Vehicle data processing method, device and equipment and computer readable storage medium

Abstract

The invention provides a vehicle data processing method, device and equipment and a computer readable storage medium. The method comprises the steps that the yaw velocity and the side slip angle in the vehicle running process are determined, and a phase plane graph is constructed based on the yaw velocity and the side slip angle; performing dynamic stability analysis on the vehicle based on the phase plane graph to obtain a first area in the phase plane graph; performing vehicle state constraint prediction based on the first region to obtain transverse stability constraint information; determining a cost function of a model prediction controller of the vehicle, and performing control quantity prediction based on the transverse stability constraint information and the cost function to obtain a first control quantity; and determining a feedback compensation control quantity of the vehicle, summing the first control quantity and the feedback compensation control quantity to obtain a second control quantity, and controlling the vehicle to perform trajectory tracking by using the second control quantity and the reference trajectory. According to the invention, the safety and stability of the vehicle in the driving process can be ensured, and the trajectory tracking precision of the vehicle is improved.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving control technology, and in particular to a vehicle data processing method, device, equipment and computer-readable storage medium. Background Art

[0002] Vehicle trajectory tracking is the process of accurately controlling the vehicle's driving path through the control system during the vehicle's driving process, so that it can drive along the predetermined trajectory. With the rapid development of autonomous driving technology, vehicle trajectory tracking has become a key research field. Autonomous driving vehicles need to be able to accurately track the predetermined path to ensure safety and efficiency. It is not only related to the performance and safety of the vehicle, but also the basis for the development of intelligent transportation systems and autonomous driving technology.

[0003] In the related art, a model predictive controller is used to predict the optimal control input within a limited prediction time domain, so that the vehicle can track the desired trajectory. When the vehicle is in extreme operating conditions such as high-speed driving, the safety and stability of the vehicle cannot be guaranteed. In addition, in the application of the model predictive controller, the trajectory tracking accuracy of the vehicle will be seriously affected due to changes in vehicle parameters, external disturbances and errors in modeling parameters. Summary of the invention

[0004] The embodiments of the present application provide a vehicle data processing method, apparatus, device and computer-readable storage medium, which can ensure the safety and stability of the vehicle during driving and improve the trajectory tracking accuracy of the vehicle.

[0005] The technical solution of the embodiment of the present application is implemented as follows:

[0006] The present application provides a vehicle data processing method, the method comprising:

[0007] Determine the yaw rate and the sideslip angle of the center of mass during the vehicle's driving process, and construct a phase plane diagram based on the yaw rate and the sideslip angle of the center of mass;

[0008] Performing a dynamic stability analysis on the vehicle based on the phase plane diagram to obtain a first region in the phase plane diagram;

[0009] Performing vehicle state constraint prediction based on the first region to obtain lateral stability constraint information;

[0010] Determining a cost function of a model predictive controller of the vehicle, and performing control amount prediction based on the lateral stability constraint information and the cost function to obtain a first control amount;

[0011] A feedback compensation control amount of the vehicle is determined, and the first control amount and the feedback compensation control amount are summed to obtain a second control amount, and the vehicle is controlled to track a trajectory using the second control amount and a reference trajectory.

[0012] The present application provides a vehicle data processing device, including:

[0013] A first determination module is used to determine the yaw rate and the sideslip angle of the center of mass during the vehicle's driving process, and to construct a phase plane diagram based on the yaw rate and the sideslip angle of the center of mass;

[0014] A data analysis module, configured to perform a dynamic stability analysis on the vehicle based on the phase plane diagram to obtain a first region in the phase plane diagram;

[0015] A first prediction module, used for predicting vehicle state constraints based on the first area to obtain lateral stability constraint information;

[0016] a second prediction module, used to determine a cost function of a model predictive controller of the vehicle, and to predict a control amount based on the lateral stability constraint information and the cost function to obtain a first control amount;

[0017] The second determination module is used to determine the feedback compensation control amount of the vehicle, and sum the first control amount and the feedback compensation control amount to obtain a second control amount, and use the second control amount and a reference trajectory to control the vehicle to perform trajectory tracking.

[0018] An embodiment of the present application provides an electronic device, the electronic device comprising:

[0019] A memory for storing computer executable instructions or computer programs;

[0020] The processor is used to implement the vehicle data processing method provided in the embodiment of the present application when executing the computer executable instructions or computer program stored in the memory.

[0021] An embodiment of the present application provides a computer-readable storage medium storing computer-executable instructions or a computer program for implementing the vehicle data processing method provided in the embodiment of the present application when executed by a processor.

[0022] An embodiment of the present application provides a computer program product, including computer executable instructions or a computer program. When the computer executable instructions or the computer program are executed by a processor, the vehicle data processing method provided in the embodiment of the present application is implemented.

[0023] The embodiments of the present application have the following beneficial effects:

[0024] By using the embodiment of the present application, the yaw rate and the sideslip angle of the center of mass of the vehicle during driving are determined, a phase plane diagram is constructed based on the yaw rate and the sideslip angle of the center of mass, and then the dynamic stability analysis of the vehicle is performed based on the phase plane diagram to obtain the first area in the phase plane diagram, and then the vehicle state constraint prediction is performed based on the first area to obtain the lateral stability constraint information, and the phase plane method analysis is realized through the lateral stability related parameters, and a safer lateral stability boundary condition is designed for the model predictive controller, so as to ensure the safety and stability of the vehicle during driving, and then the cost function of the model predictive controller of the vehicle is determined, and the control amount is predicted based on the lateral stability constraint information and the cost function to obtain the first control amount, and the feedback compensation control amount of the vehicle is determined, and the first control amount and the feedback compensation control amount are summed to obtain the second control amount, and the second control amount and the reference trajectory are used to control the vehicle to track the trajectory. In this way, the vehicle trajectory tracking is realized by using the model predictive controller under the improved lateral stability constraint state, and the feedback compensation control amount is introduced to compensate for the deviation of the vehicle trajectory tracking, thereby improving the trajectory tracking accuracy of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a schematic diagram of an application mode of the vehicle data processing method provided in an embodiment of the present application;

[0026] Figure 2 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application;

[0027] Figure 3A is a first flow chart of the vehicle data processing method provided in an embodiment of the present application;

[0028] Figure 3B is a second flow chart of the vehicle data processing method provided in an embodiment of the present application;

[0029] Figure 3C is a third flow chart of the vehicle data processing method provided in an embodiment of the present application;

[0030] Figure 3D is a fourth flow chart of the vehicle data processing method provided in an embodiment of the present application;

[0031] Figure 3E is a fifth flow chart of the vehicle data processing method provided in an embodiment of the present application;

[0032] Figure 4 It is a flow chart of the vehicle data processing method provided in an embodiment of the present application.

[0033] It should be pointed out that the above-mentioned "first" and "second" are only used to distinguish different solutions, and do not represent the degree of superiority or inferiority of the solutions or the priority in the implementation process. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0035] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0036] In the following description, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0037] The relevant data collection and processing in the embodiments of this application should be strictly in accordance with the requirements of relevant laws and regulations when applied in examples, obtain the informed consent or separate consent of the personal information subject, and carry out subsequent data use and processing within the scope of authorization of laws and regulations and the personal information subject.

[0038] In the embodiments of the present application, the term "module" or "unit" refers to a computer program or a part of a computer program with a predetermined function, and works together with other related parts to achieve a predetermined goal, and can be implemented in whole or in part by using software, hardware (such as processing circuits or memories) or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be part of an overall module or unit that includes the function of the module or unit.

[0039] Unless otherwise defined, all technical and scientific terms used in the embodiments of the present application have the same meanings as those commonly understood by those skilled in the art. The terms used in the embodiments of the present application are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0040] Before further describing the embodiments of the present application in detail, the nouns and terms involved in the embodiments of the present application are explained. The nouns and terms involved in the embodiments of the present application are subject to the following interpretations.

[0041] 1) Lateral stability boundary: It is the limit condition under which the vehicle can maintain lateral stability during driving.

[0042] 2) Proportional-Integral-Differential (PID) feedback controller: It is a closed-loop controller widely used in industrial control systems and vehicle control systems. It includes three main control functions: proportional, integral and differential. The system performance is optimized by adjusting the proportion of these three control functions.

[0043] 3) Model predictive controller: It is a controller used to optimize the vehicle's driving path and improve the vehicle's driving stability and accuracy.

[0044] The embodiments of the present application provide a vehicle data processing method, apparatus, device and computer-readable storage medium, which can ensure the safety and stability of the vehicle during driving and improve the trajectory tracking accuracy of the vehicle.

[0045] The following describes an exemplary application of the electronic device provided by the embodiment of the present application. The electronic device provided by the embodiment of the present application can be implemented as various types of terminals such as a laptop computer, a tablet computer, a desktop computer, a set-top box, a smart phone, a smart speaker, a smart watch, a smart TV, and a vehicle-mounted terminal, and can also be implemented as a server. The following describes an exemplary application when the device is implemented as a server.

[0046] See also Figure 1 , Figure 1 This is a schematic diagram of an application mode of the vehicle data processing method provided in an embodiment of the present application, for example, Figure 1 The server 200, the network 300 and the terminal 400 are involved. The terminal 400 is connected to the server 200 via the network 300. The network 300 can be a wide area network or a local area network, or a combination of the two.

