Self-adaptive identification method for kinetic parameters of self-driving automobile based on SQP and GRNN

Abstract

The invention belongs to the technical field of vehicle dynamics, and relates to an automatic driving vehicle dynamics parameter adaptive identification method based on SQP and GRNN, and the method comprises four parts: automatic driving vehicle mechanism model establishment, GRNN model establishment, training set acquisition based on the SQP algorithm, and parameter adaptive identification based on the trained GRNN model. Kinetic model key parameters which are difficult to obtain by a conventional method are solved in an off-line mode by using an automatic driving automobile mechanism model and an SQP algorithm, and a solving result is used as a training set of a GRNN (Generalized Recurrent Neural Network) model; and the trained GRNN model has the capability of processing the nonlinear relationship among the steering wheel angle, the vehicle speed, the tire cornering stiffness, the suspension roll stiffness and the roll damping in the automatic driving vehicle mechanism model. Finally, accurate kinetic parameters are identified in real time according to the operation condition of the vehicle, and a foundation is laid for effective control of the automatic driving vehicle.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle dynamics and relates to an adaptive identification method of dynamic parameters of an autonomous driving vehicle based on SQP and GRNN. Background Art

[0002] In recent years, the development of self-driving cars has become a prominent topic in the field of intelligent transportation. Compared with traditional manually driven cars, self-driving cars have significant advantages in improving safety, comfort and economy. When driving, self-driving cars must not only consider the vehicle's trajectory tracking ability, but also the vehicle's driving stability, especially for models such as buses and sports cars. Due to their large passenger capacity and high center of mass, once an accident occurs during driving, the economic losses and casualties will be more serious. Effective control, as the main way to achieve safe driving of self-driving cars, relies on a mechanism model that can reflect the actual dynamic characteristics of the vehicle and accurate dynamic parameters. Among them, parameters such as vehicle speed and steering wheel angle in the mechanism model can be measured by sensors, but parameters such as tire cornering stiffness, suspension roll stiffness and roll damping cannot be obtained by conventional methods. Therefore, how to accurately obtain these parameters is crucial to improving the driving safety of self-driving cars.

[0003] In order to obtain these key parameters, experts and scholars combine optimization algorithms and economical sensors to identify or estimate dynamic parameters that are difficult to measure directly. The Chinese patent with application number 202310958288.2 proposes a method for estimating tire cornering stiffness. It reasonably combines a variety of on-board sensors to obtain parameters such as the vehicle's longitudinal speed, lateral acceleration, and yaw angular velocity, and establishes a tire model based on mathematical derivation. It is used to estimate the tire's cornering stiffness based on vehicle driving information. However, this patent mainly estimates the cornering stiffness of the tire, and does not take into account parameters such as suspension roll stiffness and roll damping in complex mechanism models.

[0004] In addition, the paper "Study on Parameter Identification of Simplified Model of Heavy-duty Semitrailer" published in "Automotive Engineering" Vol. 37, No. 6, 2015, established three-degree-of-freedom and five-degree-of-freedom dynamic models of heavy-duty vehicles, deeply analyzed the impact of key model parameters on the output results, and used genetic algorithms to offline identify key parameters such as tire lateral stiffness, suspension roll stiffness and roll damping corresponding to different steering wheel angles and vehicle speeds, and performed linear interpolation fitting on the identified data. The key model parameters can be obtained online according to the real-time status of the vehicle, laying the foundation for parameter estimation and vehicle stability control. However, this method requires a large amount of known data to be stored in the controller, which has a heavy storage burden, and the steering wheel angle, vehicle speed and dynamic parameters of the vehicle are complex nonlinear relationships. The use of linear interpolation fitting methods will affect the accuracy of parameter identification. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a method for adaptively identifying the dynamic parameters of an autonomous driving vehicle based on a sequential quadratic programming algorithm (SQP) and a generalized regression neural network (GRNN), which lays a foundation for obtaining a high-precision dynamic model and effectively controlling the autonomous driving vehicle.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An adaptive identification method for dynamic parameters of an autonomous driving vehicle based on SQP and GRNN comprises four parts: establishing a mechanism model of the autonomous driving vehicle, establishing a GRNN model, obtaining a training set based on an SQP algorithm, and adaptive identification of parameters based on the trained GRNN model. The accuracy of the adaptive identification of the trained GRNN model is verified by an adaptive parameter hybrid dynamic model, which consists of two parts: a trained GRNN model and a mechanism model of the autonomous driving vehicle.

