Intelligent vehicle attachment coefficient online estimation method fusing real vehicle data

Abstract

The invention provides an intelligent vehicle adhesion coefficient online estimation method fusing real vehicle data. The method comprises the following steps: constructing a vehicle dynamics model containing a vehicle tire force equation; the method comprises the following steps: acquiring a real vehicle operation data sequence of a vehicle, dividing input data and output data into past time periods and future time periods, and arranging the time periods according to a time sequence to form a data time sequence matrix; on the basis of the data time sequence matrix, an optimization problem fitting vehicle dynamics model is constructed with the purpose of minimizing the error between model output and actual observation data, and a vehicle dynamics model based on real vehicle operation data is obtained by solving the optimization problem; and based on the vehicle dynamics model obtained through fitting, real-time estimation of the road adhesion coefficient is realized through an unscented Kalman filtering algorithm. By fusing the vehicle dynamic model represented based on real vehicle data, the mismatch degree of the vehicle model in the existing adhesion coefficient estimation algorithm based on the dynamic model is effectively reduced, and the estimation precision of the road adhesion coefficient is improved.

Description

Technical Field

[0001] The present application relates to the technical field of road adhesion coefficient observation, and in particular to an online estimation method for the adhesion coefficient of an intelligent vehicle integrating real vehicle data. Background Art

[0002] The intelligent chassis has the ability to sense the interaction between tires and the road. The road adhesion coefficient is a time-varying variable related to the tire's motion state. Real-time, online, and accurate estimation of the road adhesion coefficient is essential for ensuring chassis stability, driving safety, and fully utilizing the chassis's motion limits. In actual use, users are often more concerned with its maximum value, or peak adhesion coefficient. Therefore, the road adhesion coefficient parameter estimated in this invention is the peak adhesion coefficient value under current road conditions.

[0003] Road adhesion coefficient estimation is of great significance for vehicle dynamics control and safe operation. Current methods for estimating the road adhesion coefficient can be categorized into two types: signal-based and model-based. Patent CN 118182485 A proposes a PID observer based on a vehicle tire model to achieve accurate online estimation of the road adhesion coefficient. However, the accuracy of the model-based adhesion coefficient observation algorithm is closely related to the accuracy of the tire model, and mismatches in the dynamics model significantly reduce the algorithm's estimation accuracy. Patent CN 112329533B proposes a local road adhesion coefficient estimation method based on road image segmentation. Based on on-board visual signals, this method classifies road conditions by annotating and classifying images of the road environment. However, this estimation method requires accurate recognition of the road environment and signal access and alignment of additional visual sensors. Because visual sensor information is not yet connected to the chassis's local area network, this road adhesion estimation method has not yet been widely used.

[0004] A review of existing vehicle center of mass sideslip angle estimation methods reveals that adhesion estimation algorithms based solely on vehicle and wheel dynamic models face model mismatch issues. Road adhesion estimation algorithms based solely on signals face challenges with sensor information synchronization, external sensor identification accuracy, and data access, making them difficult to use directly in certain critical operating conditions. Given the direct interaction between the road adhesion coefficient and the vehicle chassis, dynamic model-based adhesion estimation methods currently offer certain advantages over signal-based methods. However, improving the accuracy of the dynamic model in current model-based estimation algorithms is crucial for improving the accuracy of road adhesion estimation for real vehicles. Summary of the Invention

[0005] The present application aims to solve one of the technical problems in the related art at least to a certain extent.

[0006] To this end, the first purpose of this application is to propose an online estimation method for the adhesion coefficient of an intelligent vehicle that integrates real vehicle data.

[0007] The second purpose of this application is to propose an online estimation device for the adhesion coefficient of an intelligent vehicle that integrates real vehicle data.

[0008] The third objective of this application is to provide an electronic device.

[0009] The fourth object of this application is to provide a computer-readable storage medium.

[0010] A fifth object of this application is to provide a computer program product.

