Vehicle attitude estimation method, device and equipment and computer readable storage medium
The method uses low-cost IMU sensors and extended Kalman filtering to estimate vehicle pitch angles, addressing the high-cost issue of high-precision IMU reliance, achieving accurate and cost-effective pitch angle estimation.
Patent Information
- Application Number
- CN202510455625.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-11
- Publication Date
- 2025-07-15
AI Technical Summary
In the prior art, the use of high-precision IMU sensors for vehicle body posture angle estimation leads to high cost problems.
By constructing a target longitudinal kinematic model corresponding to the pavement slope angle, a target vehicle longitudinal dynamic model corresponding to the longitudinal acceleration, and a target suspension model corresponding to the vehicle body relative slope angle, combined with an extended Kalman filtering algorithm, a low-cost IMU sensor is used to estimate the vehicle attitude angle.
Real-time and accurate estimation of vehicle attitude angles is achieved, reducing estimation costs and avoiding the use of high-precision IMUs.
Smart Images
Figure CN120308131A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of attitude estimation, and in particular, to a vehicle attitude estimation method, device, equipment and computer-readable storage medium. Background Art
[0002] For vehicle dynamics control, when accelerating or braking, the vehicle may have pitching motion, which will affect the stability and maneuverability of the vehicle; therefore, if the control system can accurately know the current pitching attitude (i.e., the body attitude angle), it can adjust the suspension system or the driving force distribution to optimize the dynamic response of the vehicle, thereby improving the riding comfort and safety. It can be seen that accurate estimation of the body attitude angle is extremely important for vehicle safety control.
[0003] In the related art, usually a high-precision IMU (Inertial Measurement Unit) sensor is used to obtain high-precision parameters (such as high-precision x / y / z axis acceleration and angular velocity information, etc.) in the vehicle driving state to calculate the slope information, so as to estimate the body attitude angle; although this method can improve the estimation accuracy of the body attitude angle, it needs to rely on a high-precision IMU, and a high-precision IMU often means high cost, resulting in an increase in the estimation cost of the body attitude angle invisibly. Therefore, how to reduce the estimation cost of the body attitude angle while accurately realizing the estimation of the body attitude angle is an urgent problem to be solved currently. Summary of the Invention
[0004] The present application provides a vehicle attitude estimation method, device, equipment and computer-readable storage medium, which can solve the technical problem of high cost caused by using a high-precision IMU sensor to realize the estimation of the body attitude angle in the prior art.
[0005] In a first aspect, an embodiment of the present application provides a vehicle attitude estimation method, and the vehicle attitude estimation method includes:
[0006] Respectively construct a target longitudinal kinematic model corresponding to the road surface slope angle, a target vehicle longitudinal dynamics model corresponding to the longitudinal acceleration, and a target suspension model corresponding to the relative slope angle of the vehicle body;
[0007] Define system states, observables and control inputs based on the target longitudinal kinematic model, the target vehicle longitudinal dynamics model and the target suspension model;
[0008] Establish a target state space equation according to the target longitudinal kinematic model, the target vehicle longitudinal dynamics model, the target suspension model, the system states, the observables and the control inputs;
[0009] Perform extended Kalman filter estimation through the target state space equation to output the estimated value of the road surface slope angle and the estimated value of the relative slope angle of the vehicle body;
[0010] Calculate the target vehicle body attitude angle based on the estimated value of the road surface slope angle and the estimated value of the relative slope angle of the vehicle body.
[0011] Combined with the first aspect, in an embodiment, the target longitudinal kinematic model, the target vehicle longitudinal dynamics model, and the target suspension model are respectively:
[0012]
[0013]
[0014] In the formula, represents the road surface slope angle, represents the relative slope angle of the vehicle body, a x represents the longitudinal acceleration measured by the acceleration sensor, v x represents the longitudinal speed of the vehicle, g represents the acceleration due to gravity, F d represents the driving force of the vehicle, m represents the mass of the vehicle, ρ represents the air density, C d represents the wind resistance coefficient of the vehicle, A represents the frontal area of the vehicle, f r represents the rolling resistance coefficient of the vehicle, I p represents the moment of inertia of the vehicle body rotation, C represents the equivalent damping of the pitch angle of the vehicle body, K represents the equivalent stiffness of the pitch angle of the vehicle body, and h represents the height of the vehicle's center of mass from the ground.
[0015] Combined with the first aspect, in an embodiment, the system state The observed quantity z = [v x a x T and the control input u = [F d f r T .