[0008] The establishment of the autonomous driving vehicle mechanism model is specifically as follows: establishing a three-degree-of-freedom model of the autonomous driving vehicle including the lateral motion, yaw motion and roll motion of the vehicle, and the differential equation expression thereof is specifically as follows:

[0009]

[0010] In formula (1): v y is the lateral velocity; k f is the front axle tire cornering stiffness; k r I is the rear axle tire cornering stiffness; x is the moment of inertia of the sprung mass around the x-axis; I xs =I x +m s h 2 is the moment of inertia of the suspension sprung mass around the roll axis; m is the vehicle mass; m s is the sprung mass; h is the height of the center of mass from the roll center; δ f is the front wheel angle, which is linearly related to the steering wheel angle δ; v x is the longitudinal velocity; a is the distance from the center of mass to the front axle; b is the distance from the center of mass to the rear axle; ω is the yaw angular velocity; I z is the moment of inertia of the sprung mass around the z-axis; K s is the suspension roll stiffness; C s is the suspension roll damping; φ is the roll angle; g is the gravitational acceleration.

[0011] Due to the steering wheel angle δ and the longitudinal velocity v x can be easily obtained by the steering angle sensor and the vehicle speed sensor respectively. Therefore, the steering wheel angle δ and the longitudinal speed v x As input to the mechanism model.

[0012] The GRNN model is established by constructing a GRNN model including an input layer, a pattern layer, a summation layer and an output layer. The input layer parameters include variables that can reflect the driving state of the vehicle, such as the steering wheel angle δ, the longitudinal speed v x etc.; the output layer parameters include the key dynamic parameters in the mechanism model of the autonomous vehicle, such as the lateral stiffness k of the front axle tire f , rear axle tire cornering stiffness k r and suspension roll stiffness K s , roll damping C s wait.

[0013] The training set acquisition based on the SQP algorithm is specifically as follows: by using the SQP optimization algorithm, the front axle tire cornering stiffness k corresponding to the mechanism model of the autonomous driving vehicle under typical steering wheel angles and vehicle speeds is obtained. f , rear axle tire cornering stiffness k r and suspension roll stiffness K s , suspension roll damping C s The parameter values ​​are used as the training data set for the GRNN model.

[0014] Furthermore, the solution process of the SQP algorithm is to obtain the key parameter values ​​in the self-driving car mechanism model so that the center of mass slip angle β, yaw rate ω and roll angle φ output by the self-driving car mechanism model are as close as possible to the corresponding dynamic indicators of the actual vehicle. The specific optimization problem can be described as:

[0015]

[0016] In formula (2), is the i-th center of mass sideslip angle in the time series output by the autonomous vehicle mechanism model; is the i-th centroid sideslip angle in the actual vehicle driving time series; is the i-th yaw rate in the time series output by the mechanism model of the autonomous vehicle; is the ith yaw rate in the actual vehicle driving time series; is the i-th roll angle in the time series output by the mechanism model of the autonomous vehicle; is the i-th roll angle in the actual vehicle driving time series; α, γ and ξ are weight coefficients respectively.

[0017] Furthermore, the solution condition obtained based on the training set of the SQP algorithm selects the typical operating condition of the autonomous vehicle, and the steering wheel angle and longitudinal speed range under this condition should cover the conditions of most autonomous vehicles when driving normally. In order to make the solution result serve as the training set of the GRNN model, the solution process is divided into n time series, and the longitudinal speed corresponding to the i-th time series under the solution condition can be expressed as v xi , the corresponding steering wheel angle is expressed as δ i The corresponding front axle tire cornering stiffness, rear axle tire cornering stiffness, suspension roll stiffness and suspension roll damping are expressed as k fi , k ri , K si , C si After solving, we finally get n sets of data sets that can represent the relationship between steering wheel angle, longitudinal speed and tire cornering stiffness (front axle tire cornering stiffness, rear axle tire cornering stiffness), suspension roll stiffness and suspension roll damping, and use them as the training set of the GRNN model.

