameter matrix. Output as the result of this round of iteration; this is to avoid the problem of excessive iteration caused by slow error convergence;

[0181] Step 79: The current first parameter matrix P k As the latest first parameter matrix P k-1 , the second identification parameter matrix As the latest second identification parameter matrix And return to step 74 to continue iteration;

[0182] Step 80: The latest second identification parameter matrix Output as the second estimated parameter matrix.

[0183] Here, the above steps 74-80 are the iterative processing flow of the online parameter identification process; in the first iteration, the first initial parameter matrix P' k,0 and the second initial identification parameter matrix For the current P k-1 、 Based on the first online recursive identification equation group, the latest And in this error When the error is not less than the specified threshold and the latest count value of the iteration counter does not exceed the maximum number of iterations, the P obtained this time will be k 、 Let it be the P of the next iteration k-1 、 Thus, the process returns to step 74 and continues the second iteration; the cycle continues until the error is When the error is less than the specified threshold or the latest count value of the iteration counter exceeds the maximum number of iterations, go to step 80 to end the current iteration corresponding to time j, and the second identification parameter matrix obtained is Output as the second estimated parameter matrix.

[0184] Step 8: Update the identification system equations using the latest estimated parameter matrix.

[0185] Here, if the parameter identification mode is the offline identification mode, the latest first estimated parameter matrix is ​​used to update the parameters b0, b1, b2, a1 and a2 in the identification system equation; if the parameter identification mode is the online identification mode, the latest second estimated parameter matrix is ​​used to update the parameters b0, b1, b2, a1 and a2 in the identification system equation.

[0186] After completing the identification of the system equations After updating the parameters in , we obtain the latest discrete transfer function for the vehicle motion model. This updated discrete transfer function best matches the current state of the vehicle motion model. Based on this updated discrete transfer function, using the road curvature ρ as the observation, we can obtain the state estimate of the steering wheel angle swa that best matches the current vehicle state. This undoubtedly improves the vehicle's steering control accuracy and ensures safe driving.

[0187] It should be noted that the embodiment of the present invention also develops a set of parameter identification strategies for the autonomous driving vehicle motion model based on the above two parameter identification modes, namely:

[0188] An offline update cycle is preset for each autonomous vehicle; a parameter identification mode is periodically set to an offline identification mode according to the offline update cycle; and in the offline identification mode, parameters of the identification system equation are identified using the first offline identification equation for the current autonomous vehicle to obtain an updated third estimated parameter matrix; and the updated third estimated parameter matrix is ​​used to update the parameters of the identification system equation for the current autonomous vehicle; and after the parameter update is completed, the parameter identification mode is set to a silent mode that is neither an offline identification mode nor an online identification mode; at this time, the autonomous vehicle in the silent mode will not automatically identify and update the parameters of the vehicle motion model;

[0189] An online update start time period is preset for each autonomous driving vehicle; and when the current autonomous driving vehicle is in a driving state and enters the online update start time period, the parameter identification mode is set to the online identification mode; and in the online identification mode, the first online recursive identification equation group is used to perform online parameter identification on the parameters of the identification system equation to obtain the latest fourth estimated parameter matrix; and the latest fourth estimated parameter matrix is ​​used to perform continuous online parameter update operations on the identification system equation of the current autonomous driving vehicle; and after the current time exceeds the online update start time period, the parameter identification mode is set to a silent mode that is neither an offline identification mode nor an online identification mode; at this time, the autonomous driving vehicle in the silent mode will not automatically identify and update the parameters of the vehicle motion model.

[0190] Through the above parameter identification strategy, it is possible to ensure that the vehicle's motion model parameters can be corrected regularly or in real time, and to effectively save the computing resources of the autonomous driving vehicle.