[0011] To achieve the above objectives, the first embodiment of the present application proposes an online estimation method for the adhesion coefficient of an intelligent vehicle by integrating real vehicle data, comprising:

[0012] Constructing a vehicle dynamics model including a vehicle tire force equation, wherein the vehicle dynamics model is used to describe the dynamic relationship between the vehicle state variables and the tire lateral force and longitudinal force;

[0013] Collect the actual vehicle operation data sequence, divide the input data and output data into past time periods and future time periods, and arrange them in chronological order to form a data time series matrix;

[0014] Based on the data time series matrix, with the goal of minimizing the error between the model output and the actual observation data, constructing an optimization problem to fit the vehicle dynamics model, and obtaining a vehicle dynamics model based on the actual vehicle operation data by solving the optimization problem;

[0015] Based on the fitted vehicle dynamics model and combined with the real-time vehicle input data, the unscented Kalman filter algorithm is used to realize real-time estimation of the road adhesion coefficient.

[0016] Optionally, the vehicle dynamics equation includes:

[0017] Vehicle longitudinal dynamics equation:

[0018]

[0019] Vehicle lateral dynamics equation:

[0020]

[0021] Vehicle yaw dynamics equation:

[0022]

[0023] Where, γ,v y and v xRepresent the yaw rate, lateral velocity and longitudinal velocity at the center of mass in the vehicle body coordinate system, respectively. f and L r Respectively represent the distance between the vehicle's center of mass and the front and rear axles, L w represents the half wheelbase of the vehicle, m represents the curb weight of the vehicle, I z represents the yaw moment of inertia of the vehicle around the z axis, δ f is the front wheel angle, which is converted from the steering wheel angle. The parameter and Represent the tire lateral force base forces of the left front wheel, right front wheel, left rear wheel and right rear wheel respectively, and Represent the tire longitudinal force base forces of the left front wheel, right front wheel, left rear wheel and right rear wheel respectively.

[0024] Optionally, the data time series matrix is ​​implemented by constructing a Hankel matrix. The real vehicle operation data sequence of the collected vehicle is divided into a past time period and a future time period, and arranged in chronological order to form a data time series matrix, including:

[0025] Collect and select a section with a length of T=T ini +N actual vehicle operation data, the input data u of the actual vehicle operation data d and output data y d Divide into past T ini length, future N length;

[0026] Based on the divided data, a Hankel matrix is ​​generated to obtain the data time series matrix, and the formula is:

[0027]

[0028] In the formula, the symbol U p 、U f They are the past and future parts of the input data, symbol Y p 、Y f are respectively the past and future parts of the output data, are Hankel matrices constructed according to the input data and the output data respectively.

[0029] Optionally, constructing an optimization problem fitting vehicle dynamics model based on the data time series matrix with the goal of minimizing the error between the model output and the actual observation data includes:

[0030] Based on the data time series matrix, in order to meet the continuous excitation conditions of the vehicle dynamics model, based on the dynamic response of the vehicle, the following optimization problem is constructed, which is expressed as:

[0031]

[0032] Among them, the function g is the decision variable of the optimization problem, which is used to represent the model parameters of the vehicle dynamics model; the state parameter u ini ,y ini are the input data and output data at the past moment; the state parameters u and y are the input data and output data at the current moment, respectively, which are actual observation data; r is the vehicle state vector corresponding to the current moment, which is the model output of the vehicle dynamics model; λ g >0 is the regularization parameter, and Q>0 is the weight parameter.

[0033] To achieve the above objectives, the second embodiment of the present application proposes an online estimation device for the adhesion coefficient of an intelligent vehicle that integrates real vehicle data, including:

[0034] A model building module, for building a vehicle dynamics model including a vehicle tire force equation, wherein the vehicle dynamics model is used to describe the dynamic relationship between the vehicle state variables and the tire lateral force and longitudinal force;

[0035] The matrix construction module is used to collect the actual vehicle operation data sequence, divide the input data and output data into past time periods and future time periods, and arrange them in chronological order to form a data time series matrix;

[0036] A fitting module is used to construct an optimization problem to fit the vehicle dynamics model based on the data time series matrix with the goal of minimizing the error between the model output and the actual observation data, and obtain a vehicle dynamics model based on the actual vehicle operation data by solving the optimization problem;

[0037] The estimation module is used to realize real-time estimation of the road adhesion coefficient through the unscented Kalman filter algorithm based on the vehicle dynamics model obtained by fitting and combined with the real-time vehicle input data.

[0038] To achieve the above-mentioned purpose, a third embodiment of the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0039] The memory stores computer-executable instructions;

[0040] The processor executes the computer-executable instructions stored in the memory to implement the method as described in any one of the first aspects.