[0018] The parameter adaptive identification based on the trained GRNN model is specifically as follows: the operating conditions of the autonomous driving vehicle and the corresponding SQP solution results are used as the training set of the GRNN model to learn the steering wheel angle δ and longitudinal speed v of the autonomous driving vehicle. x The nonlinear relationship between the key parameters of the self-driving car mechanism model is that the trained GRNN model can predict the vehicle's steering wheel angle δ and longitudinal speed v x Online identification of the front axle tire cornering stiffness k f , rear axle tire cornering stiffness k r and suspension roll stiffness K s and roll damping C s , to achieve adaptive identification of dynamic parameters of autonomous driving vehicles.

[0019] Furthermore, the principle of parameter adaptive identification based on the trained GRNN model is as follows: the SQP solution result is used as the training data set of the GRNN model, and the input data is set as the steering wheel angle δ and the longitudinal speed v x , the output data is the front axle tire cornering stiffness k f , rear axle tire cornering stiffness k r and suspension roll stiffness K s and roll damping C s; Then, construct the input layer to determine the dimension of the input data, and directly pass the input data to the pattern layer. The pattern layer clusters the input data based on its dimension, uses the Gaussian function to calculate the weight between the input data and each training set sample, multiplies the output data in each training set sample by its corresponding weight, and then adds all the results by the summation layer, and finally outputs the weighted average of all the results by the output layer. After the above training process, the GRNN model can calculate the weighted average of all the results based on the input data steering wheel angle δ and longitudinal speed v x , real-time identification and output of the front axle tire cornering stiffness k f , rear axle tire cornering stiffness k r and suspension roll stiffness K s and suspension roll damping C s .

[0020] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0021] This SQP and GRNN-based adaptive identification method for dynamic parameters of autonomous driving vehicles can use the autonomous driving vehicle mechanism model and the SQP algorithm to offline solve the key parameters of the dynamic model that are difficult to obtain using conventional methods; the SQP solution results are used as the training set of the GRNN model, so that the trained GRNN model has the ability to process the vehicle steering wheel angle δ and longitudinal speed v x The tire cornering stiffness (k f , k r ), suspension roll stiffness K s and suspension roll damping C s Finally, the trained GRNN model can identify accurate dynamic parameters (front axle tire cornering stiffness k) in real time according to the vehicle's operating conditions. f , rear axle tire cornering stiffness k r and suspension roll stiffness K s and suspension roll damping C s ), laying the foundation for the effective control of self-driving cars. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings are incorporated in and constitute a part of this specification and, together with the description, serve to explain the principles of the present invention.

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1The principle block diagram of the method for adaptive identification of dynamic parameters of an autonomous driving vehicle based on SQP and GRNN provided by the present invention;

[0025] Figure 2 A schematic diagram of a three-degree-of-freedom mechanism model of an autonomous driving vehicle provided by the present invention;

[0026] Figure 3 A schematic diagram of an adaptive parameter hybrid dynamics model for an autonomous driving vehicle provided by the present invention;

[0027] Figure 4 This is the curve of the sideslip angle of the center of mass of the autonomous driving car changing with time in the simulation test;

[0028] Figure 5 This is the curve of the yaw rate of the autonomous driving bus changing with time in the simulation test;

[0029] Figure 6 This is the curve of the roll angle changing with time in the simulation test of the autonomous driving car. DETAILED DESCRIPTION

[0030] Here, exemplary embodiments will be described in detail, and the embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are only examples consistent with some aspects of the present invention described in detail in the appended claims.

[0031] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0032] Example

[0033] The principle of the self-driving car adaptive parameter identification method based on SQP and GRNN provided in this embodiment is as follows: Figure 1 As shown:

[0034] First, a three-degree-of-freedom mechanism model that can accurately describe the posture changes of the autonomous driving vehicle is established, such as Figure 2 As shown, there are three degrees of freedom: lateral, yaw and roll. To facilitate the establishment of the model, the following assumptions are made:

[0035] (1) Ignore the influence of the steering system and take the front wheel angle as input;

[0036] (2) The movement of the vehicle body in the pitch direction is not considered;

[0037] (3) Ignore the impact of aerodynamic factors on vehicle status;

[0038] (4) Assume that the road on which the autonomous vehicle is traveling is a horizontal road and ignore the vertical movement of the vehicle;

[0039] (5) Ignore the changes in tire characteristics and self-aligning torque caused by changes in left and right tire loads.