[0191] Figure 2 This is a schematic diagram of the structure of an electronic device provided in the second embodiment of the present invention. The electronic device may be the aforementioned terminal device or server, or may be a terminal device or server connected to the aforementioned terminal device or server to implement the method of the embodiment of the present invention. Figure 2 As shown, the electronic device may include: a processor 301 (such as a CPU), a memory 302, and a transceiver 303; the transceiver 303 is coupled to the processor 301, and the processor 301 controls the transceiver 303's transceiver actions. Various instructions may be stored in the memory 302 for completing various processing functions and implementing the processing steps described in the aforementioned method embodiment. Preferably, the electronic device involved in the embodiment of the present invention further includes: a power supply 304, a system bus 305, and a communication port 306. The system bus 305 is used to realize communication connections between components. The above-mentioned communication port 306 is used for connection and communication between the electronic device and other peripherals.

[0192] exist Figure 2The system bus 305 mentioned in the figure can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. The communication interface is used to realize communication between the database access device and other devices (such as clients, read-write libraries, and read-only libraries). The memory may include random access memory (RAM) and may also include non-volatile memory (Non-Volatile Memory), such as at least one disk storage.

[0193] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), a graphics processing unit (GPU), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0194] It should be noted that an embodiment of the present invention further provides a computer-readable storage medium, which stores instructions. When the computer-readable storage medium is run on a computer, it enables the computer to execute the methods and processing procedures provided in the above embodiments.

[0195] An embodiment of the present invention further provides a chip for executing instructions, which is used to execute the processing steps described in the above method embodiment.

[0196] The embodiment of the present invention provides a parameter identification method, electronic device and computer-readable storage medium for an autonomous vehicle motion model. First, a conventional dynamic model and tire side slip model are used as references to customize a model state motion equation of an improved motion model with road curvature ρ as an observation and steering wheel angle swa as a state variable. Then, a discrete transfer function is analyzed based on the model state motion equation, and the analyzed discrete transfer function is regarded as a second-order identification system equation, thereby determining the set of model parameters to be identified. Then, based on this identification system equation, two parameter identification equations (offline identification equation and online recursive identification equation group) are analyzed, which are respectively applicable to two parameter identification scenarios (offline parameter identification scenario and online parameter identification scenario). Then, corresponding parameter identification processing flows are given for the two parameter identification scenarios. Through the present invention, the problem of not updating model parameters in conventional optimization estimation is solved, and two offline and online parameter identification processing methods are provided for autonomous vehicles, thereby improving the safety of autonomous vehicles.

[0197] Professionals should also be further aware that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0198] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0199] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A parameter identification method for an autonomous driving vehicle motion model, characterized in that: The method comprises: Determine the model state motion equations for the autonomous vehicle motion model; Constructing an identification system according to the model state motion equation to obtain a corresponding identification system equation; Performing offline parameter identification equation analysis according to the identification system equation to obtain a corresponding first offline identification equation; Performing an online recursive parameter identification equation group analysis according to the identification system equation to obtain a corresponding first online recursive identification equation group; Identify a preset parameter identification mode; the parameter identification mode includes an offline identification mode and an online identification mode; When the parameter identification mode is an offline identification mode, performing offline parameter identification on the parameters of the identification system equation using the first offline identification equation to obtain an updated first estimated parameter matrix; When the parameter identification mode is an online identification mode, performing online parameter identification on the parameters of the identification system equation using the first online recursive identification equation group to obtain the latest second estimated parameter matrix; updating the identified system equations using the latest estimated parameter matrix; Wherein, the first offline identification equation is: is the full-time identification parameter matrix, is the full-time input and output data matrix, is the full-time road curvature matrix; ρ k 、 Identification parameter matrix, road curvature and input and output data matrix at time k, 1≤k≤n, n is the total number of sampling times; input and output data matrix It consists of the road curvature at the corresponding time k-1 and k-2 and the steering wheel angle at the time k, k-1, and k-2.