[0047] During vehicle trajectory tracking, the server 200 obtains vehicle state variables during vehicle driving, where the vehicle state variables are yaw rate and sideslip angle of the center of mass, and constructs a phase plane diagram based on the yaw rate and sideslip angle of the center of mass; performs dynamic stability analysis on the vehicle based on the phase plane diagram to obtain a first area in the phase plane diagram; performs vehicle state constraint prediction based on the first area to obtain lateral stability constraint information; determines the cost function of the vehicle's model predictive controller, and predicts the control amount based on the lateral stability constraint information and the cost function to obtain a first control amount; determines the vehicle's feedback compensation control amount, and sums the first control amount and the feedback compensation control amount to obtain a second control amount. The server 200 sends the second control amount to the terminal 400, and the terminal 400 uses the second control amount and the reference trajectory to perform trajectory tracking. The terminal 400 can be an autonomous driving vehicle.

[0048] In some embodiments, the server (e.g., server 200) may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms. The terminal 400 may be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a car terminal, etc., but is not limited thereto. The terminal and the server may be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiments of the present application.

[0049] See also Figure 2 , Figure 2 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application, the electronic device may be a terminal or a server, Figure 2 The electronic device shown includes: at least one processor 410, a memory 450, and at least one network interface 420. The various components in the electronic device are coupled together through a bus system 440. It is understood that the bus system 440 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 440 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, the bus system 440 is not described in detail. Figure 2 Various buses are labeled as bus system 440 .

[0050] The processor 410 can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., where the general-purpose processor can be a microprocessor or any conventional processor, etc.

[0051] The memory 450 may be removable, non-removable, or a combination thereof. Exemplary hardware devices include solid-state memory, hard disk drives, optical disk drives, etc. The memory 450 may optionally include one or more storage devices that are physically remote from the processor 410.

[0052] The memory 450 includes a volatile memory or a non-volatile memory, and may also include both volatile and non-volatile memories. The non-volatile memory may be a read-only memory (ROM), and the volatile memory may be a random access memory (RAM). The memory 450 described in the embodiments of the present application is intended to include any suitable type of memory.

[0053] In some embodiments, memory 450 can store data to support various operations, examples of which include programs, modules, and data structures, or a subset or superset thereof, as exemplarily described below.

[0054] The operating system 451 includes system programs for processing various basic system services and performing hardware-related tasks, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic businesses and process hardware-based tasks.

[0055] The network communication module 452 is used to reach other electronic devices via one or more (wired or wireless) network interfaces 420. Exemplary network interfaces 420 include: Bluetooth, Wireless Authentication (WiFi), and Universal Serial Bus (USB).

[0056] In some embodiments, the device provided in the embodiments of the present application can be implemented in software. Figure 2 A vehicle data processing device 455 stored in a memory 450 is shown, which may be software in the form of a program and a plug-in, etc., and includes the following software modules: a first determination module 4551, a data analysis module 4552, a first prediction module 4553, a second prediction module 4554, and a second determination module 4555. These modules are logical, and therefore may be arbitrarily combined or further split according to the functions implemented. The functions of each module will be described below.

[0057] The vehicle data processing method provided in the embodiment of the present application will be explained in combination with the exemplary application and implementation of the server device provided in the embodiment of the present application.

[0058] The following describes the vehicle data processing method provided in the embodiment of the present application. For ease of understanding, the vehicle data processing method provided in the embodiment of the present application is described by way of example using an autonomous driving vehicle trajectory tracking scenario as an example.

[0059] As mentioned above, the electronic device implementing the vehicle data processing method of the embodiment of the present application may be a terminal, a server, or a combination of the two. Next, the vehicle data processing method provided by the embodiment of the present application is described by taking the electronic device as a server as an example. Figure 3A , Figure 3A is a first flow chart of the vehicle data processing method provided in the embodiment of the present application, which will be combined with Figure 3A The steps shown are explained.

[0060] In step 301, the yaw rate and the sideslip angle of the center of mass during the vehicle's driving process are determined, and a phase plane diagram is constructed based on the yaw rate and the sideslip angle of the center of mass.

[0061] Here, yaw rate is the angular velocity of the vehicle when it turns left or right in the horizontal plane. Yaw rate is an important parameter to measure the maneuverability of the vehicle, reflecting the response speed and stability of the vehicle when turning. The center of mass slip angle is a physical parameter that can describe the degree of deviation from the vehicle's trajectory tracking. It can characterize the degree of lateral slip of the vehicle. A larger center of mass slip angle indicates that the vehicle has a larger lateral slip, which will make the vehicle difficult to control. Therefore, the limit of the center of mass slip angle determines the maximum lateral deviation angle that the vehicle can safely control. When the center of mass slip angle is small, the size of the yaw rate will determine the vehicle's turning ability. Therefore, the center of mass slip angle and yaw rate can be used to characterize the stability and turning performance of the vehicle.

[0062] The yaw rate of the vehicle during driving can be directly observed by the vehicle sensor, while the center of mass sideslip angle is a physical quantity that is difficult to observe. For example, the center of mass sideslip angle can be determined by the following formula (1):

[0063] β=arctan(v y / v x ), (1)

[0064] Where β is the sideslip angle of the center of mass, v y represents the lateral speed, v x Indicates the longitudinal vehicle speed.

[0065] According to the vehicle dynamics model, the differential equations of the lateral and yaw of the vehicle system can be obtained, and after simplifying the differential equations, the differential equations about the sideslip angle and yaw rate of the center of mass can be obtained. For example, the differential equations about the sideslip angle and yaw rate of the center of mass can be determined by the following formula (2):

[0066]

[0067] in, represents the differential of the sideslip angle of the center of mass, represents the differential of the yaw rate, δ f represents the front wheel turning angle, m represents the vehicle mass, F lf Indicates the longitudinal force on the front wheel, F cf Indicates the lateral force on the front wheel, F crIndicates the lateral force on the rear wheel, I z represents the moment of inertia, L f Indicates the distance from the center of mass of the vehicle to the front axle of the vehicle, L r Represents the distance from the vehicle's center of mass to the vehicle's rear axle.

[0068] The lateral acceleration can be directly observed by a sensor. For example, the lateral acceleration can be determined by the following formula (3):

[0069]

[0070] Among them, a y represents the lateral acceleration, δ f represents the front wheel turning angle, m represents the vehicle mass, and F lf Indicates the longitudinal force on the front wheel, F cf Indicates the lateral force on the front wheel, F cr Indicates the lateral force on the rear wheel.

[0071] By combining the differential equations for the sideslip angle, yaw rate, and lateral acceleration, the sideslip angle of the center of mass during vehicle travel can be determined.

[0072] The phase plane is a two-dimensional space composed of vehicle state variables, which are yaw rate and sideslip angle of center of mass. A vehicle dynamics model is established and linearized to obtain a phase plane model. The yaw rate is used as the horizontal coordinate in the phase plane model, and the sideslip angle of center of mass is used as the vertical coordinate in the phase plane model to obtain the phase plane diagram. Each point in the phase plane diagram represents a state of the vehicle, that is, the yaw rate and sideslip angle of center of mass at a certain moment.

[0073] Continue to refer Figure 3A In step 302, a dynamic stability analysis of the vehicle is performed based on the phase plane diagram to obtain a first area in the phase plane diagram.

[0074] Here, dynamic stability analysis is to analyze the stable area of ​​the vehicle during driving. The stable area is the area in the phase plane diagram where the vehicle can drive along the predetermined trajectory without deviating from the driving phenomenon due to external disturbances or internal factors, that is, the first area.

[0075] In some embodiments, a dynamic stability analysis of the vehicle is performed based on a phase plane diagram to obtain a first area in the phase plane diagram, which can be achieved by executing the following steps: determining the equilibrium point and N phase trajectories of the vehicle in the phase plane diagram, where N is a positive integer; determining a target phase trajectory that converges to the equilibrium point from the N phase trajectories; and determining the area in the phase plane diagram where the target phase trajectory is located as the first area.

[0076] Here, the equilibrium point of the vehicle system is first determined in the phase plane diagram. The equilibrium point can represent the stable state of the vehicle when there is no external disturbance. The equilibrium point usually corresponds to a zero sideslip angle at the center of mass and a zero yaw rate, indicating that the vehicle is moving straight and does not yaw. According to the principles of vehicle dynamics, differential equations describing the lateral and yaw motions of the vehicle are established. The yaw rate and sideslip angle at the center of mass are used as vehicle state variables that change with time, and the vehicle state variables are used to analyze the differential equations. Based on the solution of the differential equations, N phase trajectories of the vehicle under different vehicle state variables are drawn.

[0077] Determine the distance between each trajectory point and the equilibrium point on the phase trajectory. If the distance between the trajectory point and the equilibrium point gets closer and closer over time, it is determined that the phase trajectory converges to the equilibrium point. If the phase trajectory converges to the equilibrium point, it indicates that the vehicle is in a stable state. Determine the phase trajectory that converges to the equilibrium point from the N phase trajectories, that is, the target phase trajectory. The area where the target phase trajectory is located in the phase plane diagram is determined as the area where the vehicle maintains stable driving, that is, the first area.

[0078] In an embodiment of the present application, a target phase trajectory that converges to an equilibrium point is determined from the N phase trajectories in the phase plane diagram, and the area where the target phase trajectory is located is determined as the first area, which can facilitate subsequent vehicle state constraint prediction based on the first area to obtain safer lateral stability boundary conditions.

[0079] Continue to refer Figure 3A In step 303, vehicle state constraint prediction is performed based on the first area to obtain lateral stability constraint information.