[0040] The moment balance equation along the y-axis is:

[0041]

[0042] The moment balance equation about the z-axis is:

[0043]

[0044] The moment balance equation about the x-axis is:

[0045]

[0046] In formula (3) to formula (5), v y is the lateral speed; F y1 is the lateral force on the front axle tire, F y2 is the lateral force on the rear axle tire; a y is the lateral acceleration; I x is the moment of inertia of the sprung mass around the x-axis; I xs =I x +m s h 2 is the moment of inertia of the suspension sprung mass around the roll axis; m is the vehicle mass; m s is the sprung mass; h is the height of the center of mass from the roll center; δ f is the front wheel turning angle; v x is the longitudinal velocity (i.e., vehicle speed); a is the distance from the center of mass to the front axle; b is the distance from the center of mass to the rear axle; ω is the yaw rate; I z is the moment of inertia of the sprung mass around the z-axis; K s is the suspension roll stiffness; C s is the suspension roll damping; φ is the roll angle; g is the gravitational acceleration.

[0047] The relationship between tire lateral force and sideslip angle is:

[0048]

[0049] In formula (6), α f is the front wheel slip angle; α r is the rear wheel slip angle; k f is the front axle tire cornering stiffness; k r is the rear axle tire cornering stiffness.

[0050] Under the assumption of a small tire slip angle, the following kinematic equations hold:

[0051]

[0052] In formula (7), β≈v y / v x is the vehicle's sideslip angle.

[0053] After sorting, the differential equation expression of the autonomous driving vehicle mechanism model is obtained, as shown in formula (1).

[0054] Furthermore, a GRNN model is established, which includes four layers of neurons: input layer, pattern layer, summation layer and output layer. The corresponding network input is X = [x1, x2, ..., x n ] T , the output is Y=[y1,y2,…,y k ] T The following describes the four-layer structure of the GRNN model:

[0055] (1) Input layer

[0056] The number of neurons in the input layer is equal to the dimension of the input data in the learning sample. Each neuron is a simple distribution unit that directly passes the variable to the pattern layer. x and steering wheel angle δ as the input data of the GRNN model, that is,

[0057]

[0058] (2) Model layer

[0059] The number of neurons in the pattern layer is equal to the number of training samples n. Each neuron corresponds to a different sample. The transfer function of the neuron in the pattern layer is:

[0060]

[0061] In formula (9), X is the network input data; X i is the learning sample corresponding to the i-th neuron.

[0062] (3) Summation layer: Two types of neurons are used for summation in the summation layer.

[0063] The calculation formula for the first category is:

[0064]

[0065] Formula (10) performs arithmetic summation on the outputs of all pattern layer neurons, the connection weight between the pattern layer and each neuron is 1, and the transfer function S D It is expressed as:

[0066]

[0067] The calculation formula for the second category is:

[0068]

[0069] Formula (12) performs weighted summation on the neurons in all pattern layers, and the connection weight between the i-th neuron in the pattern layer and the j-th molecular summation neuron in the summation layer is y ij , transfer function S Nj It is expressed as:

[0070]

[0071] In formula (13), y ij is the jth element in the i-th output data.

[0072] (4) Output layer

[0073] The number of neurons in the output layer is equal to the dimension k of the output data in the learning sample. Each neuron divides the output of the summation layer. The output of neuron j corresponds to the estimated result The jth element of

[0074]

[0075] Furthermore, the SQP optimization algorithm is used to optimize the front axle tire cornering stiffness k in the autonomous vehicle mechanism model. f , rear axle tire cornering stiffness k r and suspension roll stiffness K s and suspension roll damping C s Perform offline solution and use the solution result as the training data set of the GRNN model. The general optimization problem of the SQP optimization algorithm can be expressed as:

[0076]

[0077] In formula (15), f(x) is the optimization objective function, h i (x) = 0 represents an equality constraint; g i (x)≥0 represents an inequality constraint.

