Four-wheel steering intelligent vehicle transverse kinetic parameter identification method based on time sequence prediction network

Abstract

The invention provides a four-wheel steering intelligent vehicle transverse kinetic parameter identification method based on a time sequence prediction network. A four-wheel steering vehicle kinetic model and a vehicle kinetic parameter identification model based on the historical state of a vehicle are included. According to the method, vehicle state information is obtained from a real vehicle sensor or a virtual simulation driving platform, a vehicle state quantity easily measured by the sensor is selected as an observer data source, the observer data source is divided into historical features and future states, the historical features serve as observer input, the future states serve as training labels, and the training labels serve as training labels. By combining a three-degree-of-freedom four-wheel steering vehicle dynamics theory, the cornering stiffness and rotational inertia of front and rear wheels which are difficult to measure by a sensor are identified. According to the method, the time sequence characteristics of the vehicle state are considered, a time sequence prediction network is reasonably designed, multiple transverse dynamic parameters of the four-wheel steering intelligent vehicle are identified at the same time, meanwhile, the method can be combined with an existing control algorithm to achieve more accurate vehicle stability control, and the driving safety of the intelligent vehicle is effectively improved.

Description

Technical Field

[0001] The invention relates to a lateral dynamics identification method of a four-wheel steering intelligent vehicle based on a time series prediction network, and belongs to the field of automobile driving control. Background Art

[0002] The rapid development of electric vehicles and autonomous driving technology, as well as the gradual maturity of unmanned driving technology, have led to higher requirements for vehicle control stability and safety. The lateral dynamics parameters of a vehicle are one of the important indicators of vehicle driving stability. However, these parameters are usually difficult to measure directly through sensors, so the identification of lateral dynamics parameters based on observer technology has become a low-cost and efficient solution.

[0003] Influenced by technological development and market competition, high-end configurations represented by four-wheel steering have gradually become popular. Reasonable control can make four-wheel steering vehicles have significantly better flexibility and stability than traditional front-wheel steering vehicles, but their more complex dynamic characteristics also put forward higher requirements for the identification of their dynamic parameters.

[0004] To solve the above problems, the present invention provides a method for identifying the lateral dynamic parameters of a four-wheel steering intelligent vehicle based on a timing prediction network. This method uses a timing prediction network with embedded physical knowledge to observe the lateral dynamic parameters of the vehicle, and can perform real-time identification of the lateral stiffness and moment of inertia of the front and rear wheels under different working conditions, thereby achieving more precise vehicle stability control. Summary of the invention

[0005] The technical problem to be solved by the present invention is to design a lateral dynamics parameter observer for a four-wheel steering intelligent vehicle that can be used in most working conditions without adding additional sensors in the face of complex driving environments;

[0006] In order to construct a lateral dynamics parameter observer of a four-wheel steering intelligent vehicle that meets the above requirements, the present invention adopts the following technical solutions: The identification method comprises the following specific steps: Step 1: Build a vehicle status data acquisition system, collect the status information of the four-wheel steering vehicle under different working conditions at a fixed time step through a virtual simulation driving platform or real vehicle sensors, and make the obtained vehicle status data into a data set according to the historical characteristics and future status of the vehicle and divide it into a training set, a verification set, a correction set, and a test set; Step 2: Build a time series prediction network for lateral dynamic parameters of four-wheel steering vehicles based on LSTM and attention mechanism; Step 3: Establish a four-wheel steering vehicle dynamics model, and combine the dynamics formula as a priori physical knowledge with the above-mentioned time series prediction network to form a complete four-wheel steering intelligent vehicle lateral dynamics parameter observer framework; Step 4: Train the time series prediction network through the training set, and use the mean square error as the loss function for optimization. The three-degree-of-freedom vehicle dynamics model is a simplified model, which is different from the real vehicle and Carsim vehicle model, resulting in a certain systematic error between the actual prediction result and the true value. The sixth-order polynomial fitting is implemented through the correction set to obtain accurate vehicle lateral dynamics parameters; Step 5: Combine the trained observer with the lateral and longitudinal MPC trajectory tracking controller of the four-wheel steering vehicle, and verify the performance of the observer under different working conditions through Carsim-Simulink joint simulation.