[0041] To achieve the above-mentioned purpose, the fourth embodiment of the present application proposes a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, they are used to implement the method as described in any one of the first aspects.

[0042] To achieve the above-mentioned objectives, the fifth embodiment of the present application proposes a computer program product, which implements any one of the methods in the first aspect when executed by a processor.

[0043] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects:

[0044] (1) By introducing the vehicle dynamics model obtained by real vehicle data training to replace the original vehicle dynamics model based on physical force analysis, the model accuracy of the adhesion coefficient estimation algorithm based on the dynamics model is improved, which can effectively solve the problem of tire model mismatch in the actual vehicle operation of the adhesion coefficient estimation algorithm;

[0045] (2) They are all developed based on the commonly used sensors installed on the current chassis, without the need to introduce additional on-board sensors, resulting in low hardware development costs and strong practicality;

[0046] (3) All steps are analytically designed, without complex neural network unit modules such as deep networks, and the code is easier to implement.

[0047] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0049] Figure 1 A flow chart of an online estimation method for the adhesion coefficient of an intelligent vehicle integrating real vehicle data provided in an embodiment of the present application;

[0050] Figure 2 This is a schematic diagram of the structure of an online estimation device for the adhesion coefficient of an intelligent vehicle that integrates real vehicle data, provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0052] In response to the problems existing in the prior art, an embodiment of the present application provides an online estimation method for the adhesion coefficient of an intelligent vehicle by integrating real vehicle data.

[0053] In one embodiment of the present application, Figure 1As shown, a method for online estimation of the adhesion coefficient of an intelligent vehicle integrating real vehicle data is provided. This embodiment uses the application of this method to a terminal as an example. It can be understood that the vehicle adhesion coefficient estimation algorithm provided in this embodiment can be applied not only to the field of state estimation, but also to the field of vehicle active safety technology.

[0054] like Figure 1 As shown, the method includes the following steps:

[0055] Step 101: construct a vehicle dynamics model including vehicle tire force equations. The vehicle dynamics model is used to describe the dynamic relationship between the vehicle's state variables and the tire lateral force and longitudinal force.

[0056] In an embodiment of the present application, a mathematical model describing the vehicle dynamic characteristics is first constructed. The model is used to explicitly associate the vehicle's state variables (including the longitudinal velocity of the center of mass, the lateral velocity, the yaw angular velocity, etc.) with the longitudinal force and lateral force generated by the tires. The vehicle dynamics model includes three parts: the vehicle longitudinal dynamics equation, the vehicle lateral dynamics equation, and the vehicle yaw dynamics equation.

[0057] First, the longitudinal dynamics equation of the vehicle describes the longitudinal velocity v of the vehicle's center of mass x The longitudinal force generated by the front and rear wheels at the center of mass of the vehicle and the lateral force at the front wheel turning angle δ are taken into account. f The specific expression is:

[0058]

[0059] The above equation reflects the dynamic change of the longitudinal velocity of the vehicle's center of mass under the combined action of longitudinal force and lateral force.

[0060] Secondly, the vehicle lateral dynamics equation describes the lateral velocity v of the vehicle center of mass y The change of , taking into account the projection of the longitudinal force and lateral force of the tire in different directions, as well as the influence of the vehicle's yaw motion, is expressed as follows:

[0061] Vehicle lateral dynamics equation:

[0062]

[0063] This equation reflects the lateral motion characteristics of a vehicle under the influence of lateral force and tire steering angle, and is one of the important foundations for estimating the adhesion coefficient.

[0064] Finally, the vehicle yaw dynamics equation describes the change of the vehicle's yaw angular velocity γ around the center of mass, taking into account the influence of the torque generated by the tire lateral force on the vehicle's center of mass. The specific expression is:

[0065]

[0066] The above equation reflects the dynamic characteristics of the vehicle's yaw under the action of tire lateral force and torque, and is an accurate description of the vehicle's yaw motion.

