Vehicle state estimation method, system and device and storage medium

Abstract

The invention discloses a vehicle state estimation method, system and device and a storage medium. The vehicle state estimation method comprises the steps that transverse acceleration, longitudinal acceleration and yaw velocity are obtained through a sensor carried by a target vehicle; constructing a state transfer equation and a measurement equation of the square root volume Kalman filter; based on the transverse acceleration, the longitudinal acceleration, the yaw velocity, the state transfer equation and the measurement equation, key state parameters of the target vehicle are determined through a square root volume Kalman filtering method, based on the key state parameters, physical prediction is conducted on the target vehicle through a nonlinear tire model, and the tire lateral force is obtained; based on the tire lateral force and the side slip angle, the vehicle state of the target vehicle is evaluated, and the efficiency and accuracy of vehicle state estimation are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle state estimation, and in particular to a vehicle state estimation method, system, device and storage medium. BACKGROUND

[0002] Key state parameters of a vehicle, such as longitudinal speed, lateral speed and center of mass side slip angle, are indispensable input information for electronic stability control systems (ESC), anti-lock braking systems (ABS) and automatic driving decision planning modules. However, due to cost and technical limitations, key states such as lateral speed cannot be directly measured by low-cost sensors and must be estimated by algorithms.

[0003] Currently, the mainstream vehicle state estimation method is mainly achieved through high-performance nonlinear filtering algorithms, but its implementation on embedded systems requires high computing resources, challenging the computing power of vehicle microcontrollers (MCU) and increasing hardware costs. SUMMARY

[0004] The present application aims to provide a vehicle state estimation method, system, device and storage medium, which can reduce the computing power requirement, reduce the hardware cost and improve the efficiency of vehicle state estimation.

[0005] In a first aspect, an embodiment of the present application provides a vehicle state estimation method, comprising the following steps: Obtaining lateral acceleration, longitudinal acceleration and yaw rate through sensors carried by a target vehicle; Constructing a state transition equation and a measurement equation of a square root cubature Kalman filter; Determining key state parameters of the target vehicle based on the lateral acceleration, the longitudinal acceleration, the yaw rate, the state transition equation and the measurement equation through a square root cubature Kalman filtering method, wherein the key state parameters include longitudinal speed, lateral speed and center of mass side slip angle; Physically predicting the target vehicle through a nonlinear tire model based on the key state parameters to obtain tire lateral force; Performing vehicle state evaluation on the target vehicle based on the tire lateral force and the center of mass side slip angle.

[0006] The application obtains lateral acceleration, longitudinal acceleration and yaw rate through sensors carried by a target vehicle; constructs a state transmission equation and a measurement equation of a square-root cubature Kalman filter; determines key state parameters of the target vehicle through a square-root cubature Kalman filtering method based on the lateral acceleration, the longitudinal acceleration, the yaw rate, the state transmission equation and the measurement equation, wherein the key state parameters include longitudinal velocity, lateral velocity and mass center side slip angle; performs physical prediction on the target vehicle through a nonlinear tire model based on the key state parameters to obtain tire lateral force; and performs vehicle state evaluation on the target vehicle based on the tire lateral force and the mass center side slip angle, so that the demand for computing power can be reduced, the hardware cost can be reduced, and the efficiency and accuracy of vehicle state estimation can be improved.

[0007] According to some embodiments of the application, the key state parameters of the target vehicle are determined through the square-root cubature Kalman filtering method based on the lateral acceleration, the longitudinal acceleration, the yaw rate, the state transmission equation and the measurement equation, including: The discrete nonlinear system is constructed through the following formula:

[0008] wherein, is a system state vector at k moment, is a measurement vector at k moment, is a state transmission equation of the square-root cubature Kalman filter, is a measurement equation of the square-root cubature Kalman filter, is an input vector, is a system process noise, is a measurement noise; The initial state estimation value and the covariance square root are determined through the following calculation formula:

[0009]

[0010] wherein, is a process noise at k moment, is a measurement noise at k moment, is a covariance matrix at k moment, is a zero-mean Gaussian white noise at k moment, is a Kronecker-delta function, is a Cholesky decomposition of a matrix, is a process noise at k moment, is time measurement noise, is an initial state estimate value, is a covariance matrix, is a covariance square root; The volume point is determined by the following formula:

