CN113361079B - Road surface flatness detection method, device, equipment and storage medium - Google Patents
Road surface flatness detection method, device, equipment and storage medium Download PDFInfo
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- CN113361079B CN113361079B CN202110550253.6A CN202110550253A CN113361079B CN 113361079 B CN113361079 B CN 113361079B CN 202110550253 A CN202110550253 A CN 202110550253A CN 113361079 B CN113361079 B CN 113361079B
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Abstract
The invention discloses a method, a device and equipment for detecting pavement evenness and a storage medium, and relates to the technical field of pavement quality detection. The method comprises the following steps: acquiring the wheel tire pressure and the vehicle response of a target vehicle; according to the tire pressure of the wheel, utilizing a contact force calibration equation to obtain a tire contact force; and obtaining the road surface evenness by utilizing an inverse operation model according to the vehicle response and the tire contact force, wherein the inverse operation model is established based on an unknown Kalman filtering method. The invention solves the technical problem of lower detection precision of the prior art road flatness indirect detection method, realizes the improvement of the detection precision of the road flatness, and has the advantages of lower cost compared with the laser or radar-based road flatness direct detection method, and meeting the actual use requirement.
Description
Technical Field
The invention relates to the technical field of pavement quality detection, in particular to a pavement flatness detection method, a device, equipment and a storage medium.
Background
With the social requirements on the safety and comfort of road driving, scientific, intelligent, active and preventive road maintenance management is urgently needed to be enhanced, the pavement quality detection is a front guarantee link of a road maintenance decision, and the data accuracy and the updating frequency directly influence the scientificity and timeliness of the road maintenance decision work. The traditional pavement quality detection is mainly carried out in a manual mode, is time-consuming and labor-consuming, and cannot meet the requirement of rapid development of highway detection industry, particularly trunk highway detection industry. Therefore, it is urgent to improve the accuracy, automation, and cost reduction of road surface detection.
The pavement evenness is the most important index in pavement quality detection, and is the most important factor in the evaluation of the pavement full-life cycle state. In the current road flatness detection, the direct road flatness detection method based on laser or radar restricts the realization of large-range low-cost road detection due to higher equipment purchase and maintenance cost, and the existing indirect road flatness detection method has the problems of larger error, lower detection precision and difficulty in meeting the precision requirement of practical application.
Therefore, it is an urgent technical problem to provide a road flatness detection method with higher detection precision and lower use cost.
Disclosure of Invention
The main purposes of the invention are as follows: the invention provides a method, a device and equipment for detecting road flatness and a storage medium, and aims to solve the technical problem that the prior road flatness indirect detection method is low in detection precision.
In order to realize the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a method for detecting road flatness, including the following steps:
acquiring the tire pressure and vehicle response of a target vehicle;
obtaining the tire contact force by utilizing a contact force calibration equation according to the tire pressure of the wheel;
And obtaining the road surface evenness by utilizing an inverse operation model according to the vehicle response and the tire contact force, wherein the inverse operation model is established based on an unknown Kalman filtering method.
Optionally, in the above method for detecting road flatness, the vehicle response includes a vehicle body acceleration response and a wheel acceleration response;
the step of obtaining the vehicle response of the target vehicle specifically includes:
acquiring a vehicle body acceleration response and a wheel acceleration response of the target vehicle, wherein the vehicle body acceleration response comprises a vertical acceleration, a pitch rotation acceleration and a roll rotation acceleration at the center of mass of a vehicle body of the target vehicle, and the wheel acceleration response comprises a vertical acceleration at the connection of each wheel and an axle of the target vehicle.
Optionally, in the above method for detecting road flatness, the step of obtaining a vehicle body acceleration response of the target vehicle specifically includes:
acquiring the vehicle body acceleration of the target vehicle, wherein the vehicle body acceleration comprises the vertical acceleration of the front, the rear, the left side and the right side of the vehicle body of the target vehicle;
and obtaining the vehicle body acceleration response of the target vehicle by using the vehicle geometric relation according to the vehicle body acceleration.
Optionally, in the above method for detecting road flatness, the contact force calibration equation is:
wherein Δ F (t) represents a tire contact force, p0Represents the tire pressure when the target vehicle is stationary, Δ p (t) represents the dynamic tire pressure;
denotes the derivative of Δ p (t), θ1、θ2、θ3Respectively, constants obtained according to a calibration test, wherein the calibration test is performed based on an extended kalman filter method.
Optionally, in the above road flatness detecting method, before the step of obtaining the road flatness using an inverse operation model based on the vehicle response and the tire contact force, the method further includes:
putting the unknown quantity into a standard Kalman filtering equation to obtain an unknown quantity Kalman filtering equation, wherein the standard Kalman filtering equation comprises a system state space equation and a system measurement equation;
and establishing an inverse operation model based on the unknown Kalman filtering method according to the unknown Kalman filtering equation.