[0080] Here, the vehicle state constraint prediction is to predict the lateral stability boundary of the vehicle. The lateral stability boundary is the limit condition for the vehicle to maintain lateral stability during driving, that is, the lateral stability constraint information. Within the lateral stability boundary, the vehicle can drive safely without instability such as skidding and tail swinging.

[0081] In some embodiments, see Figure 3B , Figure 3B is a second flow chart of the vehicle data processing method provided in an embodiment of the present application, Figure 3A Step 303 shown can be performed by Figure 3B Steps 3031 to 3033 are implemented as described in detail below.

[0082] In step 3031, the yaw rate constraint information of the vehicle in the first area is determined, and the center of mass sideslip angle constraint information of the vehicle in the first area is determined.

[0083] Here, the yaw rate boundary of the vehicle is predicted in the first region to obtain the yaw rate constraint information, and the center of mass sideslip angle boundary of the vehicle is predicted in the first region to obtain the center of mass sideslip angle constraint information.

[0084] In some embodiments, the yaw rate constraint information includes a minimum yaw rate and a maximum yaw rate. Determining the yaw rate constraint information of the vehicle in the first area can be achieved by performing the following steps: determining the longitudinal vehicle speed and road adhesion coefficient of the vehicle in the first area; multiplying the longitudinal vehicle speed, road adhesion coefficient and gravity acceleration to obtain the maximum yaw rate; and determining the inverse of the maximum yaw rate as the minimum yaw rate.

[0085] Here, the longitudinal speed is the forward speed of the vehicle during driving. The road adhesion coefficient is a measure of the friction between the vehicle tires and the road surface. The higher the road adhesion coefficient, the greater the friction between the vehicle tires and the road surface, and the vehicle is less likely to slip or lose control. When the vehicle is driving in the first area, the longitudinal speed and road adhesion coefficient of the vehicle are obtained, and the longitudinal speed, road adhesion coefficient and gravity acceleration are multiplied to obtain the maximum yaw angular velocity, and the maximum yaw angular velocity is inversely converted to obtain the minimum yaw angular velocity.

[0086] For example, the yaw rate limit can be determined by the following formula (4):

[0087]

[0088] Among them, γ max represents the maximum yaw rate, γ min represents the minimum yaw rate, μ represents the road adhesion coefficient, and v x represents the longitudinal vehicle speed and g represents the acceleration due to gravity.

[0089] In the embodiment of the present application, the yaw rate constraint information of the vehicle in the first area is determined in combination with the longitudinal vehicle speed, road adhesion coefficient and gravity acceleration of the vehicle in the first area, so as to ensure the safety and stability of the vehicle when turning left and right in the horizontal plane.

[0090] In some embodiments, the center of mass side slip angle constraint information includes first center of mass side slip angle constraint information of the left front wheel, second center of mass side slip angle constraint information of the right front wheel, third center of mass side slip angle constraint information of the left rear wheel, and fourth center of mass side slip angle constraint information of the right rear wheel. Figure 3C , Figure 3C is a third flow chart of the vehicle data processing method provided in an embodiment of the present application, Figure 3A In step 3031, "determining the center of mass side slip angle constraint information of the vehicle in the first area" can be performed by Figure 3CSteps 30311 to 30314 are implemented as described in detail below.

[0091] In step 30311, a first constraint analysis is performed based on the longitudinal speed of the vehicle in the first area, the first distance from the center of mass of the vehicle to the front axle of the vehicle, the front wheel turning angle, the first saturated sideslip angle of the left front wheel and the second wheelbase of the rear wheel to obtain the first center of mass sideslip angle constraint information.

[0092] Here, the first saturated slip angle is the slip angle corresponding to the tire of the left front wheel when the tire reaches its lateral force limit, and the slip angle is the angle between the tire centerline and the vehicle driving direction. The first constraint analysis process is to predict the center of mass slip angle boundary of the left front wheel to obtain the first center of mass slip angle constraint information, and the first center of mass slip angle constraint information includes the minimum center of mass slip angle of the left front wheel and the maximum center of mass slip angle of the left front wheel.

[0093] In some embodiments, see Figure 3D , Figure 3D is a fourth flow chart of the vehicle data processing method provided in an embodiment of the present application, Figure 3C Step 30311 shown can be performed by Figure 3D Steps 303111 to 303115 are implemented as described in detail below.

[0094] In step 303111, a tangent trigonometric function conversion is performed on the difference between the front wheel steering angle and the first saturated sideslip angle to obtain a first conversion value; and a tangent trigonometric function conversion is performed on the sum of the front wheel steering angle and the first saturated sideslip angle to obtain a second conversion value.

[0095] Here, the front wheel steering angle is subtracted from the first saturated sideslip angle to obtain a difference, and the difference is subjected to a sine value operation by a tangent trigonometric function to obtain a first conversion value. For example, the first conversion value can be expressed as tan(δ f -α s,lf ), where δ f represents the front wheel steering angle, α s,lf represents the first saturated sideslip angle.

[0096] The front wheel steering angle is added to the first saturated sideslip angle to obtain a sum value, and the sine value of the sum value is calculated by the tangent trigonometric function to obtain a second conversion value. For example, the second conversion value can be expressed as tan(δ f +α s,lf ).

[0097] In step 303112, the longitudinal vehicle speed and the first distance are multiplied to obtain a first constraint value, and the first conversion value, the longitudinal vehicle speed and the second wheelbase are multiplied to obtain a second constraint value.

[0098] Here, the longitudinal vehicle speed can be converted to the inverse to obtain the inverse of the longitudinal vehicle speed. The inverse of the longitudinal vehicle speed and the first distance from the center of mass of the vehicle to the front axle of the vehicle are multiplied to obtain the first constraint value. The inverse of the longitudinal vehicle speed, the first conversion value, and the second wheelbase of the rear wheels can be multiplied to obtain the second constraint value. For example, the first constraint value can be expressed as The second constraint value can be expressed as Among them, L f represents the first distance, v x Indicates the longitudinal speed, B r represents the second wheelbase, tan(δ f -α s,lf ) represents the first conversion value.

[0099] In step 303113, the sum of the first constraint value and the second constraint value is multiplied by the minimum yaw angular velocity to obtain a third constraint value, and the sum of the opposite of the third constraint value and the first conversion value is determined as the minimum center of mass sideslip angle of the left front wheel.

[0100] For example, the third constraint value can be expressed as The minimum center of mass sideslip angle can be expressed as

[0101] In step 303114, the second conversion value, the longitudinal vehicle speed and the second wheelbase are multiplied to obtain a fourth constraint value.

[0102] Here, the second conversion value, the inverse of half the longitudinal vehicle speed, and the second wheelbase of the rear wheels may be multiplied to obtain the fourth constraint value. For example, the fourth constraint value may be expressed as

[0103] In step 303115, the sum of the first constraint value and the fourth constraint value is multiplied by the maximum yaw angular velocity to obtain the fifth constraint value, and the inverse of the fifth constraint value and the second conversion value are summed to obtain the maximum center of mass sideslip angle of the left front wheel.

[0104] For example, the fifth constraint value can be expressed as The minimum center of mass sideslip angle can be expressed as

[0105] The first center of mass sideslip angle constraint information of the left front wheel can be determined by the following formula (5):

[0106]

[0107] in, Indicates the maximum center of mass sideslip angle of the left front wheel, Indicates the minimum sideslip angle of the left front wheel.

[0108] Continue to refer Figure 3C In step 30312, a second constraint analysis is performed based on the longitudinal vehicle speed, the first distance, the front wheel turning angle, the second saturated sideslip angle of the right front wheel and the second wheelbase to obtain the second center of mass sideslip angle constraint information.

[0109] Here, the second saturated slip angle is the slip angle corresponding to the tire of the right front wheel when the tire reaches its lateral force limit. The second constraint analysis process predicts the center of mass slip angle boundary of the right front wheel to obtain the second center of mass slip angle constraint information, and the second center of mass slip angle constraint information includes the minimum center of mass slip angle of the right front wheel and the maximum center of mass slip angle of the right front wheel.

[0110] For example, the second center of mass sideslip angle constraint information of the right front wheel can be determined by the following formula (6):

[0111]

[0112] in, Indicates the maximum center of mass sideslip angle of the right front wheel, Indicates the minimum center of mass slip angle of the right front wheel, L f represents the first distance, v x Indicates the longitudinal speed, B r represents the second wheelbase, δ f represents the front wheel steering angle, α s,rf represents the second saturated sideslip angle.

[0113] In step 30313, a third constraint analysis is performed based on the longitudinal vehicle speed, the second distance from the vehicle center of mass to the vehicle rear axle, the third saturated sideslip angle of the left rear wheel and the first wheelbase of the front wheel to obtain third center of mass sideslip angle constraint information.

[0114] Here, the third saturated slip angle is the slip angle corresponding to the tire of the left rear wheel when the tire reaches its lateral force limit. The third constraint analysis process predicts the center of mass slip angle boundary of the left rear wheel to obtain the third center of mass slip angle constraint information, and the third center of mass slip angle constraint information includes the minimum center of mass slip angle of the left rear wheel and the maximum center of mass slip angle of the left rear wheel.

[0115] For example, the third center of mass sideslip angle constraint information of the left rear wheel can be determined by the following formula (7):

[0116]

[0117] in, Indicates the maximum center of mass sideslip angle of the left rear wheel, Indicates the minimum center of mass slip angle of the left rear wheel, L r represents the second distance, v xIndicates the longitudinal speed, B f represents the first wheelbase, α s,lr represents the third saturated sideslip angle.