[0011] wherein, is a volume point, is a state vector dimension, is a unit volume point, is a unit vector with the i-th element being 1; The predicted state mean and the predicted state covariance are calculated by the following formula:

[0012] wherein, is a value of the volume point substituted into the state transition function, is a predicted state mean, is a predicted state covariance matrix; The amplified predicted state covariance matrix is calculated by the following formula:

[0013]

[0014] wherein, is a fading factor, is an amplified predicted state covariance matrix; The predicted observation mean and the predicted observation covariance are calculated by the following formula:

[0015] wherein, is a volume point observation function value, is a predicted observation mean, is a first predicted observation covariance; The measurement noise covariance matrix is updated using the Sage-Husa adaptive filtering algorithm by the following formula: ; wherein, is a forgetting factor greater than zero and less than one; is an adjustment factor output by a fuzzy controller; is a default noise covariance matrix, is an updated measurement noise covariance matrix, is an intermediate parameter value; The second predicted observation covariance is calculated by the following formula:

[0016] in, The second predicted observation covariance; The updated square root of the covariance is obtained by using the Cholesky update formula:

[0017] in, For Kalman gain, This is the updated state estimate. The square root of the updated covariance; Based on the lateral acceleration, the longitudinal acceleration, the yaw rate, and the updated square root of the covariance, the key state parameters of the target vehicle are determined using the state transfer equation, the measurement equation, and the square root capacitive Kalman filter method.

[0018] According to some embodiments of this application, the state transfer equation is constructed using the following formula: ; in, For the first The state transfer equation at time t, For the first longitudinal velocity at time t, For the first Lateral velocity at time, For the first longitudinal acceleration at time t, For the first Lateral acceleration at time t, For the first yaw rate at time t, This is the preset sampling period.

[0019] In some implementations, the measurement equation is constructed using the following formula: ; in, For the first Equations for measuring time.

[0020] According to some embodiments of this application, based on the key state parameters, a physical prediction of the target vehicle is performed using a nonlinear tire model, and the tire lateral force is calculated using the following formula: ; in, The lateral force of the front tires. The lateral force of the rear tire. is a side slip stiffness of a front axle, is a side slip stiffness of a rear axle, is a side slip angle of a mass center, is a front wheel steering angle, lf is a distance from a mass center of the vehicle to the front axle, and lr is a distance from the mass center of the vehicle to the rear axle.

[0021] According to some embodiments of the present application, the vehicle state evaluation of the target vehicle based on the tire lateral force and the side slip angle of the mass center comprises: In a case where the tire lateral force is greater than a preset maximum grip force or the side slip angle of the mass center is greater than a preset tire limit side slip angle, the vehicle state evaluation of the target vehicle obtains a vehicle state evaluation result.

[0022] According to some embodiments of the present application, the method further comprises: In a case where the vehicle state evaluation result is a serious abnormality, a preset protection mechanism of the target vehicle is triggered and a warning mark is displayed on the target vehicle to remind a user.

[0023] In a second aspect, embodiments of the present application provide a vehicle state estimation system, which comprises: a data acquisition module configured to acquire lateral acceleration, longitudinal acceleration and yaw rate through a sensor carried by a target vehicle; an equation construction module configured to construct a state transition equation and a measurement equation of a square-root cubature Kalman filter; a key state parameter determination module configured to determine key state parameters of the target vehicle by a square-root cubature Kalman filtering method based on the lateral acceleration, the longitudinal acceleration, the yaw rate, the state transition equation and the measurement equation, wherein the key state parameters comprise longitudinal velocity, lateral velocity and side slip angle of a mass center; a tire lateral force calculation module configured to perform physical prediction on the target vehicle by a nonlinear tire model based on the key state parameters to obtain a tire lateral force; a vehicle state evaluation module configured to perform vehicle state evaluation on the target vehicle based on the tire lateral force and the side slip angle of the mass center.