Optionally, in the above method for detecting road flatness, the step of obtaining road flatness by using an inverse operation model according to the vehicle response and the tire contact force specifically includes:
obtaining measurement data according to the vehicle body response and the tire contact force;
Predicting based on an unknown Kalman filtering equation according to the system state optimal value and the system covariance matrix optimal value at the current moment, and correspondingly obtaining a system state predicted value and a system covariance matrix predicted value at the next moment;
acquiring Kalman increment according to the system covariance matrix predicted value;
and according to the predicted value of the system state and the Kalman increment, obtaining the road flatness at the current moment by using the measurement data.
Optionally, in the above method for detecting road flatness, after the step of obtaining the road flatness at the current time by using the measurement data according to the predicted value of the system state and the kalman increment, the method further includes:
and correcting the system state predicted value and the system covariance matrix predicted value based on the measured data to obtain a system state optimal value and a system covariance matrix optimal value at the next moment so as to obtain the road surface evenness at the current moment.
In a second aspect, the present invention provides a road flatness detecting apparatus, comprising:
the parameter acquisition module is used for acquiring the wheel tire pressure and the vehicle response of the target vehicle;
the contact force acquisition module is used for acquiring a tire contact force by utilizing a contact force calibration equation according to the tire pressure of the wheel;
And the road flatness acquisition module is used for acquiring the road flatness by utilizing an inverse operation model according to the vehicle response and the tire contact force, wherein the inverse operation model is established based on an unknown Kalman filtering method.
In a third aspect, the present invention provides a road flatness detecting apparatus, where the apparatus includes a memory and a processor, where the memory stores a computer program, and when the computer program is executed by the processor, the apparatus implements the above-mentioned road flatness detecting method.
In a fourth aspect, the present invention provides a storage medium having stored thereon a computer program executable by one or more processors to implement a method of detecting flatness of a road surface as described above.
One or more technical solutions provided by the present invention may have the following advantages or at least achieve the following technical effects:
according to the road flatness detection method, the road flatness detection device, the road flatness detection equipment and the storage medium, after the tire pressure and the vehicle response of a target vehicle are obtained, a tire contact force is obtained by utilizing a contact force calibration equation according to the tire pressure of the wheel; obtaining the road surface evenness by utilizing an inverse operation model according to the vehicle response and the tire contact force, wherein the inverse operation model is established based on an unknown Kalman filtering method; according to the method, the tire contact force is adopted to directly represent the state of the contact interface, the contact interface is used as a link for connecting a target vehicle and a road, the road condition can be directly reflected, the contact interface is used as the input of an inverse operation model, the estimation precision of the inverse operation model can be obviously improved, and the detection precision of the road flatness is improved; meanwhile, an inverse operation model established based on an unknown Kalman filtering method can effectively contain noise of a measured variable to obtain the minimum covariance estimation amount, the detection precision of the pavement evenness can be further improved, and compared with the traditional pavement evenness direct detection method based on laser or radar, the method is lower in cost, and therefore the actual use requirement can be met.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for detecting road flatness according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a vehicle numerical model in step S51 of a road flatness detection method according to an embodiment of the present invention;
fig. 3 is a schematic detailed flowchart of step S60 in the method for detecting road flatness according to an embodiment of the present invention;
FIG. 4 is a comparison of the flatness of a road surface of a first road segment with a laser profiler according to an embodiment of the present invention;
FIG. 5 is a comparison graph of the road flatness of a first road section and a second road section of a laser profiler according to the embodiment of the present invention;
fig. 6 is a functional block diagram of a road flatness detecting apparatus according to a second embodiment of the present invention;
fig. 7 is a detailed functional block diagram of the road flatness acquiring module in fig. 6.
The objects, features and advantages of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that all the directional indicators (such as up, down, left, right, front, and rear … …) in the embodiment of the present invention are only used to explain the relative position relationship between the components, the movement situation, etc. in a specific posture (as shown in the drawing), and if the specific posture is changed, the directional indicator is changed accordingly.
In the present invention, unless otherwise expressly stated or limited, the terms "connected," "secured," and the like are to be construed broadly, and for example, "secured" may be a fixed connection, a removable connection, or an integral part; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium; either internal or interactive relationship, unless expressly defined otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In addition, if there is a description relating to "first", "second", etc. in the embodiments of the present invention, the description of "first", "second", etc. is for descriptive purposes only and is not to be construed as indicating or implying relative importance or to implicitly indicate the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the feature.
It should be noted that the meaning of "and/or" appearing throughout includes three parallel schemes, taking "A and/or B" as an example, and including a scheme, or B scheme, or a scheme satisfied by both A and B. In addition, technical solutions between the embodiments may be combined with each other, but must be based on the realization of the technical solutions by a person skilled in the art, and when the technical solutions are contradictory to each other or cannot be realized, such a combination of technical solutions should be considered to be absent and not to be within the protection scope of the present invention.
In addition, in the following description, suffixes such as "module", "part", or "unit" used to indicate elements are used only for facilitating the description of the present invention, and have no specific meaning in themselves. Thus, "module", "component" or "unit" may be used mixedly.