2. The parameter identification method of the autonomous driving vehicle motion model according to claim 1, characterized in that: The model state motion equation for determining the motion model of the autonomous driving vehicle specifically includes: Determine the dynamic model equations of the autonomous driving vehicle as The longitudinal axis of the autonomous vehicle is the x-axis, the lateral axis is the y-axis, and the axis perpendicular to the x-axis and y-axis is the z-axis; m is the vehicle mass, is the acceleration along the y-axis, V x is the vehicle longitudinal velocity, ψ is the vehicle yaw angle, is the yaw angular velocity, is the yaw angular acceleration, is the centripetal acceleration, F yf is the front wheel lateral force, F yr is the rear wheel lateral force, I z is the moment of inertia of the vehicle around the z axis, l f is the vertical distance from the center of mass of the car to the front axle, l r is the vertical distance from the center of mass of the vehicle to the rear axle; Determine the tire side slip model equations of the autonomous driving vehicle as follows: Among them, C f is the front wheel cornering stiffness, C r is the rear wheel cornering stiffness, δ is the front wheel steering angle, θ vf is the front wheel speed angle, θ vr is the rear wheel speed angle; Substituting the tire cornering model equations into the dynamic model equations, the first state motion equation is obtained as follows: in, is the speed of movement along the y-axis; The yaw rate With the vehicle longitudinal speed V x , the corresponding relationship of road curvature ρ And the corresponding relationship between the front wheel steering angle δ, the steering wheel angle swa, and the steering gear ratio R Substituting the first state motion equation into the second state motion equation is: The second state motion equation is determined as the model state motion equation output of the autonomous vehicle motion model.

3. The parameter identification method of the automatic driving vehicle motion model according to claim 2, characterized in that: The identification system is constructed according to the model state motion equation to obtain the corresponding identification system equation, specifically including: The model state motion equation is converted into a first function group consisting of two transfer functions: Among them, the vehicle lateral acceleration is the vehicle lateral velocity V y The first-order derivative of The first derivative of the road curvature ρ Right now Performing Laplace transform on the first function group to obtain the second function group is: Where s is the Laplace transform factor; The continuous transfer function of the road curvature ρ and the steering wheel angle swa obtained by the second function group is: Performing z-transform on the continuous transfer function yields a discrete transfer function: Where z is the z transformation factor, and the parameters b0, b1, b2, a1, and a2 are the transfer function parameters; The discrete transfer function is used as the constructed identification system equation, and the identification system equation is determined to be a second-order system, and the parameters b0, b1, b2, a1 and a2 are regarded as parameters to be identified of the identification system equation.

4. The parameter identification method of the automatic driving vehicle motion model according to claim 3, characterized in that: The performing offline parameter identification equation analysis according to the identification system equation to obtain a corresponding first offline identification equation specifically includes: The identification system equation is converted into a second-order difference equation to obtain the corresponding first difference equation: r k +a1p k-1 +a2p k-2 =b0swa k +b1swa k-1 +b2swa k-2 ; Where k-1 is the moment before time k, k-2 is the moment before time k-1; ρ k , ρ k-1 , ρ k-2 are the road curvatures at time k, k-1, and k-2 respectively; swa k 、swa k-1 、swa k-2 are the steering wheel angles at time k, k-1, and k-2 respectively; The road curvature expression at time k obtained from the first differential equation is: Convert the road curvature expression into a matrix relationship expression for in, is the input and output data matrix at time k, is the identification parameter matrix at time k; The error equation is constructed as Based on the error equation, the corresponding objective function J is constructed in the minimum mean square error way: n is the total number of sampling moments; set up is the full-time road curvature matrix, is the full-time input and output data matrix, For the full-time identification parameter matrix, use A, Φ, The objective function J is converted to obtain the first conversion formula: When the first conversion formula reaches the minimum value, the first derivative of the first conversion formula is 0. Thus, the full-time identification parameter matrix that makes the first conversion formula reach the minimum value is obtained The expression is The expression Output as the first offline identification equation.