[0067] In the above equation, the meaning of each parameter is: γ,v y and v x Represent the yaw rate, lateral velocity and longitudinal velocity at the center of mass in the vehicle body coordinate system, respectively. f and L r Respectively represent the distance between the vehicle's center of mass and the front and rear axles, L w represents the half wheelbase of the vehicle, m represents the curb weight of the vehicle, I z represents the yaw moment of inertia of the vehicle around the z axis, δ f is the front wheel angle, which is converted from the steering wheel angle. The parameter and Represent the tire lateral force base forces of the left front wheel, right front wheel, left rear wheel and right rear wheel respectively, and Represent the tire longitudinal force base forces of the left front wheel, right front wheel, left rear wheel and right rear wheel respectively.

[0068] It's understandable that in the aforementioned dynamic equations, the road adhesion coefficient variables are explicitly reflected in the longitudinal, lateral, and yaw equations. These variables indirectly reflect the adhesion between the road and the tire through the distribution and interaction of the tire's longitudinal and lateral forces, providing a physical basis for estimating the adhesion coefficient.

[0069] Considering that tire lateral force depends on the current vehicle's slip angle, and longitudinal force depends on the vehicle's slip rate, the equations in this application select the front wheel angle and vehicle slip rate as system inputs. The front wheel angle directly affects the generation of tire lateral force, while the slip rate determines the magnitude of the longitudinal force. The longitudinal acceleration, lateral acceleration, and yaw rate at the vehicle's center of mass are selected as system outputs. These variables can be collected in real time by onboard sensors and serve as the basis for subsequent state estimation and adhesion coefficient estimation.

[0070] In this step, by constructing a vehicle dynamics model, explicitly splitting the road adhesion coefficient variable, and selecting key system inputs and outputs, the accuracy and practicality of the dynamic equations are ensured, laying a solid theoretical foundation for the subsequent real-time estimation of the road adhesion coefficient.

[0071] Step 102 : collect the actual vehicle operation data sequence of the vehicle, divide the input data and output data into past time periods and future time periods, and arrange them in chronological order to form a data time series matrix.

[0072] In order to realize the online estimation of vehicle adhesion coefficient, it is necessary to establish a data time series matrix based on the actual operation data of the vehicle.

[0073] In the embodiment of the present application, the vehicle input data and output data are first collected through the vehicle-mounted sensors, data acquisition device or vehicle-mounted control unit (ECU), specifically including:

[0074] (1) Input state data: system input parameters that affect vehicle dynamics, such as the vehicle's front wheel angle, vehicle slip rate, and tire slip angle.

[0075] (2) Output state data: longitudinal acceleration, lateral acceleration, yaw rate and other key parameters reflecting the vehicle state at the center of mass of the vehicle.

[0076] It should be noted that the collected data should ensure sufficient continuity and integrity within a certain time frame to meet the data quality requirements of subsequent modeling.

[0077] The collected real vehicle operation data series is then divided into past time periods and future time periods in chronological order. The past time period is used to represent the dynamic behavior of the system input and output at historical moments, and the future time period is used to describe the changing trend of the system status at future moments.

[0078] Among them, the divided input data u d and output data y d It can be expressed as:

[0079]

[0080] Where, T ini represents the length of the initial moment, that is, the length of the past time period, and N represents the length of the future time period.

[0081] Furthermore, in order to maintain the temporal structure of the data and ensure the observability of the system, the embodiment of the present application models the divided input and output data through the Hankel matrix to construct the input and output Hankel matrices The formula is:

[0082]

[0083] Where, is the Hankel matrix of the input data, is the Hankel matrix of the output data, l represents the order of the Hankel matrix, T ini +N indicates the total length of the data.

[0084] In addition, in order to ensure that the constructed Hankel matrix meets the observability requirements of the system, it is necessary to verify the row full rank condition of the matrix, that is:

[0085]

[0086] The full rank condition ensures that the data matrix has sufficient timing information and provides a stable input data basis for parameter estimation of the subsequent system model.

[0087] Finally, the input data and output data are arranged in chronological order using the Hankel matrix structure, ultimately forming a data timing matrix, which can be expressed as:

[0088]

[0089] In the formula, the symbol U p 、U f They are the past and future parts of the input data, symbol Y p 、Y f They are the past and future parts of the output data respectively.

[0090] This step collects real-world vehicle operating data, divides it into time periods, and constructs a full-rank Hankel matrix to form a data time series matrix. This matrix maintains the system's time series characteristics while providing a complete and stable data input foundation for fitting the vehicle dynamics model and estimating the road adhesion coefficient.