Analysis of the prior art finds that detection, identification and evaluation of road surface conditions are an important part of road surface maintenance, and the flatness of the road surface is the most important index of the road surface, and is the most important part of the evaluation of the full life cycle state of the road surface. Road Roughness (Road Roughness), which refers to the vertical deviation of a Road surface from an ideal plane, characterizes the flatness of a curve of a longitudinal section of the Road surface. The road surface evenness is one of the most main technical indexes in road surface quality evaluation and road surface construction acceptance, is closely related to driving comfort and safety, and generally adopts an International evenness Index (IRI for short) as a road surface evenness evaluation Index. The road with poor flatness not only influences the driving safety, reduces the driving speed, but also generates noise pollution, increases the oil consumption and the abrasion of parts of the vehicle, and simultaneously accelerates the structural damage of the road and shortens the maintenance period of the road. Therefore, it is important to automatically detect the flatness of the road surface.
At present, the automatic detection of the flatness of the road surface is mainly carried out by computer image recognition methods (such as high-speed cameras), direct distance measurement methods (such as laser and ultrasonic sensors), and indirect measurement methods based on vehicle responses. Among them, indirect measurement based on vehicle response gradually enters academic and market fields because of its advantages of low cost, high speed and high efficiency, and easy use. However, the current indirect measurement method based on vehicle response has some problems, such as: the dynamic response of a vehicle body or wheels is generally adopted as input, and the vehicle body and the wheels are not in direct contact with the road surface because the road surface is connected with the wheels through a suspension system, so that the road surface flatness estimation meeting the practical precision cannot be obtained only by adopting a vehicle body dynamic response method; only based on the method of dynamic response of the wheels, because the wheel mass is small relative to the vehicle body, the wheel response frequency is high, and the method is sensitive to measurement noise, the method can not provide an accurate road flatness estimation result; in addition, the simplified vehicle model and road surface model also affect the identification accuracy of the road surface evenness.
In view of the technical problems that the direct detection method of the road flatness in the prior art has overhigh cost and the indirect detection method has lower detection precision, the invention provides a road flatness detection method, and the general idea is as follows:
acquiring the tire pressure and vehicle response of a target vehicle; according to the tire pressure of the wheel, utilizing a contact force calibration equation to obtain a tire contact force; and obtaining the road surface evenness by utilizing an inverse operation model according to the vehicle response and the tire contact force, wherein the inverse operation model is established based on an unknown Kalman filtering method.
According to the technical scheme, the tire contact force is adopted to directly represent the state of the contact interface, the contact interface is used as a link for connecting a target vehicle and a road, the road condition can be directly reflected, the contact interface is used as the input of an inverse operation model, the estimation precision of the inverse operation model can be obviously improved, and the detection precision of the road flatness is improved; meanwhile, an inverse operation model established based on an unknown Kalman filtering method can effectively contain the noise of a measured variable to obtain the minimum covariance estimator, and the detection precision of the pavement evenness can be further improved to meet the actual use requirement.
Example one
Referring to fig. 1 to 4, a first embodiment of the present invention provides a road flatness detection method, which is applied to an automatic road detection vehicle for detecting the quality of a road, where the automatic road detection vehicle may specifically adopt a two-axle four-wheel independent suspension commercial vehicle, such as a K7 commercial vehicle, a popular T6 kelowei, and toyota praudo vehicle, as a target vehicle in this embodiment. The method comprises the following steps:
step S20: acquiring the tire pressure and vehicle response of a target vehicle;
specifically, the tire pressure of the target vehicle may be obtained by a tire pressure sensor disposed on the wheel, the vehicle response includes a vehicle body acceleration response and a wheel acceleration response, and the vehicle body acceleration response and the wheel acceleration response of the target vehicle are obtained by the acceleration sensor, respectively.
In a specific implementation process, the vertical acceleration of the front, the rear, the left side and the right side of the body of the target vehicle can be obtained by respectively arranging acceleration sensors at the front, the rear, the left side and the right side in the body of the target vehicle; then, the vehicle geometric relation is utilized to obtain the vehicle body acceleration response of the target vehicle, namely the vertical acceleration, the pitching rotation acceleration and the rolling rotation acceleration of the mass center of the vehicle body of the target vehicle; it is also possible to obtain the wheel acceleration response of the target vehicle, that is, the vertical acceleration at the junction of each wheel of the target vehicle and the axle, by providing an acceleration sensor at each of the four wheels of the target vehicle where each wheel is connected to its axle, thereby obtaining the vertical acceleration of the left front wheel, the right front wheel, the left rear wheel, and the right rear wheel of the target vehicle.
Direct measurement tire pressure and vehicle response, after comparing and establishing vehicle model and road surface model, carry out analogue test or simulation test's traditional mode, more simple and convenient, direct, and laminate the in-service use more, can satisfy the actual measurement demand.
Step S40: and obtaining the tire contact force by utilizing a contact force calibration equation according to the tire pressure of the wheel.
Specifically, the obtained tire pressure is directly substituted into a contact force calibration equation, so that the tire contact force can be obtained, and the solution is efficient and convenient.