[0016] Combined with the first aspect, in an embodiment, the establishment of the target state space equation according to the target longitudinal kinematic model, the target vehicle longitudinal dynamics model, the target suspension model, the system state, the observed quantity, and the control input includes:
[0017] Establish a nonlinear state space equation based on the target suspension model;
[0018] Perform linearization and discretization processing on the nonlinear state space equation to generate an initial state space equation;
[0019] Generate a target state process matrix and a target observation matrix according to the target longitudinal kinematic model, the target vehicle longitudinal dynamic model, the system state, the observed quantity, the control input, and the initial state space equation;
[0020] Obtain a target state space equation based on the target state process matrix, the target observation matrix, and the initial state space equation.
[0021] Combined with the first aspect, in an implementation manner, the target state process matrix A k and the target input matrix B k are respectively expressed as:
[0022]
[0023] In the formula, v x,k-1 represents the longitudinal speed corresponding to the (k - 1)-th moment, g represents the acceleration due to gravity, m represents the mass of the vehicle, ρ represents the air density, C d represents the wind resistance coefficient of the vehicle, A represents the frontal area of the vehicle, C represents the equivalent damping of the body pitch angle, K represents the equivalent stiffness of the body pitch angle, h represents the height of the vehicle's center of mass from the ground, T s represents the sampling time.
[0024] In the second aspect, an embodiment of the present application provides a vehicle attitude estimation device, and the vehicle attitude estimation device includes:
[0025] A model construction module, which is used to respectively construct a target longitudinal kinematic model corresponding to the road surface slope angle, a target vehicle longitudinal dynamic model corresponding to the longitudinal acceleration, and a target suspension model corresponding to the body relative slope angle;
[0026] A variable definition module, which is used to define the system state, the observed quantity, and the control input based on the target longitudinal kinematic model, the target vehicle longitudinal dynamic model, and the target suspension model;
[0027] An equation establishment module, which is used to establish a target state space equation according to the target longitudinal kinematic model, the target vehicle longitudinal dynamic model, the target suspension model, the system state, the observed quantity, and the control input;
[0028] An attitude estimation module, which is used to perform extended Kalman filter estimation through the target state space equation to output an estimated value of the road surface slope angle and an estimated value of the body relative slope angle; calculate a target body attitude angle based on the estimated value of the road surface slope angle and the estimated value of the body relative slope angle.
[0029] Combined with the second aspect, in an implementation manner, the target longitudinal kinematic model, the target vehicle longitudinal dynamic model, and the target suspension model are respectively:
[0030]
[0031]
[0032] In the formula, represents the road surface slope angle, represents the body relative slope angle, a x represents the longitudinal acceleration measured by the acceleration sensor, v x represents the longitudinal speed of the vehicle, g represents the acceleration due to gravity, F d represents the driving force of the vehicle, m represents the mass of the vehicle, ρ represents the air density, C d represents the wind resistance coefficient of the vehicle, A represents the frontal area of the vehicle, f r represents the rolling resistance coefficient of the vehicle, I p represents the moment of inertia of the body attitude rotation, C represents the equivalent damping of the body pitch angle, K represents the equivalent stiffness of the body pitch angle, and h represents the height of the vehicle's center of mass from the ground.
[0033] Combined with the second aspect, in an embodiment, the system state The observed quantity z = [v x a x T and the control input u = [F d f r T .
[0034] Combined with the second aspect, in an embodiment, the equation establishment module is specifically configured to:
[0035] Establish a nonlinear state space equation based on the target suspension model;
[0036] Perform linearization and discretization processing on the nonlinear state space equation to generate an initial state space equation;
[0037] Generate a target state process matrix and a target observation matrix according to the target longitudinal kinematic model, the target vehicle longitudinal dynamic model, the system state, the observed quantity, the control input, and the initial state space equation;
[0038] Obtain a target state space equation based on the target state process matrix, the target observation matrix, and the initial state space equation.
[0039] Combined with the second aspect, in an embodiment, the target state process matrix A k and the target input matrix B k The expressions of are respectively:
[0040]
[0041] In the formula, v x,k-1 represents the longitudinal speed corresponding to the (k - 1)th moment, g represents the acceleration due to gravity, m represents the mass of the vehicle, ρ represents the air density, C d represents the drag coefficient of the vehicle, A represents the frontal area of the vehicle, C represents the equivalent damping of the body pitch angle, K represents the equivalent stiffness of the body pitch angle, h represents the height of the vehicle's center of mass from the ground, T s represents the sampling time.
[0042] In a third aspect, an embodiment of the present application provides a vehicle attitude estimation device, which includes a processor, a memory, and a vehicle attitude estimation program stored on the memory and executable by the processor. When the vehicle attitude estimation program is executed by the processor, the steps of the vehicle attitude estimation method as described above are implemented.