[0007] Furthermore, the method for identifying lateral dynamic parameters of a four-wheel steering intelligent vehicle in step 1 is characterized by: The vehicle driving state information specifically includes the vehicle's lateral position X, longitudinal position Y, heading angle Yaw, lateral speed v x , longitudinal speed v y , yaw rate ω, front wheel turning angle δ f , rear wheel turning angle δ r , throttle opening throttle, brake master cylinder pressure break; The virtual simulation driving platform includes various devices that can be used to collect vehicle driving status data. The driving simulator hardware used in this method is Logitech G29, and the virtual scene and vehicle simulation platform are built using the Prescan-Carsim-Simulink joint simulation method; The vehicle history characteristics include the lateral speed v x , longitudinal speed v y , yaw rate ω, front wheel turning angle δ f , rear wheel turning angle δ r , longitudinal control amount thr, front wheel steering angle change Δδ f , rear wheel steering angle change Δδ r , longitudinal control amount change Δthr, where the longitudinal control amount, front wheel steering angle change, rear wheel steering angle change, and longitudinal control amount change are obtained by performing simple data processing on the original state data: The maximum pressure of the brake master cylinder is break max When the vehicle is braking: When the vehicle is not braking: thr t =throttle t Then we have: Δthr t =th r -thr t-1 The future state includes the lateral velocity v x , longitudinal speed v y , yaw angular velocity ω.

[0008] Furthermore, the input of the time series prediction network in step 2 is consistent with the observer input, and the output is the lateral dynamic parameters and the front wheel longitudinal force F at each moment in the future state sequence of the four-wheel steering vehicle. x,f and the rear wheel longitudinal force F x，r , where the lateral dynamic parameters include the front wheel cornering stiffness C f , rear wheel cornering stiffness C r 、Vehicle moment of inertia I z , front wheel slip angle correction S f , rear wheel slip angle correction S r .

[0009] Furthermore, the four-wheel steering vehicle dynamics formula in step 3 includes the front and rear wheel slip angle calculation formula, the front and rear wheel lateral force calculation formula, the vehicle lateral and longitudinal acceleration, the yaw angle acceleration calculation formula, the front and rear wheel slip angle α f , α r The calculation formula is as follows: Among them, l f , l r is the distance from the vehicle's center of mass to the front and rear axles. Front and rear wheel lateral force F y,f 、F y,r The calculation formula is as follows: F y,f =α f C f F y，r =α r C r Vehicle lateral and longitudinal acceleration d x d y , yaw angular acceleration d w The calculation formula is as follows: Among them, m is the mass of the vehicle. Finally, the vehicle's lateral and longitudinal speeds and yaw angular velocity at time t+n are calculated using the following formula: Among them, d t is the historical data sampling interval.

[0010] Furthermore, the loss function in step 4 is expressed as: Among them, n is the length of the time series, m is the dimension of the future vehicle state, and w j is the weight of different vehicle states, y ij is the true value of the future vehicle state, is the predicted value of the future vehicle state; The training set includes various free driving conditions, and the correction set is the driving data of the vehicle performing uniform circular motion on a road surface with a fixed adhesion coefficient, the same lane curvature, and a uniform gradient speed distribution, aiming to eliminate influencing factors other than vehicle speed and extract the numerical relationship between system error and vehicle speed through experimental methods; The sixth-order polynomial for fitting the network output to the true value is expressed as: C f =(a f v 6 +b f v 5 +c f v 4 +d f v 3 +e f v 2 +f f v+g f )C f.out C r =(a r v 6 +b r v 5 +c r v 4 +d r v 3 +e r v 2 +f r v+g r )C r.out Among them, C f.out , C r.out is the front and rear wheel cornering stiffness output by the time series prediction network, C f , C r is the front and rear wheel cornering stiffness actually output by the observer.

[0011] Furthermore, the control variables of the lateral and longitudinal MPC trajectory tracking controller in step 5 are the front and rear wheel steering angles and the expected acceleration a of the vehicle, and the steering angles of the four wheels are obtained through the underlying four-wheel steering angle distribution module. The throttle opening throttle and brake master cylinder pressure break are obtained by looking up the table in the bottom longitudinal control module, thereby realizing vehicle control. The vehicle lateral dynamics parameter C in the controller is calculated by the observer f , C r ,I z Real-time updates are performed, and the vehicle instability state is identified through the change amplitude of the lateral dynamic parameters, thereby realizing real-time updates of the vehicle reference speed, improving the stability and driving safety of the vehicle during path tracking.