[0091] Step 103 , based on the data time series matrix, with the goal of minimizing the error between the model output and the actual observation data, construct an optimization problem to fit the vehicle dynamics model, and obtain the vehicle dynamics model based on the actual vehicle operation data by solving the optimization problem.

[0092] To ensure that the constructed vehicle dynamics model accurately reflects the vehicle's actual dynamic response while also ensuring observability and identifiability, a continuous excitation condition is required. According to William's theorem, this condition requires that the time evolution of the input data sequence contain sufficiently rich frequency information to ensure observability and accurate estimation of the dynamic parameters. Simply put, the input data sequence must impose continuous and appropriate dynamic changes on the system to stimulate all of its states.

[0093] In this embodiment of the present application, the vehicle input data and the corresponding vehicle state vector at the current moment are selected as the input and output conditions of the dynamic system. If the input sequence meets the continuous excitation condition, the vehicle dynamic system can be expressed as an optimization problem of the following form:

[0094]

[0095] Among them, the function g is the decision variable of the optimization problem, which is used to represent the model parameters of the vehicle dynamics model; the state parameter uini ,y ini are the input data and output data at the past moment; the state parameters u and y are the input data and output data at the current moment, respectively, which are actual observation data; r is the vehicle state vector corresponding to the current moment, which is the model output of the vehicle dynamics model; λ g >0 is a regularization parameter used to suppress overfitting of model parameters and ensure the stability of the model; Q>0 is a weight parameter used to balance the weight of observation errors.

[0096] Then, the above optimization problem is solved by a numerical optimization method (such as the least squares method or a constrained regularization method) to obtain the optimized variable g, that is, the parameters of the vehicle dynamics model. This application does not specifically limit the specific solution method, but only provides examples for illustration.

[0097] It should be emphasized that the solved g represents the key parameter of the vehicle dynamics model. It is fitted based on actual vehicle operation data and can truly reflect the dynamic characteristics of the vehicle under actual road conditions.

[0098] By solving the optimization problem, the vehicle dynamics model parameters g obtained have the following characteristics

[0099] (1) Ability to accurately describe the dynamic relationship between vehicle state variables and tire lateral and longitudinal forces;

[0100] (2) Fitting based on actual vehicle operation data can ensure that the model parameters are consistent with the actual working conditions;

[0101] (3) Provide theoretical support and calculation basis for subsequent road adhesion coefficient estimation.

[0102] In this step, by introducing continuous excitation conditions, we ensure that the temporal evolution of the input data can stimulate all states of the system. By combining the temporal structure of the Hankel matrix with the constraints of the optimization problem, we can accurately fit the vehicle dynamics model and provide a reliable dynamic basis for the subsequent estimation of the road adhesion coefficient.

[0103] Step 104 : Based on the fitted vehicle dynamics model and in combination with the real-time vehicle input data, the road adhesion coefficient is estimated in real time using an unscented Kalman filter algorithm.

[0104] After completing the parameter fitting of the vehicle dynamics model, in order to achieve accurate real-time estimation of the road adhesion coefficient, the embodiment of the present application further designs an unscented Kalman filter estimation algorithm based on data enhancement based on the vehicle dynamics model obtained according to the measured data, combined with the real-time input data of the vehicle, according to the unscented Kalman filter algorithm, to achieve accurate real-time estimation of the road adhesion coefficient.

[0105] Specifically, the vehicle dynamics model derived from the measured data has been fitted through the previous steps. It describes the dynamic relationship between state variables such as the longitudinal velocity, lateral velocity, and yaw rate at the vehicle's center of mass and system inputs (such as front wheel angle and slip rate). In this step, the vehicle dynamics model is introduced as a state transition equation into the unscented Kalman filter algorithm to predict and update the vehicle state.

[0106] In the unscented Kalman filter algorithm, the system state variables and observation variables are defined as follows:

[0107] (1) System state variables: including the vehicle's longitudinal velocity, lateral velocity, yaw rate, and road adhesion coefficient. As a key variable to be estimated, the road adhesion coefficient is closely related to the dynamic changes of the tire longitudinal force and lateral force.