[0043] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a vehicle attitude estimation program is stored. When the vehicle attitude estimation program is executed by a processor, the steps of the vehicle attitude estimation method as described above are implemented.
[0044] The beneficial effects brought by the technical solution provided by the embodiment of the present application include:
[0045] By respectively constructing a target longitudinal kinematic model corresponding to the road surface slope angle, a target vehicle longitudinal dynamics model corresponding to the longitudinal acceleration, and a target suspension model corresponding to the body relative slope angle; then defining the system state, observation quantity, and control input based on the target longitudinal kinematic model, the target vehicle longitudinal dynamics model, and the target suspension model, and establishing a target state space equation according to the target longitudinal kinematic model, the target vehicle longitudinal dynamics model, the target suspension model, the system state, the observation quantity, and the control input; then performing extended Kalman filter estimation through the target state space equation to output the estimated value of the road surface slope angle and the estimated value of the body relative slope angle, and finally calculating the target body attitude angle based on the estimated value of the road surface slope angle and the estimated value of the body relative slope angle. The present application constructs a state space equation through the vehicle longitudinal model and the suspension model and performs extended Kalman filter processing to realize the real-time and accurate estimation of the body attitude angle, without calculating the road surface slope angle by collecting high-precision parameters through a high-precision IMU, thereby effectively reducing the estimation cost of the body attitude angle. Description of the Drawings
[0046] Figure 1 It is a schematic flowchart of an embodiment of the vehicle attitude estimation method of the present application;
[0047] Figure 2It is a schematic diagram of the vehicle body attitude angle involved in the solution of the embodiment of the present application;
[0048] Figure 3 It is a schematic diagram of the overall process of vehicle attitude estimation involved in the solution of the embodiment of the present application;
[0049] Figure 4 It is a schematic diagram of the Kalman filter principle involved in the solution of the embodiment of the present application;
[0050] Figure 5 It is a schematic diagram of the hardware structure of the vehicle attitude estimation device involved in the solution of the embodiment of the present application. Detailed implementation manners
[0051] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.
[0052] To make the purpose, technical solution and advantages of the present application clearer, the following will further describe the embodiments of the present application in detail with reference to the accompanying drawings.
[0053] In a first aspect, an embodiment of the present application provides a vehicle attitude estimation method.
[0054] In one embodiment, referring to Figure 1 , Figure 1 It is a schematic diagram of the process of the embodiment of the vehicle attitude estimation method of the present application. As Figure 1 shown, the vehicle attitude estimation method includes:
[0055] Step S10: respectively construct a target longitudinal kinematic model corresponding to the road surface slope angle, a target vehicle longitudinal dynamics model corresponding to the longitudinal acceleration, and a target suspension model corresponding to the relative slope angle of the vehicle body; wherein, the target longitudinal kinematic model, the target vehicle longitudinal dynamics model, and the target suspension model are respectively:
[0056]
[0057]
[0058] In the formula, represents the road surface slope angle, represents the relative slope angle of the vehicle body, a x represents the longitudinal acceleration measured by the acceleration sensor, v xrepresents the longitudinal velocity of the vehicle, g represents the acceleration due to gravity, and F d represents the driving force of the vehicle, m represents the mass of the vehicle, ρ represents the air density, C d represents the vehicle's drag coefficient, A represents the vehicle's frontal area, and f r Indicates the vehicle rolling resistance coefficient, I p represents the body posture moment of inertia, C represents the equivalent damping of the body pitch angle, K represents the equivalent stiffness of the body pitch angle, and h represents the height of the vehicle's center of mass from the ground.
[0059] By way of example, it should be understood that, currently, a high-precision IMU is usually required to obtain high-precision parameters in the vehicle's driving state to calculate slope information in order to estimate the vehicle body attitude angle, which invisibly increases the estimation cost of the vehicle body attitude angle; and this embodiment adopts a low-cost IMU sensor in combination with an extended Kalman filter algorithm to achieve real-time estimation of the vehicle attitude angle, that is, when only the vehicle's x-axis acceleration (i.e., longitudinal acceleration) needs to be obtained, an extended Kalman filter is used to simultaneously establish a vehicle suspension model to construct a state-space equation to achieve real-time estimation of the vehicle body attitude angle, so as to reduce the estimation cost of the vehicle body attitude angle while accurately realizing the estimation of the vehicle body attitude angle.