[0024] The application obtains lateral acceleration, longitudinal acceleration and yaw rate through sensors carried by a target vehicle; constructs a state transition equation and a measurement equation of a square-root cubature Kalman filter; determines key state parameters of the target vehicle through a square-root cubature Kalman filtering method based on the lateral acceleration, the longitudinal acceleration, the yaw rate, the state transition equation and the measurement equation, wherein the key state parameters include longitudinal velocity, lateral velocity and a mass center side slip angle; performs physical prediction on the target vehicle through a nonlinear tire model based on the key state parameters to obtain tire lateral force; and performs vehicle state evaluation on the target vehicle based on the tire lateral force and the mass center side slip angle, so that the demand for computing power can be reduced, the hardware cost can be reduced, and the efficiency and accuracy of vehicle state estimation can be improved.

[0025] In a third aspect, an embodiment of the application provides a vehicle state estimation electronic device, including at least one control processor and a memory in communication connection with the at least one control processor; the memory stores instructions executable by the at least one control processor, and the instructions are executed by the at least one control processor to enable the at least one control processor to execute the vehicle state estimation method described above.

[0026] In a fourth aspect, an embodiment of the application provides a computer readable storage medium, which stores computer executable instructions for causing a computer to execute the vehicle state estimation method described above.

[0027] Additional aspects and advantages of the application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0028] The above and / or additional aspects and advantages of the application will become apparent and be readily appreciated from the following description, including the appended drawings, wherein: Figure 1 is a flowchart of an embodiment of the vehicle state estimation method provided by the application; Figure 2 is a structural schematic diagram of an embodiment of the vehicle state estimation system provided by the application; Figure 3 is a structural schematic diagram of an embodiment of the electronic device provided by the application. DETAILED DESCRIPTION

[0029] Embodiments of the application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference signs represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below are exemplary and are only used to explain the application, and cannot be understood as a limitation on the application.

[0030] In the description of the present application, if the first, second, etc. are described, it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features or the sequence of indicated technical features.

[0031] In the description of the present application, it should be understood that the orientation description, such as the orientation or position relationship indicated by up, down, etc. is based on the orientation or position relationship shown in the drawings, only for the purpose of facilitating the description of the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the device or element indicated must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application.

[0032] In the description of the present application, it should be noted that, unless otherwise explicitly limited, the words such as setting, installing, connecting, etc. should be broadly understood, and those skilled in the art can reasonably determine the specific meaning of the above words in the present application in combination with the specific content of the technical solution.

[0033] The key state parameters of the vehicle, such as longitudinal speed, lateral speed and mass center side slip angle, are indispensable input information for electronic stability control system (ESC), anti-lock braking system (ABS) and automatic driving decision planning module. However, due to cost and technical limitations, key states such as lateral speed cannot be directly measured by low-cost sensors and must be estimated by algorithms.

[0034] At present, the mainstream vehicle state estimation method is mainly realized by high-performance nonlinear filtering algorithm, but its implementation on embedded system requires high computing resources, which challenges the computing power of vehicle-mounted microcontroller (MCU) and increases the hardware cost.

[0035] In order to solve the above technical defects, the embodiments of the present application provide a vehicle state estimation method, system, device and storage medium.

[0036] Please refer to Figure 1 , which is a flowchart of the vehicle state estimation method provided by the embodiments of the present application. The method is applied to an electronic device, which can be a server, etc. As Figure 1 shown, the vehicle state estimation method comprises: Step S101, acquiring lateral acceleration, longitudinal acceleration and yaw rate through the sensors carried by the target vehicle; Step S102, constructing the state transition equation and measurement equation of the square root cubage Kalman filter; In step S103, based on the lateral acceleration, the longitudinal acceleration, the yaw rate, the state transition equation and the measurement equation, the key state parameters of the target vehicle are determined by a square root cubature Kalman filtering method, wherein the key state parameters include the longitudinal velocity, the lateral velocity and the mass center side slip angle. In step S104, based on the key state parameters, the target vehicle is physically predicted by a nonlinear tire model to obtain tire lateral force. In step S105, based on the tire lateral force and the mass center side slip angle, the vehicle state of the target vehicle is evaluated.

[0037] In the above step S101, the lateral acceleration, the longitudinal acceleration and the yaw rate are obtained by sensors mounted on the vehicle, and these sensor data contain noise, which provides important observation information for state estimation.