5. The parameter identification method of the automatic driving vehicle motion model according to claim 4, characterized in that: The performing online recursive parameter identification equation group analysis based on the identification system equation to obtain the corresponding first online recursive identification equation group specifically includes: The input and output data matrix obtained by analyzing the identification system equation The error equation err k Construct the identification parameter matrix The linear recursion equation is Among them, the gain matrix The obtained linear recursion equation and the corresponding gain matrix K k And its parameter matrix P k And the input and output data matrix The first online recursive identification equation group is 6. The parameter identification method of the automatic driving vehicle motion model according to claim 4, characterized in that: The step of performing offline parameter identification on the parameters of the identification system equation using the first offline identification equation to obtain the latest first estimated parameter matrix specifically includes: Get the latest n+2 groups of first collected data group D i , 1≤i≤n+2; the first collected data group D i Including first feedback steering wheel angle First feedback longitudinal velocity and the first feedback yaw rate The first feedback longitudinal velocity and the first feedback yaw rate According to the yaw rate With the vehicle longitudinal speed V x , the corresponding relationship of road curvature ρ Calculate the corresponding first feedback road curvature And the first feedback road curvature arrive Construct the corresponding first full-time road curvature matrix The first feedback road curvature and the first feedback steering wheel angle Construct the corresponding first input and output data matrix The first feedback road curvature and the first feedback steering wheel angle Construct the corresponding first input and output data matrix By analogy, the first feedback road curvature and the first feedback steering wheel angle Construct the corresponding first input and output data matrix The first input output data matrix arrive Constitute the corresponding first full-time input and output data matrix The first full-time road curvature matrix A * and the first full-time input-output data matrix Φ * Substitute into the first offline identification equation Calculate the corresponding first full-time identification parameter matrix And extract the first full-time identification parameter matrix The first identification parameter matrix in as the first estimated parameter matrix to date.

7. The parameter identification method of the automatic driving vehicle motion model according to claim 5, characterized in that: The step of performing online parameter identification on the parameters of the identification system equation using the first online recursive identification equation group to obtain the latest second estimated parameter matrix specifically includes: Step 71: Obtain the second feedback steering wheel angle swa' at the current moment j j , the second feedback longitudinal speed V' x,j and the second feedback yaw rate And obtain the second feedback road curvature ρ' at time j-1 and j-2 j-1 ,ρ' j-2 and the second feedback steering wheel angle swa' j-1 、swa' j-2 ; and obtain the preset first initial parameter matrix P' k,0 As the latest first parameter matrix P k-1 , obtain the preset second initial identification parameter matrix As the latest second identification parameter matrix And initialize the iteration counter; Step 72: The second feedback longitudinal velocity V' x,j and the second feedback yaw rate According to the yaw rate With the vehicle longitudinal speed V x , the corresponding relationship of road curvature ρ Calculate the latest second road curvature ρ k ; Step 73: The second feedback road curvature ρ' j-1 ,ρ' j-2 , and the second feedback steering wheel angle swa' j 、swa' j-1 、swa' j-2 , substitute into the first online recursive identification equations The expression gets the latest second input and output data matrix Step 74: The latest first parameter matrix P k-1 , the second input and output data matrix Substitute K into the first online recursive identification equations k The expression is calculated to get the latest first gain matrix K k ; Step 75: The latest first gain matrix K k , the second input and output data matrix The first parameter matrix P k-1 Substitute P into the first online recursive identification equations k The expression is calculated to get the latest first parameter matrix P k ; Step 76: The latest second identification parameter matrix The first gain matrix K k , the second input and output data matrix Second road curvature ρ k , substitute into the first online recursive identification equations The expression is calculated to obtain the latest second identification parameter matrix Step 77, calculate the current error and determining whether the current error err is less than a specified threshold; if so, proceeding to step 80; if not, proceeding to step 78; Wherein, the specified threshold value of the current step is a preset error threshold value; Step 78, adding 1 to the count value of the iteration counter; and determining whether the latest count value has exceeded the specified threshold; if not, proceeding to step 79; if so, going to step 80; The specified threshold value of the current step is a preset maximum number of iterations threshold value; Step 79: The current first parameter matrix P k As the latest first parameter matrix P k-1 , the second identification parameter matrix As the latest second identification parameter matrix And return to step 74 to continue iteration; Step 80: The latest second identification parameter matrix is output as the second estimated parameter matrix.

8. An electronic device, characterized in that: include: memory, processors, and transceivers; The processor is configured to be coupled to the memory, read and execute instructions in the memory, so as to implement the method steps described in any one of claims 1 to 7; The transceiver is coupled to the processor, and the processor controls the transceiver to send and receive messages.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, which, when executed by a computer, enable the computer to execute the method according to any one of claims 1 to 7.

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