[0060] For details, see Figure 2 As shown in the figure, the body attitude angle θ is mainly composed of two parts: the road slope angle caused by the road slope and the relative slope angle of the vehicle body caused by the transfer of vehicle load Therefore, see Figure 3 As shown, the expression of the actual vehicle body posture angle θ can be constructed as:
[0061] In this embodiment, the road slope angle is calculated by the following target longitudinal kinematic model:
[0062] In this embodiment, the road slope angle As the initial value, it is corrected by the extended Kalman filter. Therefore, the longitudinal acceleration a in this embodiment is x It can be measured by a non-high-precision acceleration sensor instead of a high-precision IMU sensor, which can effectively reduce the estimation cost.
[0063] Understandably, see Figure 3 As shown, the longitudinal acceleration a x The corresponding expression of the target vehicle longitudinal dynamics model is:
[0064]
[0065] It should be noted that the second term on the right side of the formula (i.e., ) is the air resistance and the third term (i.e., mgf r ) is the rolling resistance.
[0066] In addition, as shown in Figure 3 , the expression corresponding to the relative body slope angle caused by vehicle load transfer is:
[0067]
[0068] In the formula, c f and c r represent the damping coefficients of the front and rear suspensions respectively, k f and k r represent the equivalent stiffnesses of the front and rear suspensions respectively, F zf and F zr represent the vertical loads of the front and rear wheels respectively, l f and l r represent the distances from the front axle and the rear axle to the center of mass respectively, and l = l f + l r , where l represents the wheelbase of the vehicle.
[0069] It should be understood that the vertical loads of the front and rear wheels consist of two parts, static and dynamic. The static component is only related to fixed geometric parameters such as the position of the center of mass, while the dynamic component is affected by the vehicle acceleration. That is, when the vehicle accelerates and decelerates suddenly, the deviation between F zf and F zr will become larger, resulting in the phenomenon of the vehicle pitching forward or backward. Among them, as shown in Figure 2 , based on the force analysis and dynamic model of the vehicle, the calculation formulas for the vertical loads F zf and F zr are as follows:
[0070]
[0071] Substituting Equation (4) into Equation (3), the expression corresponding to the difference between the body attitude angle and the road surface slope angle can be obtained:
[0072]
[0073] Simplifying Equation (5) gives:
[0074]
[0075] Since l = l f + l r , Equation (6) can be further simplified to obtain:
[0076]
[0077] Based on this, by combining Equation (1) to organize Equation (7), the target suspension model corresponding to the relative slope angle of the vehicle body can be finally obtained as:
[0078]
[0079] So far, the construction of the target longitudinal kinematic model corresponding to the road surface slope angle, the target vehicle longitudinal dynamics model corresponding to the longitudinal acceleration, and the target suspension model corresponding to the relative slope angle of the vehicle body has been completed.
[0080] Step S20: Define the system state, the observed quantity, and the control input based on the target longitudinal kinematic model, the target vehicle longitudinal dynamics model, and the target suspension model; where the system state The observed quantity z = [v x a x T and the control input u = [F d f r T .
[0081] Exemplarily, in this embodiment, the real-time estimation of the vehicle attitude angle will be realized by the extended Kalman filter algorithm. Therefore, it is first necessary to define the system state, the observed quantity, and the control input in the extended Kalman filter algorithm; specifically, in this embodiment, the system state will be defined by combining the target longitudinal kinematic model, the target vehicle longitudinal dynamics model, and the target suspension model as The observed quantity is z = [v x a x T and the control input is u = [F d f r T .
[0082] Step S30: Establish a target state space equation according to the target longitudinal kinematic model, the target vehicle longitudinal dynamics model, the target suspension model, the system state, the observed quantity, and the control input.
[0083] Exemplarily, in this embodiment, the target longitudinal kinematic model, the target vehicle longitudinal dynamics model, and the target suspension model will be used as the basic models, and with the system state The observed quantity z = [v x a x T and the control input u = [F d f r T As an input, a target state space equation corresponding to the extended Kalman filter algorithm is constructed and extended Kalman filter estimation is performed to achieve accurate estimation of the vehicle body attitude angle. It should be understood that the vehicle needs to travel within the slope range it can bear, and within the slope range it can bear, and The difference is very small. Therefore, for the convenience of calculation and analysis, in subsequent Kalman filter processing, it is preferable to take
[0084] Further, in one embodiment, establishing the target state space equation according to the target longitudinal kinematic model, the target vehicle longitudinal dynamics model, the target suspension model, the system state, the observed quantity, and the control input includes:
[0085] Establishing a nonlinear state space equation based on the target suspension model;
[0086] Performing linearization and discretization processing on the nonlinear state space equation to generate an initial state space equation;
[0087] Generating a target state process matrix and a target observation matrix according to the target longitudinal kinematic model, the target vehicle longitudinal dynamics model, the system state, the observed quantity, the control input, and the initial state space equation;
[0088] Obtaining the target state space equation based on the target state process matrix, the target observation matrix, and the initial state space equation.