[0108] (2) System input variables: including the vehicle’s front wheel angle, slip rate, and other real-time input data that can affect the vehicle’s dynamic response.

[0109] (3) System observation variables: including the longitudinal acceleration, lateral acceleration and yaw rate at the center of mass of the vehicle. These data are collected in real time by on-board sensors.

[0110] These definitions ensure the physical connection between the system state and the observed data, and provide a theoretical basis for subsequent state prediction and update.

[0111] When estimating the road adhesion coefficient, the prediction step begins with a prediction of the system state variables at the current moment using the vehicle dynamics model and system input data. The vehicle state, including longitudinal velocity, lateral velocity, yaw rate, and adhesion coefficient, is then predicted based on the vehicle dynamics model. The covariance between the state variables is simultaneously calculated to ensure effective modeling of the uncertainty in the system prediction. The prediction results include the system state mean and covariance matrix, which are updated in the next step.

[0112] For the update step: when the observation data is available, the predicted state variables are updated in combination with the longitudinal acceleration, lateral acceleration and yaw angular velocity collected by the on-board sensors. Specifically, the observation value at the current moment is first calculated through the dynamic model, and then the difference between the observation value and the actual observation data of the sensor is compared, and the predicted state is corrected according to the difference.

[0113] Through state updates, the system can correct the estimated values ​​of state variables such as the road surface adhesion coefficient in real time, thereby improving the accuracy of the estimation results.

[0114] In this step, the embodiment of the present application designs a real-time estimation algorithm for adhesion coefficient based on data enhancement by combining the vehicle dynamics model based on data fitting with the unscented Kalman filter algorithm. Through the prediction and update steps, the present application can perform high-precision and real-time estimation of the road adhesion coefficient during vehicle driving, provide reliable status information for the vehicle control system, and improve the safety and dynamic control performance of the vehicle.

[0115] It should also be noted that in addition to the unscented Kalman filter algorithm, this application also supports other adhesion coefficient estimation algorithms based on vehicle dynamics models, such as the extended Kalman filter (EKF) or particle filter (PF), etc., and this application does not make specific limitations on this.

[0116] In order to implement the above embodiment, the present application also proposes an online estimation device for the adhesion coefficient of an intelligent vehicle that integrates real vehicle data. Figure 2 This is a schematic diagram of the structure of an intelligent vehicle adhesion coefficient online estimation device that integrates real vehicle data provided by an embodiment of the present application. Figure 2 As shown, the device includes:

[0117] A model building module 100 is used to build a vehicle dynamics model including a vehicle tire force equation, wherein the vehicle dynamics model is used to describe the dynamic relationship between the vehicle state variables and the tire lateral force and longitudinal force;

[0118] The matrix construction module 200 is used to collect the actual vehicle operation data sequence of the vehicle, divide the input data and output data into past time periods and future time periods, and arrange them in chronological order to form a data time series matrix;

[0119] The fitting module 300 is used to construct an optimization problem to fit the vehicle dynamics model based on the data time series matrix with the goal of minimizing the error between the model output and the actual observation data, and obtain the vehicle dynamics model based on the actual vehicle operation data by solving the optimization problem;

[0120] The estimation module 400 is used to estimate the road adhesion coefficient in real time by using an unscented Kalman filter algorithm based on the fitted vehicle dynamics model and in combination with the real-time vehicle input data.

[0121] In order to implement the above embodiments, the present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.

[0122] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0123] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0124] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in this application are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0125] It is important to note that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold beyond these legitimate uses. Furthermore, such collection / sharing should be conducted only after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes the relevant user information before using the feature. Furthermore, any necessary steps must be taken to safeguard and secure access to such personal information and ensure that others with access to personal information comply with its privacy policy and procedures.

[0126] This application contemplates providing implementations that allow users to selectively block the use or access of personal information data. Specifically, this disclosure contemplates providing hardware and / or software to prevent or block access to such personal information data. Risks can be minimized by limiting data collection and deleting data once it is no longer needed. Furthermore, where applicable, such personal information can be de-identified to protect user privacy.

[0127] In the descriptions of the foregoing embodiments, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are mutually inconsistent.

[0128] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0129] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0130] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0131] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0132] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0133] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0134] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

[0135] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this application can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of this application can be achieved. This is not limited herein.