[0038] The lateral acceleration, the longitudinal acceleration and the yaw rate are obtained by sensors mounted on the target vehicle; the state transition equation and the measurement equation of the square root cubature Kalman filter are constructed; based on the lateral acceleration, the longitudinal acceleration, the yaw rate, the state transition equation and the measurement equation, the key state parameters of the target vehicle are determined by the square root cubature Kalman filtering method, wherein the key state parameters include the longitudinal velocity, the lateral velocity and the mass center side slip angle; based on the key state parameters, the target vehicle is physically predicted by a nonlinear tire model to obtain tire lateral force; based on the tire lateral force and the mass center side slip angle, the vehicle state of the target vehicle is evaluated, so that the demand for computing power can be reduced, the hardware cost can be reduced, and the efficiency and accuracy of vehicle state estimation can be improved.

[0039] In some embodiments, based on the lateral acceleration, the longitudinal acceleration, the yaw rate, the state transition equation and the measurement equation, the key state parameters of the target vehicle are determined by the square root cubature Kalman filtering method, including: The discrete nonlinear system is constructed by the following formula:

[0040] wherein, is the system state vector at time k, is the measurement vector at time k, is the state transition equation of the square root cubature Kalman filter, is the measurement equation of the square root cubature Kalman filter, is the input vector, is the system process noise, is the measurement noise; The initial state estimate and the covariance square root are determined by the following calculation formula:

[0041]

[0042] where, is the process noise at time k, is the measurement noise at time k, is the covariance matrix at time k, is the zero-mean Gaussian white noise at time k, is the Kronecker-delta function, is the Cholesky decomposition of matrix, is the process noise at time k, is the measurement noise at time k, is the initial state estimate, is the covariance matrix, is the square root of the covariance matrix; The cubature points are determined by the following formula:

[0043] where, is the cubature point, is the dimension of the state vector, is the unit cubature point, is the unit vector with the i-th element being 1; The predicted state mean and the predicted state covariance are calculated by the following formula:

[0044] where, is the value of the cubature point substituted into the state transition function, is the predicted state mean, is the predicted state covariance matrix; The scaled predicted state covariance matrix is calculated by the following formula:

[0045]

[0046] where, is the fading factor, is the scaled predicted state covariance matrix; The predicted observation mean and the predicted observation covariance are calculated by the following formula:

[0047] where, ​​​​​​The observed function values ​​at the volume point. To predict the observed mean, The first predicted observation covariance; The measurement noise covariance matrix is ​​updated using the Sage-Husa adaptive filtering algorithm according to the following formula: ; in, The forgetting factor is greater than zero and less than one. This is the adjustment factor output by the fuzzy controller; This is the default noise covariance matrix. To update the measurement noise covariance matrix, These are intermediate parameter values; The second predicted observation covariance is calculated using the following formula:

[0048] in, The second predicted observation covariance; The updated square root of the covariance is obtained by using the Cholesky update formula:

[0049] in, For Kalman gain, This is the updated state estimate. The square root of the updated covariance; Based on lateral acceleration, longitudinal acceleration, yaw rate, and the updated square root of covariance, the key state parameters of the target vehicle are determined by the state transfer equation, measurement equation, and square root capacitive Kalman filter method.

[0050] This application improves the system's robustness to sensor noise variations by enabling SCKF to maintain near-optimal estimation accuracy under different environmental conditions; it also implements environmental adaptive filtering, enhancing system adaptability. In some embodiments, the state transfer equation is constructed using the following formula: ; in, For the first The state transfer equation at time t, For the first longitudinal velocity at time t, For the first Lateral velocity at time, For the first longitudinal acceleration at time t, For the first Lateral acceleration at time t, For the first yaw rate at time t, This is the preset sampling period.

[0051] In some embodiments, the measurement equation is constructed using the following formula: ; in, For the first Equations for measuring time.

[0052] This application determines the key state parameters of the target vehicle by combining the state transfer equation and the measurement equation, providing more accurate reference data for vehicle state estimation and improving the accuracy of vehicle state estimation.

[0053] In some embodiments, based on key state parameters, a nonlinear tire model is used to perform physical prediction of the target vehicle, and the tire lateral force is calculated using the following formula: ; in, The lateral force of the front tires. The lateral force of the rear tire. For the lateral stiffness of the front axle, For the rear axle lateral stiffness, The sideslip angle is the angle of the center of mass. 1 is the front wheel steering angle, lf is the distance from the vehicle's center of gravity to the front axle, and lr is the distance from the vehicle's center of gravity to the rear axle.