[0089] Among them, the target state process matrix A k and the target input matrix B k The expressions are respectively:
[0090]
[0091] In the formula, v x,k-1 represents the longitudinal speed corresponding to the (k - 1)th moment, g represents the acceleration due to gravity, m represents the mass of the vehicle, ρ represents the air density, C d represents the aerodynamic drag coefficient of the vehicle, A represents the frontal area of the vehicle, C represents the equivalent damping of the vehicle body pitch angle, K represents the equivalent stiffness of the vehicle body pitch angle, h represents the height of the vehicle's center of mass from the ground, T S represents the sampling time.
[0092] Exemplarily, it should be understood that the aerodynamic drag is nonlinear, that is, the target suspension model corresponding to Equation (8) is a nonlinear system. Therefore, it is necessary to linearize the model in this embodiment to estimate the vehicle body attitude angle using the standard Kalman filter. Among them, the nonlinear system state equation and its observation equation can usually be expressed as:
[0093] x k = f(x k-1 , u k-1 , w k )
[0094] z k = h(x k , v k )
[0095] In the formula, x k represents the system state at the k-th moment; x k-1 represents the system state at the (k - 1)-th moment; u k-1 represents the system control input at the (k - 1)-th moment; z k represents the system observation at the k-th moment; w k and v k represent the process noise and measurement noise respectively, where the process noise w k and the measurement noise v k conform to the Gaussian distribution.
[0096] Therefore, linearize the target suspension model corresponding to Equation (8), that is, achieve system linearization by performing a first-order Taylor expansion of the nonlinear system at its operating point; then perform discretization to generate the following initial state-space equation:
[0097]
[0098] In the formula, A k represents the state process matrix, B k represents the input matrix, H k represents the observation matrix, W k represents the process noise matrix, V k represents the measurement noise matrix. It should be understood that the state process matrix A k , the observation matrix H k , the process noise matrix W k and the measurement noise matrix V k are all Jacobian matrices and can be calculated respectively through the following formulas:
[0099]
[0100] Among them, for the observation matrix H k , since the road surface slope angle and the relative body slope angle cannot be observed, therefore set these two to 0, and perform a setting of 1 for the longitudinal velocity v x and the longitudinal acceleration a x to obtain the following expression of the target observation matrix H k :
[0101]
[0102] For the process noise matrix W k and the measurement noise matrix V k For example, the target process noise matrix W can be customized according to actual needs. k and the target measurement noise matrix V k Set to:
[0103]
[0104] For the state process matrix A k and the input matrix B k For , it is necessary to combine equations (1), (2) and (9) and convert the system state Observation quantity z = [v x a x ] T and control input u=[F d f r ] T As input, to solve the target state process matrix A as shown below k and the target input matrix B k The expression is:
[0105]
[0106] Finally, the target state process matrix A k , target input matrix B k , target observation matrix H k 、The target process noise matrix W k and the target measurement noise matrix V k Substituting into equation (9) we can obtain the target state space equation.
[0107] Step S40: performing extended Kalman filter estimation through the target state space equation to output a road surface slope angle estimation value and a vehicle body relative slope angle estimation value.
[0108] Exemplarily, it should be understood that the extended Kalman filter equation algorithm generally includes a prediction part and an update part, as follows:
[0109] State prediction equation:
[0110]
[0111] State covariance prediction equation:
[0112]
[0113] Kalman gain calculation equation:
[0114]
[0115] State update equation:
[0116]
[0117] Covariance update equation:
[0118]
[0119] Wherein, represents the state prediction at the k-th moment; represents the state estimation at the k-th moment; represents the state estimation at the (k - 1)-th moment; represents the state covariance prediction at the k-th moment; P k represents the state covariance estimation at the k-th moment; P k-1 represents the state covariance estimation at the (k - 1)-th moment; K k represents the Kalman gain at the k-th moment; z k represents the observation output at the k-th moment; Q k-1 represents the process noise variance at the (k - 1)-th moment; R k represents the observation noise variance at the k-th moment.
[0120] Based on this, substitute the target state space equation, the target state process matrix, the target observation matrix, the target process noise matrix, and the target measurement noise matrix into the above equations, and use the system state The observed quantity z = [v x a x T and the control input u = [F d f r T as inputs, and perform iterative calculations of Kalman filter prediction, Kalman gain, and update according to the working principle of the Kalman filter shown in Figure 4 until the final estimated values of the road surface slope angle and the vehicle body relative slope angle are output. It should be noted that the implementation method and working principle of the Kalman filter are common knowledge in the art, so for the sake of simplicity of description, they will not be elaborated here; wherein, Figure 4 the physical objects in include the target longitudinal kinematic model, the target vehicle longitudinal dynamics model, and the target suspension model.