[0136] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. An online estimation method for adhesion coefficient of an intelligent vehicle integrating real vehicle data, characterized in that: The following steps are involved: Constructing a vehicle dynamics model including a vehicle tire force equation, wherein the vehicle dynamics model is used to describe a dynamic relationship between a state variable of the vehicle and a lateral force and a longitudinal force of the tire; Collect the actual vehicle operation data sequence of the vehicle, divide the input data and output data into past time periods and future time periods, and arrange them in chronological order to form a data time series matrix; Based on the data time series matrix, with the goal of minimizing the error between the model output and the actual observation data, constructing an optimization problem to fit the vehicle dynamics model, and obtaining a vehicle dynamics model based on the actual vehicle operation data by solving the optimization problem; Based on the fitted vehicle dynamics model and combined with the real-time vehicle input data, the unscented Kalman filter algorithm is used to realize real-time estimation of the road adhesion coefficient.

2. The method according to claim 1, characterized in that The vehicle dynamics equations include: Vehicle longitudinal dynamics equation: Vehicle lateral dynamics equation: Vehicle yaw dynamics equation: In the formula, γ,v y and v x Respectively represent the yaw rate, lateral velocity and longitudinal velocity at the center of mass in the vehicle body coordinate system, L f and L r Respectively represent the distance between the center of mass of the vehicle and the front and rear axles, L w represents the half wheelbase of the vehicle, m represents the curb weight of the vehicle, I z represents the yaw moment of inertia of the vehicle around the z-axis, δ f is the front wheel angle, which is converted from the steering wheel angle. and Represent the tire lateral force base forces of the left front wheel, right front wheel, left rear wheel and right rear wheel respectively, and Represent the tire longitudinal force base forces of the left front wheel, right front wheel, left rear wheel and right rear wheel respectively.

3. The method according to claim 2, characterized in that The data time series matrix is ​​realized by constructing a Hankel matrix. The real vehicle operation data sequence of the collected vehicle is divided into a past time period and a future time period, and arranged in chronological order to form a data time series matrix, including: Collect and select a section with a length of T = T ini +N actual vehicle operation data, and the input data u of the actual vehicle operation data d And the output data y d Divide into past T ini length, future N length; Based on the divided data, a Hankel matrix is ​​generated to obtain the data time series matrix, and the formula is: In the formula, the symbol U p , U f They are the past and future parts of the input data, symbol Y p , Y f are the past and future parts of the output data respectively, are Hankel matrices constructed according to the input data and the output data respectively.

4. The method according to claim 3, characterized in that The method of constructing an optimization problem fitting vehicle dynamics model based on the data time series matrix with the goal of minimizing the error between the model output and the actual observation data includes: Based on the data time series matrix, in order to meet the continuous excitation condition of the vehicle dynamics model, based on the dynamic response of the vehicle, the following form of optimization problem is constructed, and the expression is: Among them, function g is the decision variable of the optimization problem, which is used to represent the model parameters of the vehicle dynamics model; state parameter u ini ,y ini are the input data and output data at the past moment; the state parameters u and y are the input data and output data at the current moment, respectively, and are the actual observation data; r is the vehicle state vector corresponding to the current moment, and is the model output of the vehicle dynamics model; λ g >0 is the regularization parameter, and Q>0 is the weight parameter.

5. An intelligent vehicle adhesion coefficient online estimation device integrating real vehicle data, characterized in that: include: A model building module, used to build a vehicle dynamics model including a vehicle tire force equation, wherein the vehicle dynamics model is used to describe a dynamic relationship between a state variable of the vehicle and a lateral force and a longitudinal force of the tire; The matrix construction module is used to collect the actual vehicle operation data sequence of the vehicle, divide the input data and output data into past time periods and future time periods, and arrange them in chronological order to form a data time series matrix; A fitting module is used to construct an optimization problem to fit the vehicle dynamics model based on the data time series matrix with the goal of minimizing the error between the model output and the actual observation data, and obtain a vehicle dynamics model based on the actual vehicle operation data by solving the optimization problem; The estimation module is used to realize real-time estimation of the road adhesion coefficient through the unscented Kalman filter algorithm based on the fitted vehicle dynamics model and the real-time input data of the vehicle.

6. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 4 when executed by a processor.

8. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 4 when being executed by a processor.

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