[0012] Compared with the prior art, the invention provides a method for identifying lateral dynamic parameters of a four-wheel steering intelligent vehicle based on a time series prediction network, which has the following beneficial effects: The introduction of the time series prediction network enables the lateral dynamics parameter observer of the present invention to have the ability of continuous learning. As the data is continuously accumulated during the vehicle's driving process, the observer can cover more working conditions and provide a reliable basis for the lateral dynamics parameters for the vehicle control algorithm. In addition to playing the role of a traditional observer, the observer can also have a certain prediction effect on the future lateral dynamics parameters of the vehicle. Although it is only a second-level prediction, it is sufficient to compensate for the control lag caused by signal transmission time, algorithm execution time, and actuator delay, and provide the control algorithm with dynamic parameters closer to the current moment. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a flow chart of a method for identifying lateral dynamic parameters of a four-wheel steering intelligent vehicle based on a time series prediction network proposed by the present invention;

[0014] Figure 2 is a schematic diagram of a three-degree-of-freedom four-wheel steering vehicle dynamics model in step 1 of the method;

[0015] Figure 3 It is a schematic diagram of the framework of the lateral dynamics parameter observer of the four-wheel steering intelligent vehicle in step three of the method;

[0016] Figure 4 It is a data collection scenario built by Prescan in the embodiment of this method;

[0017] Figure 5 is the front and rear wheel cornering stiffness of the Carsim vehicle in the correction set in the embodiment of the method;

[0018] Figure 6is a diagram of the estimation result of the front and rear wheel cornering stiffness of the vehicle in the correction set directly output by the time series prediction network in the embodiment of the method;

[0019] Figure 7 is a diagram of the estimation result of the front and rear wheel cornering stiffness of the vehicle of the correction set actually output by the observer after fitting with a sixth-order polynomial in the embodiment of the method;

[0020] Figure 8 is the front and rear wheel cornering stiffness of the Carsim vehicle in the test set in the embodiment of the method;

[0021] Fig. 9 is a diagram of the estimation result of the front and rear wheel cornering stiffness of the vehicle actually output by the observer in the embodiment of the method; DETAILED DESCRIPTION

[0022] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0023] Figure 1 The figure is a flow chart of a method for identifying lateral dynamic parameters of a four-wheel steering intelligent vehicle based on a time series prediction network proposed by the present invention, the method comprising the following steps: Step 1: Build a vehicle status data acquisition system, collect the status information of the four-wheel steering vehicle under different working conditions at a fixed time step through a virtual simulation driving platform or real vehicle sensors, and make the obtained vehicle status data into a data set according to the historical characteristics and future status of the vehicle and divide it into a training set, a verification set, a correction set, and a test set; Step 2: Build a time series prediction network for lateral dynamic parameters of four-wheel steering vehicles based on LSTM and attention mechanism; Step 3: Establish a four-wheel steering vehicle dynamics model, and combine the dynamics formula as a priori physical knowledge with the above-mentioned time series prediction network to form a complete four-wheel steering intelligent vehicle lateral dynamics parameter observer framework; Step 4: Train the time series prediction network through the training set, and use the mean square error as the loss function for optimization. The three-degree-of-freedom vehicle dynamics model is a simplified model, which is different from the real vehicle and Carsim vehicle model, resulting in a certain systematic error between the actual prediction result and the true value. The sixth-order polynomial fitting is implemented through the correction set to obtain accurate vehicle lateral dynamics parameters; Step 5: Combine the trained observer with the lateral and longitudinal MPC trajectory tracking controller of the four-wheel steering vehicle, and verify the performance of the observer under different working conditions through Carsim-Simulink joint simulation.

[0024] Figure 1 The vehicle driving status information in step 1 specifically includes the vehicle's lateral position X, longitudinal position Y, heading angle Yaw, lateral speed vx , longitudinal speed v y , yaw rate ω, front wheel turning angle δ f , rear wheel turning angle δ r , throttle opening throttle, brake master cylinder pressure break; The virtual simulation driving platform includes various devices that can be used to collect vehicle driving status data. The driving simulator hardware used in this method is Logitech G29, and the virtual scene and vehicle simulation platform are built using the Prescan-Carsim-Simulink joint simulation method; The vehicle history characteristics include the lateral speed v x , longitudinal speed v y , yaw rate ω, front wheel turning angle δ f , rear wheel turning angle δ r , longitudinal control amount thr, front wheel steering angle change Δδ f , rear wheel steering angle change Δδ r , longitudinal control amount change Δthr, where the longitudinal control amount, front wheel steering angle change, rear wheel steering angle change, and longitudinal control amount change are obtained by performing simple data processing on the original state data: The maximum pressure of the brake master cylinder is break max When the vehicle is braking: When the vehicle is not braking: thr t =throttle t Then we have: Δthr t =thr t -thr t-1 The future state includes the lateral velocity v x , longitudinal speed v y , yaw angular velocity ω.