[0121] Step S50: Calculate the target vehicle body attitude angle based on the estimated road surface slope angle and the estimated vehicle body relative slope angle.
[0122] Exemplarily, in this embodiment, after the extended Kalman filter processes to output accurate estimated values of the road surface slope angle and the vehicle body relative slope angle, the accurate target vehicle body attitude angle can be obtained by summing the estimated value of the road surface slope angle and the estimated value of the vehicle body relative slope angle, so as to realize real-time and accurate estimation of the vehicle body attitude angle, without the need to calculate the road surface slope angle by collecting high-precision parameters through a high-precision IMU, thereby effectively reducing the estimation cost of the vehicle body attitude angle. It can be seen that in this embodiment, only by observing the output result in real time through a given input, the current actual vehicle body attitude angle can be obtained.
[0123] In summary, this embodiment provides a vehicle attitude estimation method that comprehensively considers load transfer and ramp estimation, so as to estimate the vehicle body attitude angle by using a low-cost IMU sensor, that is, when only the longitudinal acceleration of the vehicle needs to be obtained, the longitudinal kinematic model, the vehicle longitudinal dynamic model, and the vehicle suspension model are established to construct the state space equation to realize real-time estimation of the vehicle pitch motion, so that accurate and rapid identification of the vehicle attitude can be realized under working conditions such as complex ramps and sudden acceleration and deceleration of the vehicle.
[0124] In a second aspect, the embodiments of the present application also provide a vehicle attitude estimation device.
[0125] In one embodiment, the vehicle attitude estimation device includes:
[0126] A model construction module, which is used to respectively construct a target longitudinal kinematic model corresponding to the road surface slope angle, a target vehicle longitudinal dynamic model corresponding to the longitudinal acceleration, and a target suspension model corresponding to the vehicle body relative slope angle;
[0127] A variable definition module, which is used to define the system state, the observed quantity, and the control input based on the target longitudinal kinematic model, the target vehicle longitudinal dynamic model, and the target suspension model;
[0128] An equation establishment module, which is used to establish a target state space equation according to the target longitudinal kinematic model, the target vehicle longitudinal dynamic model, the target suspension model, the system state, the observed quantity, and the control input;
[0129] An attitude estimation module, which is used to perform extended Kalman filter estimation through the target state space equation to output an estimated value of the road surface slope angle and an estimated value of the vehicle body relative slope angle; calculate the target vehicle body attitude angle based on the estimated value of the road surface slope angle and the estimated value of the vehicle body relative slope angle.
[0130] Further, in one embodiment, the target longitudinal kinematic model, the target vehicle longitudinal dynamic model, and the target suspension model are respectively:
[0131]
[0132]
[0133]
[0134] In the formula, represents the road surface slope angle, represents the relative slope angle of the vehicle body, a x represents the longitudinal acceleration measured by the acceleration sensor, v x represents the longitudinal speed of the vehicle, g represents the acceleration due to gravity, F d represents the driving force of the vehicle, m represents the mass of the vehicle, ρ represents the air density, C d represents the wind resistance coefficient of the vehicle, A represents the frontal area of the vehicle, f r represents the rolling resistance coefficient of the vehicle, I p represents the moment of inertia of the vehicle body attitude, C represents the equivalent damping of the vehicle body pitch angle, K represents the equivalent stiffness of the vehicle body pitch angle, and h represents the height of the vehicle's center of mass from the ground.
[0135] Further, in one embodiment, the system state observed quantity z = [v x a x T and control input u = [F d f r T .
[0136] Further, in one embodiment, the equation establishing module is specifically configured to:
[0137] Establish a nonlinear state space equation based on the target suspension model;
[0138] Linearize and discretize the nonlinear state space equation to generate an initial state space equation;
[0139] Generate a target state transition matrix and a target observation matrix according to the target longitudinal kinematic model, the target vehicle longitudinal dynamic model, the system state, the observed quantity, the control input, and the initial state space equation;
[0140] Obtain a target state space equation based on the target state transition matrix, the target observation matrix, and the initial state space equation.
[0141] Further, in one embodiment, the expressions of the target state transition matrix A k and the target input matrix B k are respectively:
[0142]
[0143] In the formula, v x,k-1 represents the longitudinal speed corresponding to the (k - 1)-th moment, g represents the acceleration due to gravity, m represents the mass of the vehicle, ρ represents the air density, C d represents the aerodynamic drag coefficient of the vehicle, A represents the frontal area of the vehicle, C represents the equivalent damping of the body pitch angle, K represents the equivalent stiffness of the body pitch angle, h represents the height of the vehicle's center of mass from the ground, T S represents the sampling time.