In the specific implementation process, a contact force calibration equation is obtained by performing a calibration test based on an extended Kalman filtering method, and the contact force calibration equation is as follows:
wherein Δ F (t) represents a tire contact force, p0Represents the tire pressure when the target vehicle is stationary, Δ p (t) represents the dynamic tire pressure;
denotes the derivative of Δ p (t), θ1、θ2、θ3Respectively constants obtained from calibration tests.
When the tire directly contacts the ground, the tire contact force is the most direct and fundamental physical parameter describing the state of a contact interface, is the most direct characteristic quantity of the road condition, is a link connecting two subsystems of a vehicle and a road, and contains response information of the vehicle and the road, so that the tire contact force is used as an input parameter for measuring the road flatness, the estimation precision of the road flatness can be improved, and the detection precision requirement is met.
Step S60: and obtaining the road surface evenness by utilizing an inverse operation model according to the vehicle response and the tire contact force, wherein the inverse operation model is established based on an unknown Kalman filtering method.
Specifically, after an inverse operation model is established by using an unknown Kalman filtering method, vehicle response and tire contact force are used as measurement data, the measurement data are input into the inverse operation model, the road surface flatness is estimated by the inverse operation model, and finally the road surface flatness is output.
In the specific implementation process, because the inverse operation model is established based on an unknown Kalman filtering method, the related unknown Kalman filtering equation mainly comprises two equations, namely a system state space equation and a system measurement equation, firstly, system state prediction and system covariance matrix prediction are carried out according to the system state space equation, a system state optimal value and a system covariance matrix optimal value at the current moment, a system state predicted value and a system covariance matrix predicted value at the next moment are obtained, then, Kalman increment is calculated according to the system covariance matrix predicted value, an unknown quantity, namely pavement evenness, is calculated according to the Kalman increment and the system state predicted value, the pavement evenness at the current moment is obtained, and the pavement evenness is output.
The Kalman filtering method is a very typical unbiased minimum covariance estimator identification algorithm, the minimum mean square error is used as an estimation criterion, high degree of containment and robustness are provided for measurement noise, a random process is described in a state space form, and recursive estimation is provided for a plurality of state variables. In the invention, the unknown Kalman filtering method is obtained by utilizing the advantages of the Kalman filtering method and combining the unknown quantity, and the inverse operation model is established based on the unknown Kalman filtering method, so that the measurement noise, namely the actual measurement error can be effectively contained, the finally output error of the pavement evenness is further reduced, and the detection accuracy of the invention is improved.
The method for detecting the flatness of the pavement provided in this embodiment is described in detail below with reference to the schematic flow chart shown in fig. 1, and the method may specifically include the following steps:
step S20: acquiring the tire pressure and vehicle response of a target vehicle;
specifically, the step S20 may include:
step S21: and acquiring the tire pressure of the target vehicle.
In the specific implementation process, the target vehicle adopts a two-axle four-wheel independent suspension commercial vehicle, the four wheels of the commercial vehicle are respectively provided with a tire pressure sensor, and the tire pressure of the target vehicle is obtained through the tire pressure sensors in the running process of the target vehicle.
In this embodiment, the target vehicle is an automatic road detection vehicle. The inspection vehicle is equipped with sensors based on camera, laser and radar, such as high-speed three-dimensional cameras, laser profilometers and millimeter wave radars, as well as sensors of acoustic technology and vehicle response type, such as directional microphones, acceleration sensors and tire pressure sensors. The automatic road detection vehicle can be applied to detection and data acquisition of in-service roads. In the embodiment, the road automatic detection vehicle runs on the in-service road at the running speed of 40km/h, and field parameter acquisition is carried out to obtain the tire pressure of the wheels.
Step S22: a vehicle response of the target vehicle is obtained.
Specifically, the vehicle response comprises a vehicle body acceleration response and a wheel acceleration response; the step of obtaining the vehicle response of the target vehicle specifically includes:
acquiring a vehicle body acceleration response and a wheel acceleration response of the target vehicle, wherein the vehicle body acceleration response comprises a vertical acceleration, a pitch rotation acceleration and a roll rotation acceleration at the center of mass of a vehicle body of the target vehicle, and the wheel acceleration response comprises a vertical acceleration at the connection of each wheel and an axle of the target vehicle.
In the specific implementation process, the vehicle body acceleration response and the wheel acceleration response of the target vehicle are respectively obtained through the acceleration sensor.
Specifically, the step S22 may include:
step S22.1: and acquiring the vehicle body acceleration response of the target vehicle.
More specifically, the step S22.1 may include:
step S22.1.1: acquiring the vehicle body acceleration of the target vehicle, wherein the vehicle body acceleration comprises the vertical acceleration of the front, the rear, the left side and the right side of the vehicle body of the target vehicle;
in the specific implementation process, the target vehicle mainly comprises a vehicle body and wheels, acceleration sensors are respectively arranged on the front wall, the rear wall, the left wall and the right wall in the vehicle body, specifically, the front wall, the rear wall, the left wall and the right wall of an inner chamber of the vehicle body, and the parameters of the acceleration sensors are obtained, so that the vertical acceleration of the front side, the rear side, the left side and the right side of the vehicle body of the target vehicle can be obtained.