[0025] Figure 1 The input of the time series prediction network in step 2 is consistent with the observer input, and the output is the lateral dynamic parameters and the front wheel longitudinal force F at each moment in the future state sequence of the four-wheel steering vehicle. x，f and the rear wheel longitudinal force F x，r , where the lateral dynamic parameters include the front wheel cornering stiffness C f , rear wheel cornering stiffness C r 、Vehicle moment of inertia I z , front wheel slip angle correction S f , rear wheel slip angle correction Sr .

[0026] Figure 1 The four-wheel steering vehicle dynamics formula in step 3 includes the front and rear wheel side slip angle calculation formula, the front and rear wheel lateral force calculation formula, the vehicle's lateral and longitudinal acceleration, the yaw angle acceleration calculation formula, and the front and rear wheel side slip angle α f , α t The calculation formula is as follows: Among them, l f , l r is the distance from the vehicle's center of mass to the front and rear axles. Front and rear wheel lateral force F y，f 、F y，r The calculation formula is as follows: F y,f =α f C f F y，r =α r C r Vehicle lateral and longitudinal acceleration d x d y , yaw angular acceleration d w The calculation formula is as follows: Among them, m is the mass of the vehicle. Finally, the vehicle's lateral and longitudinal speeds and yaw angular velocity at time t+n are calculated using the following formula: Among them, d t is the historical data sampling interval.

[0027] Figure 1 The loss function in step 4 is expressed as: Among them, n is the length of the time series, m is the dimension of the future vehicle state, and w j is the weight of different vehicle states, y ij is the true value of the future vehicle state, is the predicted value of the future vehicle state; The training set includes various free driving conditions, and the correction set is the driving data of the vehicle performing uniform circular motion on a road surface with a fixed adhesion coefficient, the same lane curvature, and a uniform gradient speed distribution, aiming to eliminate influencing factors other than vehicle speed and extract the numerical relationship between system error and vehicle speed through experimental methods; The sixth-order polynomial for fitting the network output to the true value is expressed as: Cf =(a f v 6 +b f v 5 +c f v 4 +d f v 3 +e f v 2 +f f v+g f )C f.out C r =(a r v 6 +b r v 5 +c r v 4 +d r v 3 +e r v 2 +f r v+g r )C r.out Among them, C f.out , C r.out is the front and rear wheel cornering stiffness output by the time series prediction network, C f , C r is the front and rear wheel cornering stiffness actually output by the observer.

[0028] Figure 1 In step 5, the control variables of the lateral and longitudinal MPC trajectory tracking controller are the front and rear wheel steering angles and the expected acceleration a of the vehicle. The steering angles of the four wheels are obtained through the underlying four-wheel steering angle distribution module. The throttle opening throttle and brake master cylinder pressure break are obtained by looking up the table in the bottom longitudinal control module, thereby realizing vehicle control. The vehicle lateral dynamics parameter C in the controller is calculated by the observer f , C r ,I z Real-time updates are performed, and the vehicle instability state is identified through the change amplitude of the lateral dynamic parameters, thereby realizing real-time updates of the vehicle reference speed, improving the stability and driving safety of the vehicle during path tracking.