[0078] Furthermore, the above-mentioned process of solving the parameters of the mechanism model of the autonomous driving vehicle based on the SQP algorithm is to obtain the corresponding side slip stiffness, roll stiffness and roll damping values, so that the output of the mechanism model of the autonomous driving vehicle is as close as possible to the dynamic indicators of the actual vehicle. Considering that the cost of using a real vehicle experiment is high and the risk is greater during high-speed driving, this embodiment uses the vehicle model in the professional software TruckSim to replace the actual vehicle and output the corresponding dynamic indicators. In the problem of motion control of autonomous driving vehicles, the three commonly used indicators that can characterize the dynamic characteristics of the vehicle are the sideslip angle β of the center of mass, the yaw rate ω and the roll angle φ. Therefore, the solution process can be transformed into the following nonlinear optimization problem:

[0079]

[0080] In formula (16), is the i-th center of mass sideslip angle in the time series output by the autonomous vehicle mechanism model; The i-th center of mass sideslip angle in the time series output by the TruckSim model; is the i-th yaw rate in the time series output by the mechanism model of the autonomous vehicle; is the i-th yaw rate in the time series output by the TrcukSim model; is the i-th roll angle in the time series output by the mechanism model of the autonomous vehicle; is the i-th roll angle in the time series output by the TruckSim model; α, γ and ξ are weight coefficients, which are respectively taken as α=4, γ=3, ξ=1 in this embodiment, and are determined based on experience.

[0081] Furthermore, in this embodiment, the SQP optimization algorithm is used to solve the working condition selection angle step condition. When the vehicle speed is lower than 10km / h, the autonomous driving car generally will not become unstable, and under low-speed conditions, the tire and suspension characteristics are in the linear region, so the solving speed is selected from 20km / h to 60km / h, with an interval of 10km / h. This speed range covers the driving requirements of most autonomous driving cars and can meet actual needs. The steering wheel angle is selected from -150° to 150°, with an interval of 10°. After solving, the lateral stiffness, roll stiffness and roll damping under a series of working conditions are finally obtained. The solution process is divided into n time series, and the longitudinal velocity under the solution condition corresponding to the i-th time series can be expressed as v xi , the corresponding steering wheel angle is expressed as δ i The corresponding front axle tire cornering stiffness, rear axle tire cornering stiffness, suspension roll stiffness and roll damping are expressed as k fi , k ri , K si , Csi (i=1,2,…,n). After the above solution process, a total of 154 sets of data are obtained as the GRNN model training set.

[0082] Furthermore, in order to verify the accuracy of the adaptive identification of the trained GRNN model, the following Figure 3 The self-driving car adaptive parameter hybrid dynamics model shown in FIG. 1 is composed of two parts: a trained GRNN model and a self-driving car mechanism model. The input of the self-driving car adaptive parameter hybrid dynamics model is the longitudinal speed v of the self-driving car. x and steering wheel angle δ. Among them, the longitudinal velocity v x and steering wheel angle δ are from the TruckSim model. The above two sets of parameters (the aforementioned input data) are passed into the trained GRNN model, which can identify the tire cornering stiffness, suspension roll stiffness and suspension roll damping under the vehicle's current operating conditions in real time, and serve as the input of the autonomous vehicle mechanism model, and then calculate the center of mass slip angle β, yaw angular velocity ω and roll angle φ of the autonomous vehicle in real time.

[0083] In order to verify the effectiveness of the proposed adaptive identification method of dynamic parameters of autonomous driving vehicles based on SQP and GRNN, the present invention builds relevant models and develops algorithms in the joint simulation platform of TruckSim and Matlab / Simulink, conducts simulation tests under double lane-changing conditions, and compares the test results with the output results of the TruckSim model and the fixed-parameter dynamics model.