In this embodiment, an acceleration sensor is disposed at the center of the bottom of the four walls of the vehicle body inner chamber of the road automatic detection vehicle, and vertical accelerations of the front, rear, left, and right sides of the vehicle body of the target vehicle, that is, the vehicle body acceleration of the target vehicle, are obtained.
Step S22.1.2: and obtaining a body acceleration response of the target vehicle by utilizing a vehicle geometrical relation according to the body acceleration, wherein the body acceleration response comprises a vertical acceleration, a pitching rotation acceleration and a rolling rotation acceleration at the body mass center of the target vehicle.
In the concrete implementation process, a left front wheel and a right front wheel of a target vehicle are connected through a front axle, a left rear wheel and a right rear wheel of the target vehicle are connected through a rear axle, and the distributed load sizes of the front axle and the rear axle and the mass center position of the target vehicle are determined by directly measuring the distance between the left front wheel, the right front wheel, the left rear wheel and the right rear wheel of the target vehicle and the total mass, the front axle mass and the rear axle mass of the target vehicle. And then, according to the obtained body acceleration of the target vehicle, namely the vertical acceleration of the front side, the rear side, the left side and the right side of the body of the target vehicle, the body acceleration response of the target vehicle, namely the vertical acceleration, the pitching rotation acceleration and the rolling rotation acceleration at the center of mass of the body of the target vehicle are obtained by utilizing the vehicle geometrical relation.
In this embodiment, the position of the center of mass of the vehicle body of the road automatic detection vehicle is calculated according to the vehicle parameters of the target vehicle collected by the road automatic detection vehicle, and then the vertical acceleration, the pitch rotational acceleration and the roll rotational acceleration at the position of the center of mass of the vehicle body are calculated by using the collected vehicle body acceleration.
Step S22.2: and acquiring wheel acceleration response of the target vehicle, wherein the wheel acceleration response comprises the vertical acceleration of the joint of each wheel and an axle of the target vehicle.
In a specific implementation process, acceleration sensors are respectively arranged at the positions where the four wheels of the target vehicle are respectively connected with the axles of the target vehicle, so that the acceleration vertical speeds of the left front wheel, the right front wheel, the left rear wheel and the right rear wheel of the target vehicle are obtained, and the wheel acceleration response of the target vehicle, namely the vertical acceleration of the joints of the wheels and the axles of the target vehicle is obtained.
In this embodiment, acceleration sensors are respectively disposed at a joint of a front left wheel and a front axle, a joint of a front right wheel and the front axle, a joint of a rear left wheel and a rear axle, and a joint of a rear right wheel and a rear axle of the road automatic detection vehicle, so as to obtain vertical accelerations of the front left wheel, the front right wheel, the rear left wheel, and the rear right wheel of the road automatic detection vehicle, and thus obtain a wheel acceleration response of the target vehicle, that is, a vertical acceleration at a joint of each wheel and an axle of the target vehicle.
Direct measurement tire pressure and vehicle response, after comparing and establishing vehicle model and road surface model, carry out analogue test or simulation test's traditional mode, more simple and convenient, direct, and laminate the in-service use more, can satisfy the actual measurement demand.
Step S40: and obtaining the tire contact force by utilizing a contact force calibration equation according to the tire pressure of the wheel.
Specifically, the contact force calibration equation is as follows:
wherein Δ F (t) represents a tire contact force, p0Represents the tire pressure when the target vehicle is stationary, Δ p (t) represents the dynamic tire pressure;
denotes the derivative of Δ p (t), θ1、θ2、θ3Respectively, constants obtained according to a calibration test, wherein the calibration test is performed based on an extended kalman filter method.
In the specific implementation process, a calibration test is carried out based on an extended Kalman filtering method, specifically, external forces with different amplitudes are applied to the tire of a target vehicle, the tire pressure is measured by a tire pressure sensor, and the contact force between the tire and the road surface is measured by the pressure sensor, so that the one-to-one correspondence relationship between the tire pressure of the wheel and the contact force of the tire is established. The one-to-one correspondence relationship between the tire pressure of the wheel and the tire contact force established by the calibration test, i.e. a contact force calibration equation in which the constant theta is1、θ2、θ3The value of (b) is obtained by an extended kalman filter method. The extended Kalman filtering method is an improved Kalman filtering method for solving the nonlinear problem. Since the wheels roll continuously during the actual running of the vehicle, and the contact force between the vehicle and the road surface cannot be obtained by arranging the pressure sensor at the position where the wheels contact the road surface, the required tire contact force is indirectly obtained by directly substituting the tire pressure of the wheels obtained in step S20 into the contact force calibration equation, and the solution is efficient and convenient.
In this embodiment, after the tire pressure of the vehicle wheel is obtained by the road automatic detection vehicle, the tire pressure of the vehicle wheel is calculated through a preset relationship, that is, a contact force calibration equation, so that the tire contact force can be obtained.