[0118] In step 30314, a fourth constraint analysis is performed based on the longitudinal vehicle speed, the second distance, the fourth saturated sideslip angle of the right rear wheel and the first wheelbase to obtain fourth center of mass sideslip angle constraint information.

[0119] Here, the fourth saturated slip angle is the slip angle corresponding to the tire of the right rear wheel when the tire reaches its lateral force limit. The fourth constraint analysis process predicts the center of mass slip angle boundary of the right rear wheel to obtain fourth center of mass slip angle constraint information, and the fourth center of mass slip angle constraint information includes the minimum center of mass slip angle of the right rear wheel and the maximum center of mass slip angle of the right rear wheel.

[0120] For example, the fourth center of mass sideslip angle constraint information of the right rear wheel can be determined by the following formula (8):

[0121]

[0122] in, Indicates the maximum center of mass sideslip angle of the right rear wheel, Indicates the minimum center of mass slip angle of the right rear wheel, L r represents the second distance, v x Indicates the longitudinal speed, B f represents the first wheelbase, α s,rr represents the fourth saturated sideslip angle.

[0123] In an embodiment of the present application, the center of mass sideslip angle constraint information of the vehicle in the first area is determined by the first center of mass sideslip angle constraint information of the left front wheel, the second center of mass sideslip angle constraint information of the right front wheel, the third center of mass sideslip angle constraint information of the left rear wheel, and the fourth center of mass sideslip angle constraint information of the right rear wheel. The center of mass sideslip angle constraint information of the vehicle in the first area can be comprehensively determined based on the center of mass sideslip angle constraint information of the front and rear four wheels, thereby improving the accuracy of the center of mass sideslip angle constraint information and ensuring the safety and stability of the vehicle during driving.

[0124] Continue to refer Figure 3B In step 3032, a boundary value prediction is performed based on the yaw rate constraint information and the center of mass sideslip angle constraint information to obtain the front wheel turning angle boundary value of the vehicle.

[0125] Here, the boundary value prediction is to predict the maximum front wheel turning angle for stable driving of the vehicle, and the boundary value of the front wheel turning angle is the maximum front wheel turning angle. The yaw rate constraint information and the center of mass sideslip angle constraint information are combined and simplified to obtain the boundary value of the front wheel turning angle.

[0126] For example, the boundary value of the front wheel turning angle can be determined by the following formula (9):

[0127]

[0128] Among them, δ f,max Indicates the maximum front wheel turning angle, L f Indicates the first distance from the center of mass of the vehicle to the front axle of the vehicle, L r Represents the second distance from the vehicle's center of mass to the vehicle's rear axle, α s,f represents the saturated sideslip angle of the front wheel, α s,r Represents the saturated sideslip angle of the rear wheel.

[0129] In step 3033, vehicle state constraint prediction is performed based on the yaw rate constraint information, the center of mass sideslip angle constraint information, and the front wheel turning angle boundary value to obtain lateral stability constraint information.

[0130] Here, the lateral stability boundary of the vehicle is predicted based on the yaw rate constraint information, the center of mass sideslip angle constraint information and the front wheel turning angle boundary value. The lateral stability constraint information is derived by combining the yaw rate constraint information, the center of mass sideslip angle constraint information and the front wheel turning angle boundary value and assuming that the saturated sideslip angles of the front and rear four wheels are equal.

[0131] For example, the lateral stability constraint information can be determined by the following formula (10):

[0132]

[0133] Among them, γ represents the yaw rate, β represents the sideslip angle of the center of mass, and λ 1 The expression is λ 2 The expression of tan(α s,r ), λ 3 The expression is λ 4 The expression is

[0134] The system state error formula can be used to transform the lateral stability constraint information. For example, the system state error formula can be determined by the following formula (11):

[0135]

[0136] Among them, e l represents the lateral position error, represents the lateral velocity error, represents the yaw error, represents the derivative of the yaw angle error, e s represents the longitudinal position error, represents the longitudinal velocity error, x represents the longitudinal position of the vehicle, y represents the lateral position of the vehicle, θ represents the yaw angle, and x d Indicates the longitudinal position of the vehicle matching point, y d represents the lateral position of the vehicle matching point, θ d represents the yaw angle of the vehicle matching point, k d represents the curvature of the vehicle trajectory, θ r represents the yaw angle of the projection point, v y represents the lateral speed, v x Indicates the longitudinal vehicle speed.

[0137] According to the lateral error in the system state error formula Assume that the center of mass sideslip angle β≈v y / v x , determine the lateral stability constraint information after the form transformation. For example, the lateral stability constraint information after the form transformation can be determined by the following formula (12):

[0138]

[0139] In an embodiment of the present application, vehicle state constraint prediction is performed based on yaw rate constraint information, center of mass sideslip angle constraint information, and front wheel turning angle boundary value to obtain lateral stability constraint information, thereby achieving a safer lateral stability boundary condition design, thereby ensuring the safety and stability of the vehicle during driving.

[0140] Continue to refer Figure 3A In step 304, a cost function of a model predictive controller of the vehicle is determined, and a control amount is predicted based on the lateral stability constraint information and the cost function to obtain a first control amount.

[0141] Here, the model predictive controller can be a model predictive controller of a nominal model, the nominal model is a model used to approximately describe the dynamic behavior of the actual vehicle system, and the model predictive controller is a controller used to optimize the vehicle's driving path and improve the vehicle's driving stability and accuracy. The cost function of the model predictive controller is a function used to optimize the control input, and the cost function can be determined by the following formula (13):

[0142]

[0143] Among them, J(k) represents the cost function, N p represents the prediction time domain, N c represents the control time domain, represents the output of the vehicle system at the i-th moment, represents the control increment of the front wheel steering angle at the i-th moment, Q represents the weight coefficient matrix of the state quantity of the model predictive controller, and R represents the weight coefficient matrix of the control increment of the model predictive controller.

[0144] The first control quantity is the optimal control quantity. The optimal control quantity is predicted for the lateral stability constraint information and the cost function of the model predictive controller to obtain the first control quantity.

[0145] In some embodiments, see Figure 3E , Figure 3E is a fifth flow chart of the vehicle data processing method provided in an embodiment of the present application, Figure 3A Step 304 shown can be performed by Figure 3E Steps 3041 to 3043 are implemented as described in detail below.

[0146] In step 3041, the lateral stability constraint information is discretized to obtain discrete lateral stability constraint information.

[0147] Here, the system state error formula is used to transform the lateral stability constraint information, and the transformed lateral stability constraint information is discretized to obtain discrete lateral stability constraint information. For example, the discrete lateral stability constraint information can be determined by the following formula (14):

[0148] -I s (k)≤V s (k)X(k)≤I s (k) (14)

[0149] Among them, X(k) represents the discrete vehicle system state quantity, I s The expression of (k) is V s The expression of (k) is

[0150] In step 3042, the control increment constraint information of the front wheel steering angle, the control amount constraint information of the front wheel steering angle and the vehicle system output amount constraint information in the model predictive controller are determined.

[0151] Here, the control increment constraint information includes the minimum front wheel steering angle control increment and the maximum front wheel steering angle control increment. For example, the control increment constraint information can be expressed as in represents the front wheel steering angle control increment at the i-th moment, represents the minimum front wheel steering angle control increment, Indicates the maximum front wheel steering angle control increment.

[0152] The control amount constraint information includes the minimum front wheel steering angle control amount and the maximum front wheel steering angle control amount. For example, the control amount constraint information can be expressed as in represents the front wheel steering angle control amount at the i-th moment, represents the minimum front wheel steering angle control amount, Indicates the maximum front wheel steering angle control amount.

[0153] The vehicle system output constraint information includes a minimum vehicle system output and a maximum vehicle system output. For example, the vehicle system output constraint information can be expressed as in represents the vehicle system output, represents the minimum vehicle system output, Indicates the maximum vehicle system output.

[0154] In step 3043, based on discrete lateral stability constraint information, control increment constraint information, control quantity constraint information and vehicle system output quantity constraint information, the minimum constraint value of the cost function is calculated, and the control quantity of the front wheel steering angle corresponding to the minimum constraint value is determined as the first control quantity.

[0155] Here, the input state vector of the vehicle system is obtained, and the front wheel angle control amount is added to the input state vector to obtain an updated input state vector. Based on the discrete lateral stability constraint information, control increment constraint information, control amount constraint information, vehicle system output constraint information and the updated input state vector, the cost function is constrained to obtain a cost constraint function. For example, the cost constraint function can be determined by the following formula (15):

[0156]

[0157] in, The expression is represents the updated input state vector, ξ k represents the input state vector of the vehicle system, u k-1 represents the front wheel steering angle control amount at the previous moment, represents the minimum front wheel steering angle control increment, Indicates the maximum front wheel steering angle control increment, represents the minimum front wheel steering angle control amount, Indicates the maximum front wheel steering angle control amount, represents the minimum vehicle system output, Represents the maximum vehicle system output, N c Represents the control time domain.

[0158] The minimum cost value of the cost function, that is, the minimum constraint value, is determined in combination with the cost constraint function, and the control amount of the front wheel steering angle corresponding to the minimum constraint value is determined as the optimal control amount, that is, the first control amount.

[0159] In an embodiment of the present application, a control amount prediction is performed based on lateral stability constraint information and a cost function to obtain a first control amount, thereby achieving vehicle trajectory tracking using a model predictive controller based on an improved lateral stability constraint state, thereby improving the vehicle trajectory tracking accuracy.

[0160] Continue to refer Figure 3A In step 305, the feedback compensation control amount of the vehicle is determined, and the first control amount and the feedback compensation control amount are summed to obtain a second control amount, and the second control amount and the reference trajectory are used to control the vehicle to track the trajectory.