[0054] In some embodiments, a vehicle condition assessment of the target vehicle is performed based on tire lateral force and center of gravity sideslip angle, including: When the lateral force of the tire is greater than the preset maximum grip or the center of gravity sideslip angle is greater than the preset tire limit sideslip angle, the vehicle condition of the target vehicle is assessed, and the vehicle condition assessment result is obtained.

[0055] The above-mentioned preset maximum grip is a value set in advance according to actual needs.

[0056] The aforementioned preset tire slip angle is a value pre-set according to actual needs.

[0057] This application assesses the vehicle state of the target vehicle by using tire lateral force and center of gravity sideslip angle, thereby enhancing the reliability of the state estimation: it avoids estimation results that are "mathematically optimal but physically unreasonable" by verifying them through a physical model.

[0058] In some embodiments, the method further includes: If the vehicle condition assessment result is severely abnormal, a warning sign will be displayed on the target vehicle to alert the user, and the target vehicle's preset protection mechanism will be triggered.

[0059] The application improves system safety by timely identifying and triggering protection when there is model mismatch or sensor abnormality.

[0060] In addition, with reference to Figure 2 An embodiment of the application provides a vehicle state estimation system, including a data acquisition module 1100, an equation construction module 1200, a key state parameter determination module 1300, a tire lateral force calculation module 1400 and a vehicle state evaluation module 1500, wherein: The data acquisition module 1100 is configured to acquire lateral acceleration, longitudinal acceleration and yaw rate through sensors carried by the target vehicle. The equation construction module 1200 is configured to construct a state transition equation and a measurement equation of a square-root cubature Kalman filter. The key state parameter determination module 1300 is configured to determine key state parameters of the target vehicle based on the lateral acceleration, the longitudinal acceleration, the yaw rate, the state transition equation and the measurement equation by a square-root cubature Kalman filtering method, wherein the key state parameters include longitudinal velocity, lateral velocity and a center of mass side slip angle. The tire lateral force calculation module 1400 is configured to perform physical prediction on the target vehicle based on the key state parameters by a nonlinear tire model to obtain tire lateral force. The vehicle state evaluation module 1500 is configured to perform vehicle state evaluation on the target vehicle based on the tire lateral force and the center of mass side slip angle.

[0061] The application acquires lateral acceleration, longitudinal acceleration and yaw rate through sensors carried by the target vehicle, constructs a state transition equation and a measurement equation of a square-root cubature Kalman filter, determines key state parameters of the target vehicle based on the lateral acceleration, the longitudinal acceleration, the yaw rate, the state transition equation and the measurement equation by a square-root cubature Kalman filtering method, wherein the key state parameters include longitudinal velocity, lateral velocity and a center of mass side slip angle, performs physical prediction on the target vehicle based on the key state parameters by a nonlinear tire model to obtain tire lateral force, and performs vehicle state evaluation on the target vehicle based on the tire lateral force and the center of mass side slip angle, so that the demand for computing power can be reduced, the hardware cost can be reduced, and the efficiency and accuracy of vehicle state estimation can be improved.

[0062] It should be noted that the system embodiment and the method embodiment described above are based on the same inventive concept, and therefore the related content of the method embodiment described above is also applicable to the system embodiment, which will not be described here again.

[0063] Figure 3 A hardware structure schematic diagram of vehicle state estimation provided by an embodiment of the application is shown.

[0064] The vehicle state estimation device can include a processor 301 and a memory 302 having computer program instructions stored therein.

[0065] In particular, the processor 301 described above can include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement one or more embodiments of the present application.

[0066] The memory 302 can include a mass storage for data or instructions. By way of example and not limitation, the memory 302 can include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a Universal Serial Bus (USB) drive or a combination of two or more of these. The memory 302 can include removable or non-removable (or fixed) media, where appropriate. The memory 302 can be internal or external to the integrated gateway disaster recovery device, as appropriate. In particular embodiments, the memory 302 is non-volatile, solid-state memory.

[0067] In some embodiments, the memory 302 can include read-only memory (ROM), random access memory (RAM), a disk storage medium device, an optical storage medium device, a flash memory device, electrical, optical, or other physical / tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software that, when executed (by one or more processors), is operable to perform operations described with reference to the methods according to an aspect of the present disclosure.