When the tire contact force is directly contacted with the ground, the most direct and most fundamental physical parameters describing the contact interface state are ties connecting two subsystems of a vehicle and a road, and can directly represent the road surface condition. Therefore, the tire contact force is used as an input parameter for measuring the road flatness, the estimation precision of the road flatness can be improved, and the detection precision requirement is met. Therefore, there is a clear advantage to having tire contact force as a measurement input.
In one embodiment, before the step S60, the method may further include:
step S50: and establishing an inverse operation model based on an unknown Kalman filtering method.
Specifically, the step S50 may include:
step S51: and putting the unknown quantity into a standard Kalman filtering equation to obtain an unknown Kalman filtering equation, wherein the standard Kalman filtering equation comprises a system state space equation and a system measurement equation.
Specifically, the kalman filtering method is a very typical unbiased minimum covariance estimator identification algorithm, takes the minimum mean square error as an estimation criterion, has high degree of containment and robustness on measurement noise, describes a random process in a state space form, and provides recursive estimation for a plurality of state variables. The standard kalman filter equation is:
Wherein Xn+1Represents the system state at time n + 1, i.e. the next time, XnIndicating the system state at time n, i.e. the current time, FnRepresenting the amount of control of the system at time n, e.g. external load vector, YnRepresenting the measured value at time n, A and B each representing a matrix of system parameters, C and D each representing a matrix of measured parameters, wnAnd vnRepresenting process noise and measurement noise, respectively.
Because the system state space equation in the standard Kalman filtering equation is a typical time course forward calculation process, the condition that all parameters on the right side of the equation are known and unknown quantity in the equation cannot be processed, namely the time course reverse calculation process cannot be realized, the standard Kalman filtering equation is expanded and improved on the basis of a standard Kalman filtering method, and the time course forward calculation is converted into the time course reverse calculation.
In the specific implementation process, a simulation test can be carried out according to a vehicle numerical model of the four-wheel automobile, so that correlation equation derivation of the Kalman filtering method improved into the unknown Kalman filtering method is carried out. In most of the existing problems of axle coupling, road coupling and vehicle characteristic parameter identification, a vehicle numerical model comprises a vehicle body system, a wheel system and a suspension system positioned between the vehicle body system and the wheel system. In the suspension system, the front suspension and the rear suspension adopt the same mode, but the suspension stiffness is different. However, in fact, the front wheel is responsible for steering, it can be assumed that the left and right front wheels are independent from each other, and the left and right rear wheels are connected by the stabilizer bar, and the rear wheel acceleration contains more high frequency components than the front wheel acceleration, which means that the mutual balance and constraint between the rear wheels are better than those of the front wheel, so it cannot be simply assumed that the left and right rear wheels are also independent from each other. Therefore, a more refined vehicle numerical model, especially the correct simulation of the stabilizer bar, is beneficial to improving the identification precision of the inverse operation model. In addition, the rolling effect and the rolling friction of tires and the nonlinearity of a suspension system are not considered in the existing vehicle numerical model, the recognition accuracy is not greatly influenced under the condition that the road surface condition is good, but when the road surface has obvious fluctuation such as deceleration strips, tunnels, serious damage and the like, the rolling effect, the rolling friction and the nonlinearity of the suspension system are considered to have certain influence on the recognition accuracy, so that the method further improves and corrects the vehicle numerical model, and is favorable for improving the recognition accuracy of the road surface flatness.
The vehicle numerical model is a very complex vibration system with multiple degrees of freedom and multiple components, corresponding simplification is needed when a vehicle is simulated from the angle of numerical analysis, and a two-dimensional four-degree-of-freedom half-vehicle model or a three-dimensional seven-degree-of-freedom full-vehicle model can be specifically adopted by combining a multi-rigid-body dynamics theory. In the embodiment, a two-dimensional four-degree-of-freedom half-car model is taken as an example, as shown in fig. 2, which is a schematic structural diagram of the numerical model of the vehicle in the embodiment, the model includes a car body, wheels, and a suspension system connecting the car body and the wheels, the wheels include front wheels and rear wheels, and the suspension system includes front suspension and rear suspension.