[0161] Here, a proportional-integral-differential (PID) feedback controller is used to determine the feedback compensation control quantity based on the error between the model state quantity output by the model predictive controller and the actual state quantity of the vehicle. The first control quantity and the feedback compensation control quantity are summed to obtain the actual vehicle control quantity, that is, the second control quantity. The model predictive controller uses the second control quantity and the reference trajectory to control the vehicle to track the trajectory. For example, the second control quantity can be determined by the following formula (16):

[0162]

[0163] Where u(k) represents the second control variable, represents the first control quantity, u pid (k) represents the feedback compensation control amount.

[0164] In some embodiments, determining the feedback compensation control amount of the vehicle can be achieved by executing the following steps: determining a first vehicle state quantity corresponding to the minimum constraint value and a second vehicle state quantity corresponding to the actual state of the vehicle, the first vehicle state quantity including a first position of the vehicle's center of mass and a first yaw angle, and the second vehicle state quantity including a second position of the vehicle's center of mass and a second yaw angle; determining a distance error between the first position and the second position, and an angle error between the first yaw angle and the second yaw angle; weighting the angle error based on a preset weight coefficient to obtain a weighted angle error, and determining the sum of the weighted angle error and the distance error as a control error; adjusting the control error based on a preset proportional coefficient, a preset integral coefficient, a preset differential coefficient, and a preset sampling interval to obtain the feedback compensation control amount of the vehicle.

[0165] Here, the vehicle state quantity corresponding to the minimum constraint value of the cost function of the model predictive controller is determined as the optimal vehicle state quantity, that is, the first vehicle state quantity. The first vehicle state quantity includes the optimal position and the optimal yaw angle of the vehicle's center of mass, that is, the first position and the first yaw angle of the vehicle's center of mass. The second vehicle state quantity is the actual vehicle state quantity, and the second vehicle state quantity includes the actual vehicle center of mass position and the actual vehicle yaw angle, that is, the second position and the second yaw angle of the vehicle's center of mass.

[0166] The first position includes a first lateral position and a first longitudinal position of the center of mass of the vehicle, and the second position includes a second lateral position and a second longitudinal position of the center of mass of the vehicle. The first longitudinal position and the second longitudinal position are subtracted to obtain a longitudinal position difference, and the longitudinal position difference is multiplied by the sine value of the first yaw angle to obtain a longitudinal position error. The first lateral position and the second lateral position are subtracted to obtain a lateral position difference, and the lateral position difference is multiplied by the cosine value of the first yaw angle to obtain a lateral position error. The difference between the lateral position error and the longitudinal position error is determined as a distance error. The first yaw angle and the second yaw angle are subtracted to obtain an angle error. For example, the distance error and the angle error can be determined by the following formula (17):

[0167]

[0168] Among them, e d Represents the distance error, e θ represents the angle error, represents the first longitudinal position, represents the first horizontal position, x represents the second vertical position, y represents the second horizontal position, represents the first yaw angle, and θ represents the second yaw angle.

[0169] The angle error weighted by the preset weight coefficient is summed with the distance error to obtain the control error of the PID feedback controller. For example, the control error can be determined by the following formula (18):

[0170] e(k)=e d (k)+λe θ (k) (18)

[0171] Where e(k) represents the control error, λ represents the preset weight coefficient, and e d (k) represents the distance error, e θ (k) represents the angle error.

[0172] The preset proportional coefficient of the PID feedback controller is multiplied by the control error to obtain the first compensation control amount. The preset integral coefficient of the PID feedback controller is multiplied by the cumulative sum of the control errors to obtain the second compensation control amount. The product between the preset differential coefficient of the PID feedback controller and the control error increment is divided by the preset sampling interval to obtain the feedback compensation control amount. For example, the feedback compensation control amount can be determined by the following formula (19):

[0173]

[0174] Among them, u pid Denotes the feedback compensation control quantity, k p Indicates the preset proportionality factor, k i Indicates the preset integral coefficient, k d represents the preset differential coefficient, e(k) represents the control error, represents the cumulative sum of control errors, and e(k)-e(k-1) represents the control error increment.

[0175] In an embodiment of the present application, the control error is adjusted based on the preset proportional coefficient, preset integral coefficient, preset differential coefficient and preset sampling interval of the PID feedback controller to obtain the feedback compensation control amount of the vehicle, which can compensate for the deviation of the vehicle trajectory tracking, thereby improving the vehicle trajectory tracking accuracy.

[0176] In some embodiments, the vehicle data processing method provided by the embodiment of the present application can be applied in the field of unmanned driving technology. The unmanned driving terminal server determines the yaw rate and the sideslip angle of the center of mass of the vehicle during driving, constructs a phase plane diagram based on the yaw rate and the sideslip angle of the center of mass, and then performs a dynamic stability analysis on the vehicle based on the phase plane diagram to obtain the first area in the phase plane diagram, and then performs a vehicle state constraint prediction based on the first area to obtain lateral stability constraint information, and realizes the phase plane method analysis through the lateral stability related parameters, and designs a safer lateral stability boundary condition for the model predictive controller, thereby ensuring the safety and stability of the vehicle during driving, and then determines the cost function of the model predictive controller of the vehicle, and predicts the control amount based on the lateral stability constraint information and the cost function to obtain the first control amount, determines the feedback compensation control amount of the vehicle, and sums the first control amount and the feedback compensation control amount to obtain the second control amount, and uses the second control amount and the reference trajectory to control the vehicle to track the trajectory. In this way, it is realized that the vehicle trajectory tracking is performed using the model predictive controller under the improved lateral stability constraint state, and the feedback compensation control amount is introduced to compensate for the deviation of the vehicle trajectory tracking, thereby improving the trajectory tracking accuracy of the vehicle.

[0177] Below, the vehicle data processing method provided in an embodiment of the present application will be described in an exemplary application in an autonomous driving vehicle trajectory tracking scenario.

[0178] Among the related technologies, the model predictive control method is a traditional method in the field of autonomous driving vehicle trajectory tracking. This method uses a vehicle model to predict the vehicle state within a limited prediction time domain, and calculates the optimal control input by solving the optimization problem, so that the vehicle can track the desired trajectory. It can handle the constraints and nonlinear characteristics of multi-input and multi-output systems, and provides a flexible and efficient control strategy.

[0179] When the vehicle is in extreme working conditions, such as when the front wheel angular swing affects the vehicle's yaw stability during high-speed driving, it is necessary to accurately evaluate the vehicle's lateral stability constraints to ensure the vehicle's safety and stability in various complex environments. In addition, in the application of model predictive controllers, due to the uncertainty of the vehicle model used to construct the optimization problem, such as changes in vehicle parameters, external disturbances, and errors in modeling parameters, the final optimization control result causes errors in the vehicle's trajectory tracking, which seriously affects the vehicle's trajectory tracking accuracy.

[0180] In view of the problems existing in the related art, the embodiment of the present application proposes a vehicle data processing method, which includes the following improvements compared with the related art:

[0181] By using the phase plane method of the sideslip angle-yaw rate of the center of mass, the lateral stability constraint of the vehicle under extreme working conditions is obtained. A composite control system combining a nominal model predictive controller and a proportional-integration-differential (PID) feedback controller is constructed to perform convergence control on the deviation between the predicted state of the nominal model predictive controller and the actual vehicle state. The nominal model predictive controller is used to track the trajectory of the autonomous driving vehicle under the improved lateral stability constraint state, and the control input is dynamically adjusted. The generated optimal control input is used to guide the vehicle to drive along the predetermined trajectory. The PID feedback controller is introduced to compensate for the deviation between the actual vehicle model and the nominal model, eliminate the steady-state error of the vehicle trajectory tracking, and thus improve the vehicle's trajectory tracking accuracy.

[0182] The input of the vehicle trajectory tracking controller is the reference trajectory of the upstream planning module and the current actual state of the vehicle. The trajectory tracking control can be achieved through the nominal model predictive controller based on the PID feedback controller. For example, refer to Figure 4 , Figure 4 is a flow chart of a vehicle data processing method provided in an embodiment of the present application. Figure 4The vehicle data processing process provided in the embodiment of the present application is explained.

[0183] First, the nominal model predictive controller is designed. The nominal model predictive controller 401 is composed of a state estimation module 402, a stability constraint module 403, a cost function calculation module 404, and an actual vehicle model module 405. The input of the nominal model predictive controller 401 is the reference trajectory, and the output is the nominal model control quantity (the first control quantity in the above embodiment). The nominal model control quantity is used to control the nominal vehicle model (the nominal model in the above embodiment) to obtain the nominal model state quantity (the first vehicle state quantity in the above embodiment). The difference between the nominal model state quantity and the actual vehicle state quantity (the second vehicle state quantity in the above embodiment) is used as the state error (the distance error and the angle error in the above embodiment), and the state error is input into the PID feedback controller 406. The output of the PID feedback controller 406 is the feedback control quantity. The feedback control quantity and the nominal model control quantity are summed to obtain the composite control quantity (the second control quantity in the above embodiment), which is the actual vehicle control quantity. The nominal model predictive controller is implemented to solve the optimal control quantity based on the reference trajectory and the nominal model state quantity.