[0144] Among them, the functional implementation of each module in the above vehicle attitude estimation device corresponds to each step in the above vehicle attitude estimation method embodiment, and its functions and implementation processes will not be elaborated here one by one.
[0145] In a third aspect, an embodiment of the present application provides a vehicle attitude estimation device, and the vehicle attitude estimation device may be a device with data processing functions such as a personal computer (PC), a notebook computer, a server, etc.
[0146] Referring to Figure 5 , Figure 5 is a schematic diagram of the hardware structure of the vehicle attitude estimation device involved in the embodiment of the present application. In the embodiment of the present application, the vehicle attitude estimation device may include a processor, a memory, a communication interface, and a communication bus.
[0147] Among them, the communication bus can be of any type and is used to interconnect the processor, the memory, and the communication interface.
[0148] The communication interface includes interfaces such as input / output (I / O) interfaces, physical interfaces, and logical interfaces for realizing the interconnection of components inside the vehicle attitude estimation device, and interfaces for realizing the interconnection of the vehicle attitude estimation device with other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, an optical fiber interface, an ATM interface, etc.; the user device can be a display screen (Display), a keyboard (Keyboard), etc.
[0149] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical memory, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.
[0150] The processor can be a general-purpose processor, which can call the vehicle attitude estimation program stored in the memory and execute the vehicle attitude estimation method provided by the embodiments of the present application. For example, the general-purpose processor can be a central processing unit (CPU). Among them, the method executed when the vehicle attitude estimation program is called can refer to the various embodiments of the vehicle attitude estimation method of the present application, which will not be elaborated here.
[0151] Those skilled in the art can understand that Figure 5 the hardware structure shown in does not constitute a limitation to the present application, and may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.
[0152] Fourthly, the embodiments of the present application further provide a computer-readable storage medium.
[0153] The vehicle attitude estimation program is stored on the readable storage medium of the present application. When the vehicle attitude estimation program is executed by a processor, the steps of the vehicle attitude estimation method as described above are implemented.
[0154] Among them, the method implemented when the vehicle attitude estimation program is executed can refer to the various embodiments of the vehicle attitude estimation method of the present application, which will not be elaborated here.
[0155] It should be noted that the serial numbers of the above embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.
[0156] The terms "including" and "having" and any variations thereof in the specification, claims and drawings of the present application are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes unlisted steps or units, or optionally further includes other steps or units inherent to these processes, methods, products or devices. The descriptions of the terms "first", "second", "third", etc. are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit that "first", "second" and "third" are different types.
[0157] In the description of the embodiments of the present application, "exemplary", "for example" or "for instance" etc. are used to indicate as an example, illustration or explanation. Any embodiment or design solution described as "exemplary", "for example" or "for instance" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of words such as "exemplary", "for example" or "for instance" is intended to present relevant concepts in a specific manner.
[0158] In the description of the embodiments of the present application, unless otherwise specified, " / " means "or". For example, A / B may mean A or B. The "and / or" in the text is only a relationship describing the associated objects, indicating that there can be three relationships. For example, A and / or B may mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of the present application, "a plurality of" means two or more than two.
[0159] In some of the processes described in the embodiments of the present application, there are a plurality of operations or steps that appear in a specific order. However, it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or may be executed in parallel. The serial numbers of the operations are only used to distinguish the different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.
[0160] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above and includes several instructions for causing a terminal device to execute the methods described in the various embodiments of the present application.
[0161] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.
Claims
1. A vehicle attitude estimation method, characterized in that, The vehicle attitude estimation method includes: Constructing a target longitudinal kinematic model corresponding to the road surface slope angle, a target vehicle longitudinal dynamics model corresponding to the longitudinal acceleration, and a target suspension model corresponding to the relative body slope angle respectively; Defining system states, observables, and control inputs based on the target longitudinal kinematic model, the target vehicle longitudinal dynamics model, and the target suspension model; Establishing a target state space equation according to the target longitudinal kinematic model, the target vehicle longitudinal dynamics model, the target suspension model, system states, observables, and control inputs; Performing extended Kalman filter estimation through the target state space equation to output an estimated value of the road surface slope angle and an estimated value of the relative body slope angle; Calculating a target body attitude angle based on the estimated value of the road surface slope angle and the estimated value of the relative body slope angle.