In view of the present implementationExample vehicle numerical model, incorporating unknowns
And converting a standard kinetic equation of the vehicle running on the road surface into a system state space equation form so as to meet the form requirement of a Kalman filtering method. The standard kinetic equation is:
wherein the content of the first and second substances,
MVa matrix of the mass of the vehicle is represented,
MV＝diag[mc Ic mw1 mw2]，
wherein diag denotes a diagonalized matrix, mcRepresenting the mass of the vehicle body, IcRepresenting the pitch moment of inertia of the vehicle body, mw1Representing front wheel mass, mw2Representing the rear wheel mass;
CVa vehicle damping matrix is represented that is,
wherein, cw1Indicating front suspension damping, cw2Indicating rear suspension damping,/ 1Indicating the distance of the front wheel from the centre of mass of the body, l2Representing the distance from the rear wheel to the mass center of the vehicle body;
KVa matrix of vehicle stiffness is represented and,
wherein k isw1Representing front suspension stiffness, kw2Representing rear suspension stiffness, kr1Denotes the tire contact stiffness, k, of the front wheelr2Representing the tire contact stiffness of the rear wheel;
uVrepresenting a vehicle displacement vector;
FVrepresenting the vector of the external force acting on the vehicle,
FV＝[-mcg 0 -mw1g -mw2g]T，
wherein m iscRepresenting the mass of the vehicle body, g representing the acceleration of gravity, mw1Representing front wheel mass, mw2Representing the rear wheel mass;
WVrepresenting a contact assignment matrix containing only 0 and 1 for assigning contact degrees of freedom to corresponding vehicle degrees of freedom,
kra matrix of contact stiffness of the tire is represented,
kr＝diag[kr1 kr2]，
wherein diag represents a diagonal matrix;
wherein the content of the first and second substances,
indicating the flatness of the road surface of the front wheel,
indicating the road flatness of the rear wheel.
The standard kinetic equations are typically two-dimensional differential equations that are converted to the Kalman filter equations by first combining
And
and
and uVIt is deformed into:
wherein, I and 0 represent an identity matrix and a zero matrix;
then, considering time step n, the standard kinetic equation is reduced into a one-dimensional state space equation by a two-dimensional differential equation:
Wherein the content of the first and second substances,
Ac、Bcand
are intermediate variables of the system state space equation,
then introducing an Euler method step-by-step integral, and dispersing the one-dimensional state space equation into a form of time steps n +1 and n
Obtaining:
multiplying by dt yields:
and comparing the system state space equation in the standard Kalman filtering equation to obtain a system state space equation containing unknown quantities:
wherein the content of the first and second substances,
A＝I+dtAc，
B＝dtBc，
finally, the unknowns are combined
Respectively putting the unknown quantities into a system measurement equation comprising a system state space equation of the unknown quantities and a standard Kalman filtering equation to obtain an unknown quantity Kalman filtering equation:
wherein, B*A companion matrix, D, representing a system parameter matrix B*A companion matrix representing a measured parameter matrix D.
Step S52: and establishing an inverse operation model based on the unknown Kalman filtering method according to the unknown Kalman filtering equation.
In the present embodiment, an inverse operation model is established based on the unknown kalman filter method from the obtained unknown kalman filter equation to calculate the unknown quantity therein, i.e., the road flatness, from the obtained vehicle body response and the tire contact force.
An unknown quantity, namely pavement evenness, is added to an unknown quantity Kalman filtering method corresponding to the unknown quantity Kalman filtering equation on the basis of a standard Kalman filtering method, and a constraint condition can be established through a least square method, so that the unknown quantity is obtained through equation multiple matrix inversion in an iteration process.
Step S60: and obtaining the road surface evenness by utilizing an inverse operation model according to the vehicle response and the tire contact force, wherein the inverse operation model is established based on an unknown Kalman filtering method.
Specifically, as shown in the detailed flowchart of step S60 in fig. 3, the step S60 may include:
step S61: and obtaining measurement data according to the vehicle body response and the tire contact force.
In the embodied process, the vehicle response obtained at step S20 and the tire contact force obtained at step S40 are regarded as the measurement data Yn。
Step S62: and predicting based on an unknown Kalman filtering equation according to the system state optimal value and the system covariance matrix optimal value at the current moment, and correspondingly obtaining a system state predicted value and a system covariance matrix predicted value at the next moment.
In the concrete implementation process, the optimal value X is determined according to the system state at the current moment, namely n momentn|nPredicting based on a system state space equation in an unknown Kalman filtering equation to obtain a system state predicted value X at the next momentn+1|n：
In n +1| n, the left side of | is a time step corresponding to a prediction process of an unknown Kalman filtering method, and the right side of | is a time step corresponding to a correction process;
According to the optimal value P of the covariance matrix of the system at the current moment, namely n momentn|nPredicting based on a system state space equation in an unknown Kalman filtering equation to obtain a system covariance matrix predicted value Pn+1|n：
Pn+1|n＝APn|nAT+Q，
Wherein, ATA transposed matrix representing the system parameter matrix A, Q representing the process noise wnThe covariance matrix of (c).
Step S63: and acquiring Kalman increment according to the system covariance matrix predicted value.
In the specific implementation process, the value P is predicted according to the covariance matrix of the systemn+1|nCalculating Kalman increment Kn+1The calculation formula is as follows:
Kn+1＝Pn+1|n·CT[C·Pn+1|n·CT+R]-1，
wherein, CTA transposed matrix representing a measurement parameter matrix C, R representing a measurement noise vnThe covariance matrix of (2).
Step S64: and obtaining the road surface evenness at the current moment by using the measurement data according to the system state predicted value and the Kalman increment.