[0184] In the state estimation module 402, the lateral stability constraint of the vehicle is determined by the state quantity related to the lateral stability of the autonomous driving vehicle. In the vehicle system, the center of mass sideslip angle is a physical parameter that can describe the degree of deviation of the vehicle's trajectory tracking. When the center of mass sideslip angle is small, the magnitude of the yaw angular velocity will determine the turning ability of the vehicle. Therefore, the stability and turning performance of the vehicle are characterized by the center of mass sideslip angle and the yaw angular velocity. Considering that the yaw angular velocity and lateral acceleration of the vehicle can be directly observed by sensors, while the center of mass sideslip angle is a physical quantity that is difficult to observe, it is necessary to design a state estimator to observe the center of mass sideslip angle. For example, the center of mass sideslip angle can be determined by the following formula (1):

[0185] β=arctan(v y / v x ), (1)

[0186] Where β is the sideslip angle of the center of mass, v y represents the lateral speed, v x Indicates the longitudinal vehicle speed.

[0187] The extended Kalman filter method can be used to observe the center of mass sideslip angle. Specifically, the differential equations of the lateral and yaw of the vehicle system can be obtained according to the vehicle dynamics model. After simplification, the differential equations of the center of mass sideslip angle and yaw angular velocity can be obtained. For example, the differential equations of the center of mass sideslip angle and yaw angular velocity can be determined by the following formula (2):

[0188]

[0189] in, represents the differential of the sideslip angle of the center of mass, represents the differential of the yaw rate, δ f represents the front wheel turning angle, m represents the vehicle mass, F lf Indicates the longitudinal force on the front wheel, F cf Indicates the lateral force on the front wheel, F cr Indicates the lateral force on the rear wheel, I z represents the moment of inertia, L f Indicates the distance from the center of mass of the vehicle to the front axle of the vehicle, L r Represents the distance from the vehicle's center of mass to the vehicle's rear axle.

[0190] The yaw rate and lateral acceleration can be directly observed by sensors. For example, the lateral acceleration can be determined by the following formula (3):

[0191]

[0192] Among them, a y represents the lateral acceleration, δ f represents the front wheel turning angle, m represents the vehicle mass, and F lf Indicates the longitudinal force on the front wheel, F cf Indicates the lateral force on the front wheel, F cr Indicates the lateral force on the rear wheel.

[0193] The center of mass slip angle can represent the degree of lateral slip of the vehicle. A larger center of mass slip angle means that the vehicle has a larger lateral slip, which will make the vehicle difficult to control. Therefore, the maximum size of the center of mass slip angle determines the maximum lateral deviation angle that the vehicle can safely control. For example, continue to refer to Figure 4 , in the stability constraint module 403, the vehicle dynamic stability analysis is performed. By constructing a center of mass sideslip angle-yaw rate phase plane diagram, the dynamic trajectory formed by the change of vehicle state parameters can be observed. When the vehicle performs a steering operation during driving, the lateral force of the tire will increase accordingly. If the steering angle increases to a certain value, the lateral force of the tire will enter the nonlinear saturation region, and the vehicle is very likely to become unstable at this time. When the lateral force of the front wheels of the vehicle reaches a saturated state, the vehicle may lose its steering ability and thus fail to track the predetermined trajectory. When the lateral force of the rear wheels of the vehicle is saturated, the vehicle is prone to unstable behaviors such as tail swinging.

[0194] The lateral and yaw motion system of a vehicle is a second-order dynamic system. By observing the vehicle equilibrium point on the phase plane diagram and the phase trajectory that changes with the vehicle state variables, the stable domain and unstable domain of the vehicle during driving are analyzed. Specifically, based on the principle of vehicle dynamics, a differential equation describing the lateral and yaw motion of the vehicle is established. Using the vehicle state variables that change with time, the differential equation is analyzed, and the phase trajectory of the vehicle under different vehicle state variables is plotted. In the phase plane diagram, it is first necessary to determine the equilibrium point of the system, that is, the stable state of the vehicle when there is no external disturbance. The equilibrium point usually corresponds to a zero sideslip angle at the center of mass and a zero yaw rate, indicating that the vehicle is moving straight and does not yaw. If the phase trajectory converges toward the equilibrium point, it indicates that the vehicle is in a stable state, and the area where the phase trajectory is located is the stable domain. If the phase trajectory is far away from the equilibrium point, it indicates that the vehicle is not in a stable state, and the area where the phase trajectory is located is the unstable domain.

[0195] Based on the stability domain, the state constraints of the vehicle system are performed to design the lateral stability boundary of the vehicle. Assuming that the longitudinal vehicle speed and the road adhesion coefficient remain constant, the yaw rate boundary of the vehicle in the stability domain is determined by the longitudinal vehicle speed and the road adhesion coefficient. For example, the yaw rate boundary can be determined by the following formula (4):

[0196]

[0197] Among them, γ max represents the maximum yaw rate, γ min represents the minimum yaw rate, μ represents the road adhesion coefficient, and v x represents the longitudinal vehicle speed and g represents the acceleration due to gravity.

[0198] Determine the center of mass side slip angle boundary of the front wheel and the center of mass side slip angle boundary of the rear wheel in the stability domain of the vehicle. For example, the center of mass side slip angle boundary of the left front wheel can be determined by the following formula (5):

[0199]

[0200] in, Indicates the maximum center of mass sideslip angle of the left front wheel, Indicates the minimum center of mass slip angle of the left front wheel, L f Represents the distance from the center of mass of the vehicle to the front axle of the vehicle, v x Indicates the longitudinal speed, B r represents the track width of the rear wheels, δ f represents the front wheel steering angle, α s,ls Indicates the saturated slip angle of the left front wheel.

[0201] For example, the center of mass side slip angle boundary of the right front wheel can be determined by the following formula (6):

[0202]

[0203] in, Indicates the maximum center of mass sideslip angle of the right front wheel, represents the minimum center of mass sideslip angle of the right front wheel, α s,rf Indicates the saturated slip angle of the right front wheel.

[0204] For example, the center of mass side slip angle boundary of the left rear wheel can be determined by the following formula (7):

[0205]

[0206] in, Indicates the maximum center of mass sideslip angle of the left rear wheel, Indicates the minimum center of mass slip angle of the left rear wheel, L r Indicates the distance from the vehicle's center of mass to the vehicle's rear axle, B f represents the track width of the front wheels, α s,lr Indicates the saturated slip angle of the left rear wheel.

[0207] For example, the center of mass side slip angle boundary of the right rear wheel can be determined by the following formula (8):

[0208]

[0209] in, Indicates the maximum center of mass sideslip angle of the right rear wheel, represents the minimum center of mass sideslip angle of the right rear wheel, α s,rr Indicates the saturated slip angle of the right rear wheel.

[0210] Based on the yaw rate boundary, the center of mass slip angle boundary of the front wheel, and the center of mass slip angle boundary of the rear wheel, the maximum steady-state front wheel turning angle boundary is predicted. Formulas (4) to (8) are combined and simplified to obtain the boundary value of the front wheel turning angle. For example, the boundary value of the front wheel turning angle can be determined by the following formula (9):

[0211]

[0212] Among them, δ f,max Indicates the maximum front wheel turning angle, L f Indicates the distance from the center of mass of the vehicle to the front axle of the vehicle, L r Represents the distance from the vehicle's center of mass to the vehicle's rear axle, α s,f represents the saturated sideslip angle of the front wheel, α s,r Represents the saturated sideslip angle of the rear wheel.

[0213] Based on the yaw rate boundary, the center of mass slip angle boundary determined by the front and rear wheels, and the maximum steady-state front wheel turning angle boundary, the vehicle system is constrained in state, and the lateral stability boundary of the vehicle during driving is designed to ensure the lateral stability of the vehicle during driving. Formulas (4) to (9) are combined, and the saturated slip angles of the front and rear four wheels are assumed to be equal, and simplified to obtain the lateral stability boundary. For example, the lateral stability boundary can be determined by the following formula (10):

[0214]

[0215] Among them, γ represents the yaw rate, β represents the sideslip angle of the center of mass, and λ 1 The expression is λ 2 The expression of tan(α s,r ), λ 3 The expression is λ 4 The expression is

[0216] The system state error formula can be used to transform the lateral stability boundary. For example, the system state error formula can be determined by the following formula (11):

[0217]

[0218] Among them, e l represents the lateral position error, represents the lateral velocity error, represents the yaw error, represents the derivative of the yaw angle error, e s represents the longitudinal position error, represents the longitudinal velocity error, x represents the longitudinal position of the vehicle, y represents the lateral position of the vehicle, θ represents the yaw angle, and x d Indicates the longitudinal position of the vehicle matching point, y d represents the lateral position of the vehicle matching point, θ d represents the yaw angle of the vehicle matching point, k d represents the curvature of the vehicle trajectory, θ r Represents the yaw angle of the projection point.

[0219] According to the lateral error in the system state error formula Assume that the center of mass sideslip angle β≈v y / v x , determine the lateral stability boundary after the form transformation. For example, the lateral stability boundary after the form transformation can be determined by the following formula (12):

[0220]

[0221] The lateral stability boundary after the form transformation is discretized to obtain a discrete lateral stability boundary. For example, the discrete lateral stability boundary can be determined by the following formula (14):

[0222] -I s (k)≤V s (k)X(k)≤I s (k) (14)

[0223] Among them, X(k) represents the discrete vehicle system state quantity, I s The expression of (k) is V s The expression of (k) is

[0224] In the embodiment of the present application, a lateral stability boundary is designed in the nominal model predictive controller to constrain the control quantity and state quantity in the lateral controller, thereby ensuring the lateral stability of the vehicle during driving.