[0029] The specific operation process of this embodiment is as follows: First, several important parameters of the vehicle to be controlled are determined. In this embodiment, the vehicle to be controlled is a front-drive four-wheel steering electric car with a vehicle mass of m = 1412 kg and a moment of inertia of I z =1109.7kg·m 2 , the distance from the center of mass to the front axle lf =1.015m, distance from center of mass to rear axle l r =1.895m, maximum torque of front wheel drive motor T max =309.87N·m, maximum brake pressure of the brake master cylinderbreak max =9Mpa; The above vehicle parameters are synchronously configured to the virtual simulation driving platform Carsim vehicle model, observer vehicle physical parameters, trajectory tracking control simulation Carsim vehicle model, and four-wheel steering vehicle transverse and longitudinal MPC trajectory tracking controller vehicle physical parameters, and the throttle and brake calibration table of the controlled vehicle is prepared for the underlying vehicle longitudinal controller; Use Prescan to build Figure 4 The driving roads shown in the figure include straight roads, curves, roundabouts, intersections and other working conditions. The vehicle driving data of experienced drivers on the roads with adhesion coefficients of 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 and 0.9 are collected through the virtual simulation driving platform and made into a data set. The sampling interval is d t = 0.02s. The data set is divided into a training set and a validation set to train the time series prediction network; The virtual simulation driving platform is used to collect the driving data of vehicles driven by skilled drivers on a loop road with a road adhesion coefficient of 0.9 and a radius of 100m at uniform circular speeds at different speeds. The speeds are 20, 30, 40, 50, 60, 70, 80, 90, and 100 km / h. The collected data are made into a correction set, which is input into the trained time series prediction network to obtain Figure 6 The network output shown is also calculated by the vehicle's front and rear wheel slip angles and front and rear wheel lateral forces output by Carsim. Figure 5 The front and rear wheel cornering stiffness of the vehicle shown in the figure is fitted with the network output results and Carsim results at different vehicle speeds using a sixth-order polynomial. The fitting results are shown in Figure 7 As shown; The driving data of vehicles driven freely by experienced drivers on a road with a road adhesion coefficient of 0.75 are collected through a virtual simulation driving platform and made into a test set. The test set is input into the observer to obtain Fig. 9 The front and rear wheel cornering stiffness observation data shown in the figure are calculated by the front and rear wheel side slip angles and front and rear wheel lateral forces output by Carsim. Figure 8 The front and rear wheel cornering stiffness of the vehicle is shown. It can be seen that the output of the observer is highly consistent with the Carsim data in terms of value and change trend. At this point, the lateral dynamics parameter observer of the vehicle has been built. Add the trained observer to the Simulink model and convert the C output of the observer into f , C r ,Iz The parameter list of the lateral and longitudinal MPC trajectory tracking controller of the four-wheel steering vehicle is updated at a frequency of 0.1s, and based on C f , C r For the target speed v r Real-time update, the updated target speed v r The calculation formula is as follows: v r =v ref -slog(w f (C f.risk -C f )+w r (C r.risk -C r )) where v ref is the original target speed, C f.risk is the front wheel instability threshold, C r.risk is the rear wheel instability threshold, s is the target speed change adjustment weight, w f 、w r is the instability weight of the front and rear wheels; The Carsim-Simulink joint simulation results show that the vehicle lateral stability control performance of the adaptive four-wheel steering vehicle lateral and longitudinal MPC trajectory tracking controller combined with the four-wheel steering intelligent vehicle lateral dynamics parameter identification method provided by the present invention is greatly improved.

[0030] The above embodiments are only some of the embodiments of the present invention. For ordinary technicians in this field, improvements, modifications and related applications can be made without departing from the technical principles of the present invention. These improvements should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for identifying lateral dynamic parameters of a four-wheel steering intelligent vehicle, characterized in that: The following steps are involved: Step 1: Build a vehicle status data acquisition system, collect the status information of the four-wheel steering vehicle under different working conditions at a fixed time step through a virtual simulation driving platform or real vehicle sensors, and make the obtained vehicle status data into a data set according to the historical characteristics and future status of the vehicle and divide it into a training set, a verification set, a correction set, and a test set; Step 2: Build a time series prediction network for lateral dynamic parameters of four-wheel steering vehicles based on LSTM and attention mechanism; Step 3: Establish a four-wheel steering vehicle dynamics model, and combine the dynamics formula as a priori physical knowledge with the above-mentioned time series prediction network to form a complete four-wheel steering intelligent vehicle lateral dynamics parameter observer framework; Step 4: Train the time series prediction network through the training set, and use the mean square error as the loss function for optimization. The three-degree-of-freedom vehicle dynamics model is a simplified model, which is different from the real vehicle and Carsim vehicle model, resulting in a certain systematic error between the actual prediction result and the true value. The sixth-order polynomial fitting is implemented through the correction set to obtain accurate vehicle lateral dynamics parameters; Step 5: Combine the trained observer with the lateral and longitudinal MPC trajectory tracking controller of the four-wheel steering vehicle, and verify the performance of the observer under different working conditions through Carsim-Simulink joint simulation.