[0068] The processor 301 implements any one of the vehicle state estimation methods described in the above embodiments by reading and executing the computer program instructions stored in the memory 302.

[0069] In one example, the vehicle state estimation device can further include a communication interface 303 and a bus 310. As shown, the processor 301, the memory 302, and the communication interface 303 are connected through the bus 310 and complete communication therebetween. Figure 3

[0070] The communication interface 303 is mainly used to realize the communication between the modules, devices, units and / or equipment in the embodiments of the present application.

[0071] ​Bus 310 includes hardware, software, or both, to couple components of the vehicle state estimation device to each other in a known manner. Although specific bus implementations are described and illustrated, the application contemplates any suitable bus or interconnect, or combination of busses or interconnects, and the components coupled thereto.

[0072] The vehicle state estimation device can perform the vehicle state estimation method in the embodiments of the application based on the three-dimensional design model, thereby realizing the vehicle state estimation method and system described in the embodiments of the application. Figure 1 and Figure 2 The vehicle state estimation method and system described in the embodiments of the application.

[0073] In addition, in combination with the vehicle state estimation method in the above embodiments, the embodiments of the application can provide a computer storage medium to realize. The computer storage medium has computer program instructions stored thereon; the computer program instructions are executed by a processor to realize any one of the vehicle state estimation methods in the above embodiments.

[0074] It needs to be clear that the application is not limited to the specific configurations and processes described above and shown in the drawings. For the sake of brevity, detailed descriptions of well-known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between steps, after understanding the spirit of the application.

[0075] The functions noted in the description of the structural block diagrams above can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, and the like. When implemented in software, the elements of the present application are program or code segments that are used to perform the required tasks. The program or code segments can be stored in a machine-readable medium, or transmitted through a data signal carried in a carrier wave over a transmission medium or communication link. A "machine-readable medium" includes any medium that can store or transport information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, and the like. The code segments can be downloaded via computer networks such as the Internet, intranets, and the like.

[0076] It is also important to note that the examples mentioned in the present application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the steps mentioned above, that is, the steps can be performed in the order mentioned in the examples, or in an order different from the examples, or several steps can be performed simultaneously.

[0077] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer program instructions can also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other processing device to operate in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function / act specified in the flowchart and / or block diagram block or blocks. The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer program instructions can also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other processing device to operate in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function / act specified in the flowchart and / or block diagram block or blocks.

[0078] The above merely describes a specific implementation of the present application. Those skilled in the art can clearly understand the specific working processes of the system, modules and units described above for the convenience and brevity of description, and can refer to the corresponding processes in the foregoing method embodiments, which will not be described herein again. It should be understood that the protection scope of the present application is not limited to this, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements should be covered within the protection scope of the present application.

Claims

1. A vehicle state estimation method, characterized in that, The vehicle state estimation method includes: The lateral acceleration, longitudinal acceleration, and yaw rate are obtained through sensors mounted on the target vehicle. Construct the state transfer equation and measurement equation for the square root commutative Kalman filter; Based on the lateral acceleration, longitudinal acceleration, yaw rate, state transfer equation, and measurement equation, the key state parameters of the target vehicle are determined by the square root commensurate Kalman filter method, wherein the key state parameters include longitudinal velocity, lateral velocity, and centroid sideslip angle. Based on the key state parameters, the target vehicle is physically predicted using a nonlinear tire model to obtain the tire lateral force. The vehicle condition is assessed based on the tire lateral force and the center of gravity sideslip angle.