[0084] Some vehicle parameters used in this embodiment are shown in Table 1:

[0085] Table 1 Vehicle parameters

[0086] Vehicle parameters / units value Vehicle mass m / kg 7620 <![CDATA[sprung mass m s / kg]]> 6360 <![CDATA[The moment of inertia I of the sprung mass about the x-axis x / (kg·m -2 )]]> 30782.4 <![CDATA[The moment of inertia I of the sprung mass about the z-axis z / (kg·m -2 )]]> 7695.6 Distance from center of mass to front axle a / m 3.105 Distance from center of mass to rear axle b / m 1.385

[0087] The dynamic parameters used in the fixed parameter dynamics model are empirical parameters, specifically the front axle tire cornering stiffness k f 150000N·rad -1 , rear axle tire cornering stiffness k r 460000N·rad -1 , suspension roll stiffness K s 130000Nm·rad -1 , suspension roll damping C s 30000Nm·rad -1 .

[0088] In this example, the maximum error R between the model output result and the TruckSim output result is selected. maxAs an indicator for evaluating the output results, its calculation formula is:

[0089] R max =|max(y model )-max(y TruckSim )| Formula (17)

[0090] In formula (17), y model Output of fixed parameter dynamic model or adaptive parameter hybrid dynamic model; y TruckSim Output for TruckSim model.

[0091] The speed of the autonomous vehicle is 70 km / h, the steering wheel angle is double lane change condition, and the road adhesion coefficient is 0.85. The test results under the above simulation environment are as follows Figure 4 , Figure 5 and Figure 6 As shown. Figure 4 It can be seen that the center of mass slip angle of the adaptive parameter hybrid dynamics model is closer to the actual vehicle, and the maximum errors of the center of mass slip angle of the fixed parameter dynamics model and the adaptive parameter hybrid dynamics model are 0.0039 rad and 7.84×10 -5 rad; from Figure 5 It can be seen that the yaw rate outputs of the fixed parameter dynamics model and the adaptive parameter hybrid dynamics model are close, and both can accurately reflect the yaw rate changes of the actual vehicle. Figure 6 It can be seen that at the peak of the roll angle change, the fixed parameter dynamics model has a large error with the actual vehicle roll angle change, and cannot accurately reflect the dynamic change of the vehicle roll angle in the nonlinear region; while the output of the adaptive parameter hybrid dynamics model is closer to the actual vehicle roll angle output, and can accurately reflect the actual vehicle roll angle change. The maximum errors of the roll angles of the two models are 0.0061rad and 0.0016rad respectively. This proves that the GRNN model trained with the SQP algorithm solution can be based on the vehicle steering wheel angle δ and longitudinal speed v x The dynamic changes of the vehicle mechanism model are adaptively identified and the effectiveness of the method proposed in this invention is verified.

[0092] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention.

[0093] It should be understood that the present invention is not limited to what has been described above and that various modifications and changes may be made without departing from its scope. The scope of the present invention is limited only by the appended claims.

Claims

1. An adaptive identification method of dynamic parameters of an autonomous driving vehicle based on SQP and GRNN, characterized in that: include: The four parts include the establishment of the mechanism model of the autonomous driving vehicle, the establishment of the GRNN model, the acquisition of the training set based on the SQP algorithm, and the adaptive identification of parameters based on the trained GRNN model; The establishment of the autonomous driving vehicle mechanism model is specifically as follows: establishing a three-degree-of-freedom model of the autonomous driving vehicle including the lateral motion, yaw motion and roll motion of the vehicle, and the differential equation expression thereof is specifically as follows: In formula (1): v y is the lateral velocity; k f is the front axle tire cornering stiffness; k r I is the rear axle tire cornering stiffness; x is the moment of inertia of the sprung mass around the x-axis; I xs =I x +m s h 2 is the moment of inertia of the suspension sprung mass around the roll axis; m is the vehicle mass; m s is the sprung mass; h is the height of the center of mass from the roll center; δ f is the front wheel angle, which is linearly related to the steering wheel angle δ; v x is the longitudinal velocity; a is the distance from the center of mass to the front axle; b is the distance from the center of mass to the rear axle; ω is the yaw angular velocity; I z is the moment of inertia of the sprung mass around the z-axis; K s is the suspension roll stiffness; C s is the suspension roll damping; φ is the roll angle; g is the gravitational acceleration; The GRNN model is established, specifically, by: constructing a GRNN model including an input layer, a pattern layer, a summation layer and an output layer; wherein the input layer parameters include variables that can reflect the driving state of the vehicle, and the output layer parameters include key dynamic parameters in the mechanism model of the autonomous driving vehicle; The training set acquisition based on the SQP algorithm is specifically as follows: by using the SQP optimization algorithm, the front axle tire cornering stiffness k corresponding to the mechanism model of the autonomous driving vehicle under typical steering wheel angles and vehicle speeds is obtained. f , rear axle tire cornering stiffness k r and suspension roll stiffness K s , suspension roll damping C s The parameter values ​​are used as the training data set of the GRNN model; The parameter adaptive identification based on the trained GRNN model is specifically as follows: the operating conditions of the autonomous driving vehicle and the corresponding SQP solution results are used as the training set of the GRNN model to learn the steering wheel angle δ and longitudinal speed v of the autonomous driving vehicle. x The nonlinear relationship between the key parameters of the self-driving car mechanism model is that the trained GRNN model can predict the vehicle's steering wheel angle δ and longitudinal speed v x Online identification of the front axle tire cornering stiffness k f , rear axle tire cornering stiffness k r And the suspension roll stiffness K s and suspension roll damping C s , to achieve adaptive identification of dynamic parameters of autonomous driving vehicles.