[0225] Continue to refer Figure 4 In the cost function calculation module 404, the cost function of the nominal model predictive controller is designed. For example, the cost function can be determined by the following formula (13):

[0226]

[0227] Among them, J(k) represents the cost function, N p represents the prediction time domain, N c represents the control time domain, represents the output of the vehicle system at the i-th moment, represents the control increment of the front wheel steering angle at the i-th moment, Q represents the weight coefficient matrix of the state quantity of the model predictive controller, and R represents the weight coefficient matrix of the control increment of the model predictive controller.

[0228] Based on the discrete lateral stability boundary, the cost function in the nominal model predictive controller is constrained to obtain the cost constraint function, and the cost constraint function is solved to obtain the optimal control quantity. For example, the cost constraint function can be determined by the following formula (15):

[0229]

[0230] in, The expression is represents the updated input state vector, ξ k represents the input state vector of the vehicle system, u k-1represents the front wheel steering angle control amount at the previous moment, represents the minimum front wheel steering angle control increment, Indicates the maximum front wheel steering angle control increment, represents the minimum front wheel steering angle control amount, Indicates the maximum front wheel steering angle control amount, represents the minimum vehicle system output, Indicates the maximum vehicle system output.

[0231] In order to achieve lateral control of the vehicle, a PID feedback controller is designed based on the nominal model state quantity and the actual vehicle state quantity to perform state feedback control on the external disturbance of the vehicle system and the error of the nominal model predictive controller. For example, the control error of the PID feedback controller can be determined by the following formula (18):

[0232] e(k)=e d (k)+λe θ (k) (18)

[0233] Among them, e represents the control error, θ represents the weight coefficient, and e d and e θ The expression of is shown in the following formula (17):

[0234]

[0235] Among them, the expression of the nominal model state quantity is represents the longitudinal position of the vehicle predicted by the nominal model, represents the lateral position of the vehicle predicted by the nominal model, represents the yaw angle predicted by the nominal model. The actual vehicle state is expressed as [x, y, θ] T , x represents the actual longitudinal position of the vehicle, y represents the actual lateral position of the vehicle, and θ represents the actual yaw angle.

[0236] The feedback compensation control quantity formula of the PID feedback controller is designed. For example, the feedback compensation control quantity formula can be determined by the following formula (19):

[0237]

[0238] Among them, u pid Denotes the feedback compensation control quantity, k p represents the proportional coefficient of the PID feedback controller, k i Indicates the integral coefficient of the PID feedback controller, k d Represents the differential coefficient of the PID feedback controller.

[0239] Finally, the optimal control amount and the feedback compensation control amount are summed to obtain the actual vehicle control amount. For example, the actual vehicle control amount can be determined by the following formula (16):

[0240]

[0241] Among them, u represents the actual vehicle control amount, represents the optimal control quantity, u pid Represents the feedback compensation control amount.

[0242] Continue to refer Figure 4 In the actual vehicle model module 405, the actual vehicle control amount and the reference trajectory are used to track the trajectory of the autonomous driving vehicle.

[0243] In the above-mentioned application scenario of autonomous vehicle trajectory tracking, by considering the lateral stability constraint of the vehicle under extreme working conditions, the extended Kalman filter method is used to estimate the state quantity that is difficult to obtain in the vehicle, and the phase plane method is analyzed through the lateral stability related parameters to design a safer lateral stability boundary condition for the model predictive controller. The composite control system combining the nominal model predictive controller and the PID feedback controller effectively eliminates the steady-state error between the nominal vehicle model and the actual vehicle model, thereby improving the trajectory tracking accuracy of the autonomous vehicle.

[0244] Next, the exemplary structure of the vehicle data processing device 455 provided in the embodiment of the present application implemented as a software module is further described. In some embodiments, as shown in FIG3 , the software modules stored in the vehicle data processing device 455 of the memory 450 may include: a first determination module 4551, used to determine the yaw rate and the sideslip angle of the center of mass during the driving process of the vehicle, and to construct a phase plane diagram based on the yaw rate and the sideslip angle of the center of mass; a data analysis module 4552, used to perform a dynamic stability analysis on the vehicle based on the phase plane diagram to obtain a first area in the phase plane diagram; a first prediction module 4553, used to perform a vehicle state constraint prediction based on the first area to obtain lateral stability constraint information; a second prediction module 4554, used to determine the cost function of the model predictive controller of the vehicle, and to perform a control amount prediction based on the lateral stability constraint information and the cost function to obtain a first control amount; a second determination module 4555, used to determine the feedback compensation control amount of the vehicle, and to sum the first control amount and the feedback compensation control amount to obtain a second control amount, and to control the vehicle to track the trajectory using the second control amount and the reference trajectory.

[0245] In some embodiments, the data analysis module 4552 is also used to determine the equilibrium point and N phase trajectories of the vehicle in the phase plane diagram, where N is a positive integer; determine the target phase trajectory that converges to the equilibrium point from the N phase trajectories; and determine the area in the phase plane diagram where the target phase trajectory is located as the first area.

[0246] In some embodiments, the first prediction module 4553 is also used to determine the yaw rate constraint information of the vehicle in the first area, and determine the center of mass sideslip angle constraint information of the vehicle in the first area; perform boundary value prediction based on the yaw rate constraint information and the center of mass sideslip angle constraint information to obtain the front wheel angle boundary value of the vehicle; perform vehicle state constraint prediction based on the yaw rate constraint information, the center of mass sideslip angle constraint information and the front wheel angle boundary value to obtain lateral stability constraint information.

[0247] In some embodiments, the yaw rate constraint information includes a minimum yaw rate and a maximum yaw rate. The first prediction module 4553 is further used to determine the longitudinal vehicle speed and road adhesion coefficient of the vehicle in the first area; multiply the longitudinal vehicle speed, the road adhesion coefficient and the acceleration of gravity to obtain the maximum yaw rate; and determine the inverse of the maximum yaw rate as the minimum yaw rate.

[0248] In some embodiments, the center of mass sideslip angle constraint information includes first center of mass sideslip angle constraint information of the left front wheel, second center of mass sideslip angle constraint information of the right front wheel, third center of mass sideslip angle constraint information of the left rear wheel, and fourth center of mass sideslip angle constraint information of the right rear wheel. The first prediction module 4553 is further used to perform a first constraint analysis based on the longitudinal speed of the vehicle in the first area, a first distance from the center of mass of the vehicle to the front axle of the vehicle, the front wheel turning angle, the first saturated sideslip angle of the left front wheel, and the second wheel track of the rear wheel to obtain the first center of mass sideslip angle constraint information; perform a second constraint analysis based on the longitudinal speed, the first distance, the front wheel turning angle, the second saturated sideslip angle of the right front wheel, and the second wheel track to obtain the second center of mass sideslip angle constraint information; perform a third constraint analysis based on the longitudinal speed, the second distance from the center of mass of the vehicle to the rear axle of the vehicle, the third saturated sideslip angle of the left rear wheel, and the first wheel track to obtain the third center of mass sideslip angle constraint information; perform a fourth constraint analysis based on the longitudinal speed, the second distance, the fourth saturated sideslip angle of the right rear wheel, and the first wheel track to obtain the fourth center of mass sideslip angle constraint information.

[0249] In some embodiments, the first center of mass sideslip angle constraint information includes the minimum center of mass sideslip angle of the left front wheel and the maximum center of mass sideslip angle of the left front wheel. The first prediction module 4553 is further used to perform a tangent trigonometric function conversion on the difference between the front wheel steering angle and the first saturated sideslip angle to obtain a first conversion value; perform a tangent trigonometric function conversion on the sum of the front wheel steering angle and the first saturated sideslip angle to obtain a second conversion value; multiply the longitudinal vehicle speed and the first distance to obtain a first constraint value, and multiply the first conversion value, the longitudinal vehicle speed and the second wheelbase to obtain a second constraint value. second constraint value; multiplying the sum of the first constraint value and the second constraint value by the minimum yaw angular velocity to obtain a third constraint value, and determining the sum of the opposite number of the third constraint value and the first conversion value as the minimum center of mass sideslip angle of the left front wheel; multiplying the second conversion value, the longitudinal vehicle speed, and the second wheelbase to obtain a fourth constraint value; multiplying the sum of the first constraint value and the fourth constraint value by the maximum yaw angular velocity to obtain a fifth constraint value, and summing the opposite number of the fifth constraint value and the second conversion value to obtain the maximum center of mass sideslip angle of the left front wheel.

[0250] In some embodiments, the second prediction module 4554 is also used to discretize the lateral stability constraint information to obtain discrete lateral stability constraint information; determine the control increment constraint information of the front wheel angle, the control amount constraint information of the front wheel angle and the vehicle system output constraint information in the model predictive controller; calculate the minimum constraint value of the cost function based on the discrete lateral stability constraint information, the control increment constraint information, the control amount constraint information and the vehicle system output constraint information, and determine the control amount of the front wheel angle corresponding to the minimum constraint value as the first control amount.

[0251] In some embodiments, the second determination module 4555 is also used to determine a first vehicle state quantity corresponding to the minimum constraint value and a second vehicle state quantity corresponding to the actual state of the vehicle, the first vehicle state quantity including a first position of the vehicle's center of mass and a first yaw angle, and the second vehicle state quantity including a second position of the vehicle's center of mass and a second yaw angle; determine the distance error between the first position and the second position, and the angle error between the first yaw angle and the second yaw angle; weight the angle error based on a preset weight coefficient to obtain a weighted angle error, and determine the sum of the weighted angle error and the distance error as the control error; adjust the control error based on a preset proportional coefficient, a preset integral coefficient, a preset differential coefficient and a preset sampling interval to obtain the feedback compensation control quantity of the vehicle.