2. The method according to claim 1, characterized in that, The key state parameters of the target vehicle are determined using the square root capacitive Kalman filter method based on the lateral acceleration, longitudinal acceleration, yaw rate, state transfer equation, and measurement equation, including: Construct a discrete nonlinear system using the following formula: in, Let k be the system state vector at time k. Let k be the measurement vector. The state transfer equation for the square root capacitive Kalman filter is... The measurement equation for the square root capacitive Kalman filter is... For the input vector, For system process noise, For measuring noise; The initial state estimate and the square root of the covariance are determined using the following formula: in, for Time-process noise, for Measure noise at all times. for Time-varying covariance matrix for Zero-mean Gaussian white noise at time step For the Kronecker-δ function, For the Cholesky decomposition of the matrix, for Time-process noise, for Measure noise at all times. This is the initial state estimate. Let covariance matrix be the variance matrix. The square root of the covariance; The volume point is determined using the following formula: in, For volume point, Let be the dimension of the state vector. It is a unit volume point. Let i be a unit vector whose i-th element is 1; The predicted state mean and predicted state covariance are calculated using the following formulas: in, Substitute the value of the state transition function into the volume point. To predict the state mean, For predicting the state covariance matrix; The amplified predicted state covariance matrix is ​​calculated using the following formula: in, As a gradually diminishing factor, This is the magnified predicted state covariance matrix; The predicted observation mean and predicted observation covariance are calculated using the following formulas: in, The observed function values ​​at the volume point. To predict the observed mean, The first predicted observation covariance; The measurement noise covariance matrix is ​​updated using the Sage-Husa adaptive filtering algorithm according to the following formula: ； in, The forgetting factor is greater than zero and less than one. This is the adjustment factor output by the fuzzy controller; This is the default noise covariance matrix. To update the measurement noise covariance matrix, These are intermediate parameter values; The second predictive observation covariance is calculated using the following formula: in, The second predicted observation covariance; The updated square root of the covariance is obtained by using the Cholesky update formula: in, For Kalman gain, This is the updated state estimate. The square root of the updated covariance; Based on the lateral acceleration, the longitudinal acceleration, the yaw rate, and the updated square root of the covariance, the key state parameters of the target vehicle are determined using the state transfer equation, the measurement equation, and the square root capacitive Kalman filter method.

3. The method according to claim 2, characterized in that, The state transfer equation is constructed using the following formula: ； in, For the first The state transfer equation at time t, For the first longitudinal velocity at time t, For the first Lateral velocity at time, For the first longitudinal acceleration at time t, For the first Lateral acceleration at time t, For the first yaw rate at time t, This is the preset sampling period.

4. The method according to claim 3, characterized in that, The measurement equation is constructed using the following formula: ； in, For the first Equations for measuring time.

5. The method according to claim 4, characterized in that, Based on the key state parameters, a physical prediction of the target vehicle is performed using a nonlinear tire model, and the tire lateral force is calculated using the following formula: ； in, The lateral force of the front tires. The lateral force of the rear tire. For the lateral stiffness of the front axle, For the rear axle lateral stiffness, The sideslip angle is the angle of the center of mass. 1 is the front wheel steering angle, lf is the distance from the vehicle's center of gravity to the front axle, and lr is the distance from the vehicle's center of gravity to the rear axle.

6. The method according to claim 5, characterized in that, The vehicle condition assessment of the target vehicle based on the tire lateral force and the center of gravity sideslip angle includes: When the lateral force of the tire is greater than the preset maximum grip or the sideslip angle of the center of gravity is greater than the preset limit sideslip angle of the tire, the vehicle condition of the target vehicle is assessed to obtain the vehicle condition assessment result.

7. The method according to claim 6, characterized in that, The method further includes: If the vehicle status assessment result is severely abnormal, a warning sign will be displayed on the target vehicle to remind the user, and the target vehicle's preset protection mechanism will be triggered.

8. A vehicle state estimation system, characterized in that, The vehicle state estimation system includes: The data acquisition module is used to acquire lateral acceleration, longitudinal acceleration, and yaw rate through sensors mounted on the target vehicle. The equation building module is used to construct the state transfer equation and measurement equation for the square root commutative Kalman filter. The key state parameter determination module is used to determine the key state parameters of the target vehicle based on the lateral acceleration, the longitudinal acceleration, the yaw rate, the state transfer equation, and the measurement equation, using the square root capacitive Kalman filter method. The key state parameters include longitudinal velocity, lateral velocity, and centroid sideslip angle. The tire lateral force calculation module is used to perform physical prediction of the target vehicle based on the key state parameters and through a nonlinear tire model to obtain the tire lateral force. The vehicle condition assessment module is used to assess the vehicle condition of the target vehicle based on the tire lateral force and the center of gravity sideslip angle.

9. A vehicle state estimation device, characterized in that, It includes at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enable the at least one control processor to perform a vehicle state estimation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions for causing a computer to perform a vehicle state estimation method as described in any one of claims 1 to 7.

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