2. The method for adaptive identification of dynamic parameters of an autonomous driving vehicle based on SQP and GRNN according to claim 1, characterized in that: The solution process of the SQP optimization algorithm is: to obtain the key parameter values ​​in the mechanism model of the autonomous driving vehicle so that the center of mass slip angle β, yaw rate ω and roll angle φ output by the mechanism model of the autonomous driving vehicle are consistent with the corresponding dynamic indicators of the actual vehicle. The solution formula is: In formula (2), is the i-th center of mass sideslip angle in the time series output by the autonomous vehicle mechanism model; is the i-th centroid sideslip angle in the actual vehicle driving time series; is the i-th yaw rate in the time series output by the mechanism model of the autonomous vehicle; ω i vehicle is the ith yaw rate in the actual vehicle driving time series; is the i-th roll angle in the time series output by the autonomous vehicle mechanism model; φ i vehicle is the i-th roll angle in the actual vehicle driving time series; α, γ and ξ are weight coefficients respectively.

3. The method for adaptive identification of dynamic parameters of an autonomous driving vehicle based on SQP and GRNN according to claim 2, characterized in that: In order to use the solution of the SQP optimization algorithm as the training set of the GRNN model, the solution process is divided into n time series, and the longitudinal velocity corresponding to the i-th time series under the solution condition is expressed as v xi , the corresponding steering wheel angle is expressed as δ i The corresponding front axle tire cornering stiffness, rear axle tire cornering stiffness, suspension roll stiffness and roll damping are expressed as k fi , k ri , K si , C si ; After solving the problem, we finally get n sets of data sets that can represent the relationship between the steering wheel angle, longitudinal speed and the front axle tire cornering stiffness, rear axle tire cornering stiffness, suspension roll stiffness and roll damping, which are used as the training set of the GRNN model.

4. The method for adaptive identification of dynamic parameters of an autonomous driving vehicle based on SQP and GRNN according to claim 1, characterized in that: The specific process of parameter adaptive identification based on the trained GRNN model is as follows: First, the SQP solution result is used as the training data set of the GRNN model, and the input data is set to the steering wheel angle δ and the longitudinal speed v x , the output data is the front axle tire cornering stiffness k f , rear axle tire cornering stiffness k r and suspension roll stiffness K s and roll damping C s ; Then, the input layer is constructed to determine the dimension of the input data, and the input data is directly passed to the pattern layer. The pattern layer clusters the input data based on its dimension, uses the Gaussian function to calculate the weight between the input data and each training set sample, multiplies the output data in each training set sample by its corresponding weight, and then the summation layer adds all the results. Finally, the output layer outputs the weighted average of all the results to complete the training of the GRNN model. Finally, the trained GRNN model is based on the input data steering wheel angle δ and longitudinal velocity v x , real-time identification and output of the front axle tire cornering stiffness k f , rear axle tire cornering stiffness k r and suspension roll stiffness K s and roll damping C s .

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