2. The method for identifying lateral dynamic parameters of a four-wheel steering intelligent vehicle according to claim 1, characterized in that: In step 1, the vehicle driving status information specifically includes the vehicle's lateral position X, longitudinal position Y, heading angle Yaw, lateral speed v x , longitudinal speed v y , yaw rate ω, front wheel turning angle δ f , rear wheel turning angle δ r , throttle opening throttle, brake master cylinder pressure break; The virtual simulation driving platform includes various devices that can be used to collect vehicle driving status data. The driving simulator hardware used in this method is Logitech G29, and the virtual scene and vehicle simulation platform are built using the Prescan-Carsim-Simulink joint simulation method; The vehicle history characteristics include the lateral speed v x , longitudinal speed v y , yaw rate ω, front wheel turning angle δ f , rear wheel turning angle δ r , longitudinal control amount thr, front wheel steering angle change Δδ f , rear wheel steering angle change Δδ r , longitudinal control amount change Δthr, where the longitudinal control amount, front wheel steering angle change, rear wheel steering angle change, and longitudinal control amount change are obtained by performing simple data processing on the original state data: The maximum pressure of the brake master cylinder is break max When the vehicle is braking: When the vehicle is not braking: thr t =throttle t Then we have: Δthr t =thr t -thr t-1 The future state includes the lateral velocity v x , longitudinal speed v y , yaw angular velocity ω.

3. The method for identifying lateral dynamic parameters of a four-wheel steering intelligent vehicle according to claim 1, characterized in that: In step 2, the input of the time series prediction network is consistent with the observer input, and the output is the lateral dynamic parameters and the front wheel longitudinal force F at each moment in the future state sequence of the four-wheel steering vehicle. x，f and the rear wheel longitudinal force F x，r , where the lateral dynamic parameters include the front wheel cornering stiffness C f , rear wheel cornering stiffness C r 、Vehicle moment of inertia I z , front wheel slip angle correction S f , rear wheel slip angle correction S r .

4. The method for identifying lateral dynamic parameters of a four-wheel steering intelligent vehicle according to claim 1, characterized in that: In step 3, the four-wheel steering vehicle dynamics formula includes the front and rear wheel side slip angle calculation formula, the front and rear wheel lateral force calculation formula, the vehicle lateral and longitudinal acceleration, the yaw angle acceleration calculation formula, the front and rear wheel side slip angle α f , α r The calculation formula is as follows: Among them, l f , l r is the distance from the vehicle's center of mass to the front and rear axles. Front and rear wheel lateral force F y，f 、F y，r The calculation formula is as follows: F y,f =a f C f F y，r =a r C r Vehicle lateral and longitudinal acceleration d x ,d y , yaw angular acceleration d w The calculation formula is as follows: Among them, m is the mass of the vehicle. Finally, the vehicle's lateral and longitudinal speeds and yaw angular velocity at time t+n are calculated using the following formula: Among them, d t is the historical data sampling interval.

5. The method for identifying lateral dynamic parameters of a four-wheel steering intelligent vehicle according to claim 1, characterized in that: In step 4, the loss function is expressed as: Among them, n is the length of the time series, m is the dimension of the future vehicle state, and w j is the weight of different vehicle states, y ij is the true value of the future vehicle state, is the predicted value of the future vehicle state; The training set includes various free driving conditions, and the correction set is the driving data of the vehicle performing uniform circular motion on a road surface with a fixed adhesion coefficient, the same lane curvature, and a uniform gradient speed distribution, aiming to eliminate influencing factors other than vehicle speed and extract the numerical relationship between system error and vehicle speed through experimental methods; The sixth-order polynomial for fitting the network output to the true value is expressed as: C f =(a f v 6 +b f v 5 +c f v 4 +d f v 3 +e f v 2 +f f v+g f )C f.out C r =(a r v 6 +b r v 5 +c r v 4 +d r v 3 +e r v 2 +f r v+g r )C r.out Among them, C f.out , C r.out is the front and rear wheel cornering stiffness output by the time series prediction network, C f , C r is the front and rear wheel cornering stiffness actually output by the observer.

6. The method for identifying lateral dynamic parameters of a four-wheel steering intelligent vehicle according to claim 1, characterized in that: In step 5, the control variables of the lateral and longitudinal MPC trajectory tracking controller are the front and rear wheel steering angles and the expected acceleration a of the vehicle, and the steering angles of the four wheels are obtained through the underlying four-wheel steering angle distribution module. The throttle opening throttle and brake master cylinder pressure break are obtained by looking up the table in the bottom longitudinal control module, thereby realizing vehicle control. The vehicle lateral dynamics parameter C in the controller is calculated by the observer f , C r ,I z Real-time updates are performed, and the vehicle instability state is identified through the change amplitude of the lateral dynamic parameters, thereby realizing real-time updates of the vehicle reference speed, improving the stability and driving safety of the vehicle during path tracking.

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