2. The vehicle attitude estimation method according to claim 1, wherein The target longitudinal kinematic model, the target vehicle longitudinal dynamics model, and the target suspension model are respectively: In the formula, represents the road surface slope angle, represents the relative slope angle of the vehicle body, a x represents the longitudinal acceleration measured by the acceleration sensor, v x represents the longitudinal speed of the vehicle, g represents the acceleration due to gravity, F d represents the driving force of the vehicle, m represents the mass of the vehicle, ρ represents the air density, C d represents the drag coefficient of the vehicle, A represents the frontal area of the vehicle, f r represents the rolling resistance coefficient of the vehicle, I p represents the moment of inertia of the vehicle body attitude rotation, C represents the equivalent damping of the vehicle body pitch angle, K represents the equivalent stiffness of the vehicle body pitch angle, h represents the height of the vehicle's center of mass from the ground.
3. The vehicle attitude estimation method according to claim 2, wherein: System status The observed quantity z = [v x a x T and the control input u = [F d f r T . 4. The vehicle attitude estimation method according to any one of claims 1 to 3, characterized in that The establishing of the target state space equation according to the target longitudinal kinematic model, the target vehicle longitudinal dynamics model, the target suspension model, system states, observables, and control inputs includes: Establishing a nonlinear state space equation based on the target suspension model; Performing linearization and discretization processing on the nonlinear state space equation to generate an initial state space equation; Generating a target state process matrix and a target observation matrix according to the target longitudinal kinematic model, the target vehicle longitudinal dynamics model, system states, observables, control inputs, and the initial state space equation; Obtaining the target state space equation based on the target state process matrix, the target observation matrix, and the initial state space equation.
5. The vehicle attitude estimation method according to claim 4, characterized in that, The target state process matrix A k and the target input matrix B k are respectively expressed as: where v x,k-1 represents the longitudinal speed corresponding to the (k - 1)-th moment, g represents the gravitational acceleration, m represents the mass of the vehicle, ρ represents the air density, C d represents the drag coefficient of the vehicle, A represents the frontal area of the vehicle, C represents the equivalent damping of the body pitch angle, K represents the equivalent stiffness of the body pitch angle, h represents the height of the vehicle's center of mass from the ground, T s represents the sampling time.
6. A vehicle attitude estimation device, characterized in that, The vehicle attitude estimation device includes: A model construction module for constructing a target longitudinal kinematic model corresponding to the road surface slope angle, a target vehicle longitudinal dynamics model corresponding to the longitudinal acceleration, and a target suspension model corresponding to the relative body slope angle respectively; A variable definition module for defining system states, observables, and control inputs based on the target longitudinal kinematic model, the target vehicle longitudinal dynamics model, and the target suspension model; An equation establishment module for establishing a target state space equation according to the target longitudinal kinematic model, the target vehicle longitudinal dynamics model, the target suspension model, system states, observables, and control inputs; An attitude estimation module for performing extended Kalman filter estimation through the target state space equation to output an estimated value of the road surface slope angle and an estimated value of the relative body slope angle; calculating a target body attitude angle based on the estimated value of the road surface slope angle and the estimated value of the relative body slope angle.
7. The vehicle attitude estimation device according to claim 6, characterized in that The target longitudinal kinematic model, the target vehicle longitudinal dynamics model, and the target suspension model are respectively: In the formula, represents the road surface slope angle, represents the relative slope angle of the vehicle body, a x represents the longitudinal acceleration measured by the acceleration sensor, v x represents the longitudinal speed of the vehicle, g represents the acceleration due to gravity, F d represents the driving force of the vehicle, m represents the mass of the vehicle, ρ represents the air density, C d represents the aerodynamic drag coefficient of the vehicle, A represents the frontal area of the vehicle, f r represents the rolling resistance coefficient of the vehicle, I p represents the moment of inertia of the vehicle body attitude rotation, C represents the equivalent damping of the vehicle body pitch angle, K represents the equivalent stiffness of the vehicle body pitch angle, h represents the height of the vehicle's center of mass from the ground.
8. The vehicle attitude estimation device according to claim 7, characterized in that: System status The observed quantity z = [v x a x T and the control input u = [F d f r T . 9. A vehicle attitude estimation device, characterized in that, The vehicle attitude estimation device includes a processor, a memory, and a vehicle attitude estimation program stored on the memory and executable by the processor. When the vehicle attitude estimation program is executed by the processor, the steps of the vehicle attitude estimation method according to any one of claims 1 to 5 are implemented.
10. A computer-readable storage medium, characterized in that, A vehicle attitude estimation program is stored on the computer-readable storage medium. When the vehicle attitude estimation program is executed by a processor, the steps of the vehicle attitude estimation method according to any one of claims 1 to 5 are implemented.