[0252] The embodiment of the present application provides a computer program product, which includes computer executable instructions or computer programs, and the computer executable instructions or computer programs are stored in a computer readable storage medium. The processor of the electronic device reads the computer executable instructions or computer programs from the computer readable storage medium, and the processor executes the computer executable instructions or computer programs, so that the electronic device executes the vehicle data processing method described in the embodiment of the present application.

[0253] The present application embodiment provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions or computer programs are stored. When the computer-executable instructions or computer programs are executed by a processor, the processor will be caused to execute the vehicle data processing method provided by the present application embodiment, for example, Figure 3A The vehicle data processing method is shown.

[0254] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface storage, optical disk, or CD-ROM; or it may be various devices including one or any combination of the above memories.

[0255] In some embodiments, computer executable instructions may be in the form of a program, software, software module, script or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine or other unit suitable for use in a computing environment.

[0256] As an example, computer-executable instructions may, but need not, correspond to a file in a file system, may be stored as part of a file that stores other programs or data, such as in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or code portions).

[0257] As an example, computer executable instructions may be deployed to be executed on one electronic device, or on multiple electronic devices located at one site, or on multiple electronic devices distributed at multiple sites and interconnected by a communication network.

[0258] The above is only an embodiment of the present application and is not intended to limit the protection scope of the present application. Any modifications, equivalent substitutions and improvements made within the spirit and scope of the present application are included in the protection scope of the present application.

Claims

1. A vehicle data processing method, characterized in that: The method comprises: Determine the yaw rate and the sideslip angle of the center of mass during the vehicle's driving process, and construct a phase plane diagram based on the yaw rate and the sideslip angle of the center of mass; Performing a dynamic stability analysis on the vehicle based on the phase plane diagram to obtain a first region in the phase plane diagram; Performing vehicle state constraint prediction based on the first region to obtain lateral stability constraint information; Determining a cost function of a model predictive controller of the vehicle, and performing control amount prediction based on the lateral stability constraint information and the cost function to obtain a first control amount; A feedback compensation control amount of the vehicle is determined, and the first control amount and the feedback compensation control amount are summed to obtain a second control amount, and the vehicle is controlled to track a trajectory using the second control amount and a reference trajectory.

2. The method according to claim 1, characterized in that The performing of dynamic stability analysis on the vehicle based on the phase plane diagram to obtain a first region in the phase plane diagram comprises: Determine an equilibrium point and N phase trajectories of the vehicle in the phase plane diagram, where N is a positive integer; Determining a target phase trajectory that converges to the equilibrium point from the N phase trajectories; The area where the target phase trajectory in the phase plane diagram is located is determined as the first area.

3. The method according to claim 1, characterized in that The performing vehicle state constraint prediction based on the first region to obtain lateral stability constraint information includes: Determining yaw rate constraint information of the vehicle in the first area, and determining center of mass sideslip angle constraint information of the vehicle in the first area; Based on the yaw rate constraint information and the center of mass sideslip angle constraint information, a boundary value prediction is performed to obtain a front wheel turning angle boundary value of the vehicle; The vehicle state constraint prediction is performed based on the yaw rate constraint information, the center of mass sideslip angle constraint information and the front wheel turning angle boundary value to obtain lateral stability constraint information.

4. The method according to claim 3, characterized in that The yaw rate constraint information includes a minimum yaw rate and a maximum yaw rate. The determining the yaw rate constraint information of the vehicle in the first area includes: determining a longitudinal speed and a road adhesion coefficient of the vehicle in the first area; Multiplying the longitudinal vehicle speed, the road adhesion coefficient and the gravitational acceleration to obtain a maximum yaw angular velocity; The inverse of the maximum yaw angular velocity is determined as the minimum yaw angular velocity.

5. The method according to claim 4, characterized in that The center of mass sideslip angle constraint information includes first center of mass sideslip angle constraint information of the left front wheel, second center of mass sideslip angle constraint information of the right front wheel, third center of mass sideslip angle constraint information of the left rear wheel, and fourth center of mass sideslip angle constraint information of the right rear wheel. The determining of the center of mass sideslip angle constraint information of the vehicle in the first area includes: Performing a first constraint analysis process based on the longitudinal speed of the vehicle in the first area, a first distance from the center of mass of the vehicle to the front axle of the vehicle, a front wheel turning angle, a first saturated sideslip angle of the left front wheel, and a second wheelbase of the rear wheels, to obtain the first center of mass sideslip angle constraint information; Performing a second constraint analysis process based on the longitudinal vehicle speed, the first distance, the front wheel turning angle, the second saturated sideslip angle of the right front wheel, and the second wheelbase to obtain the second center of mass sideslip angle constraint information; Performing a third constraint analysis process based on the longitudinal vehicle speed, the second distance from the vehicle center of mass to the vehicle rear axle, the third saturated sideslip angle of the left rear wheel and the first wheelbase of the front wheel to obtain the third center of mass sideslip angle constraint information; A fourth constraint analysis process is performed based on the longitudinal vehicle speed, the second distance, a fourth saturated sideslip angle of the right rear wheel and the first wheelbase to obtain the fourth center of mass sideslip angle constraint information.

6. The method according to claim 5, characterized in that The first center of mass sideslip angle constraint information includes a minimum center of mass sideslip angle of a left front wheel and a maximum center of mass sideslip angle of a left front wheel. The first constraint analysis processing is performed based on the longitudinal speed of the vehicle in the first area, a first distance from the center of mass of the vehicle to the front axle of the vehicle, a front wheel turning angle, a first saturated sideslip angle of the left front wheel, and a second wheelbase of the rear wheels to obtain the first center of mass sideslip angle constraint information, including: Performing tangent trigonometric function conversion on the difference between the front wheel steering angle and the first saturated sideslip angle to obtain a first conversion value; performing tangent trigonometric function conversion on the sum of the front wheel steering angle and the first saturated sideslip angle to obtain a second conversion value; multiplying the longitudinal vehicle speed and the first distance to obtain a first constraint value, and multiplying the first conversion value, the longitudinal vehicle speed and the second wheelbase to obtain a second constraint value; Multiplying the sum of the first constraint value and the second constraint value by the minimum yaw angular velocity to obtain a third constraint value, and determining the sum of the opposite number of the third constraint value and the first conversion value as the minimum center of mass sideslip angle of the left front wheel; multiplying the second converted value, the longitudinal vehicle speed and the second wheelbase to obtain a fourth constraint value; The sum of the first constraint value and the fourth constraint value is multiplied by the maximum yaw angular velocity to obtain a fifth constraint value, and the inverse of the fifth constraint value and the second conversion value are summed to obtain a maximum center of mass sideslip angle of the left front wheel.

7. The method according to claim 3, characterized in that The predicting of the control amount based on the lateral stability constraint information and the cost function to obtain the first control amount includes: discretizing the lateral stability constraint information to obtain discrete lateral stability constraint information; Determining control increment constraint information of the front wheel steering angle, control amount constraint information of the front wheel steering angle and vehicle system output constraint information in the model predictive controller; Based on the discrete lateral stability constraint information, the control increment constraint information, the control amount constraint information and the vehicle system output constraint information, the minimum constraint value of the cost function is calculated, and the control amount of the front wheel steering angle corresponding to the minimum constraint value is determined as the first control amount.

8. The method according to any one of claims 1 to 7, characterized in that: The determining of the feedback compensation control amount of the vehicle includes: Determine a first vehicle state quantity corresponding to the minimum constraint value and a second vehicle state quantity corresponding to the actual state of the vehicle, wherein the first vehicle state quantity includes a first position of the vehicle center of mass and a first yaw angle, and the second vehicle state quantity includes a second position of the vehicle center of mass and a second yaw angle; determining a distance error between the first position and the second position, and an angular error between the first yaw angle and the second yaw angle; Performing weighted processing on the angle error based on a preset weight coefficient to obtain a weighted angle error, and determining the sum of the weighted angle error and the distance error as a control error; Based on a preset proportional coefficient, a preset integral coefficient, a preset differential coefficient and a preset sampling interval, the control error is adjusted to obtain a feedback compensation control amount of the vehicle.

9. A vehicle data processing device, characterized in that: The device comprises: A first determination module is used to determine the yaw rate and the sideslip angle of the center of mass during the vehicle's driving process, and to construct a phase plane diagram based on the yaw rate and the sideslip angle of the center of mass; A data analysis module, configured to perform a dynamic stability analysis on the vehicle based on the phase plane diagram to obtain a first region in the phase plane diagram; A first prediction module, used for predicting vehicle state constraints based on the first area to obtain lateral stability constraint information; a second prediction module, used to determine a cost function of a model predictive controller of the vehicle, and to predict a control amount based on the lateral stability constraint information and the cost function to obtain a first control amount; The second determination module is used to determine the feedback compensation control amount of the vehicle, and sum the first control amount and the feedback compensation control amount to obtain a second control amount, and use the second control amount and a reference trajectory to control the vehicle to perform trajectory tracking.

10. An electronic device, characterized in that: The electronic device comprises: A memory for storing computer executable instructions or computer programs; The processor is used to implement the vehicle data processing method according to any one of claims 1 to 8 when executing the computer executable instructions or computer programs stored in the memory.

11. A computer-readable storage medium storing computer-executable instructions or a computer program, characterized in that: When the computer executable instructions or computer program are executed by a processor, the vehicle data processing method according to any one of claims 1 to 8 is implemented.

Images