In the implementation process, K is increased according to Kalmann+1Obtaining an intermediate variable Sn+1：
Sn+1＝[D*T·R-1(I-C·Kn+1)D*]-1，
Wherein D is*TRepresents D*I denotes an identity matrix;
then according to the system state predicted value X at the next momentn+1|nAnd an intermediate variable Sn+1And measurement data Yn+1Calculating unknowns, i.e. flatness of the road at the current moment
In this embodiment, after the measurement data is input into the inverse operation model, the specific unknown quantity is obtained through the above steps and is used as the output of the inverse operation model, so as to obtain the detection result of the road surface evenness.
The inverse operation model established by the unknown Kalman filtering method can effectively contain the measurement noise, namely the actual measurement error, further reduce the error of the finally output pavement evenness and improve the detection accuracy. In the process of identifying the road flatness, the inverse operation model fully reflects the effective quantity, the combination rule and the correlation relation among different variables under the condition that a plurality of variables such as tire contact force, vehicle response and the like are known.
In an embodiment, after the step S64, the method further includes a step of updating the inverse operation model, which specifically includes:
step S65: and correcting the system state predicted value and the system covariance matrix predicted value based on the measured data to obtain a system state optimal value and a system covariance matrix optimal value at the current moment so as to obtain the road flatness at the current moment.
Specifically, the inverse operation model is corrected and updated based on the measurement data at the current time, so that the accuracy of the inverse operation model is higher when the road flatness is calculated according to the input measurement data subsequently.
In the implementation process, the system shape according to the next momentState prediction value Xn+1|nAnd the unknowns obtained in step S64 
Obtaining the optimal value X of the system state at the next momentn+1|n+1：
And according to the system covariance matrix predicted value P at the next momentn+1|nAnd the unknowns obtained in step S64
Obtaining the optimal value P of the covariance matrix of the system at the next momentn+1|n+1：
Pn+1|n+1＝I+Kn+1D*Sn+1D*TR-1C)(I-Kn+1·C)Pn+1|n，
And preparing for iterative solution of unknowns at subsequent time instants.
The inverse operation model is continuously corrected and updated, so that the estimation of the road flatness is more and more accurate, and the technical effect of improving the detection precision of the road flatness is realized.
In this embodiment, the tests are performed on the first road section and the second road section through the above steps, and the road surface flatness (IRI) obtained by the tire contact force of the left front wheel of the vehicle according to the road automatic detection and the vehicle response is compared with the road surface flatness (IRI) obtained by the direct measurement of the laser profilometer arranged on the left front wheel of the vehicle according to the road automatic detection, as shown in fig. 4, a road surface flatness comparison graph of the first road section with the laser profilometer according to this embodiment is shown in fig. 5, a road surface flatness comparison graph of the second road section with the laser profilometer according to this embodiment is shown in the horizontal axis, the road mileage is shown in km, and the vertical axis shows the road surface flatness in m/km. As can be seen from the figure, the detection results of the two methods are highly consistent, which is enough to illustrate the realizability and practicability of the method.
According to the method for detecting the road flatness, after the tire pressure and the vehicle response of a target vehicle are obtained, a tire contact force is obtained by utilizing a contact force calibration equation according to the tire pressure of the wheel; then according to the vehicle response and the tire contact force, obtaining the road surface evenness by utilizing an inverse operation model, wherein the inverse operation model is established based on an unknown Kalman filtering method; according to the method, the tire contact force is adopted to directly represent the state of the contact interface, the contact interface is used as a link for connecting a target vehicle and a road, the road condition can be directly reflected, the link is used as the input of the inverse operation model, the estimation precision of the inverse operation model can be obviously improved, and the detection precision of the road flatness is improved; meanwhile, an inverse operation model established based on an unknown Kalman filtering method can effectively contain the noise of a measured variable to obtain the minimum covariance estimator, and the detection precision of the pavement evenness can be further improved to meet the actual use requirement.
The method of the embodiment provides scientific basis and theoretical method for upgrading of road automatic detection technology, provides advanced, accurate, efficient and practical technical means for collecting road surface information, comprehensively assists in realizing accuracy, automation, high efficiency and low cost of road surface detection, promotes development of road surface state recognition and road maintenance industry, and even has important significance for improving road maintenance departments in road surface disaster disease risk management and prevention capability and guaranteeing sustainable development of road construction industry.
Example two
Based on the same inventive concept, referring to fig. 6, a second embodiment of the present invention provides a road flatness detection apparatus, which is described in detail with reference to a functional block diagram shown in fig. 6, and includes:
the parameter acquisition module is used for acquiring the wheel tire pressure and the vehicle response of the target vehicle;
the contact force acquisition module is used for acquiring a tire contact force by utilizing a contact force calibration equation according to the tire pressure of the wheel;
and the road flatness acquisition module is used for acquiring the road flatness by utilizing an inverse operation model according to the vehicle response and the tire contact force, wherein the inverse operation model is established based on an unknown Kalman filtering method.
Specifically, the parameter obtaining module may include:
the wheel tire pressure acquisition submodule is used for acquiring the wheel tire pressure of the target vehicle;
and the vehicle response acquisition submodule is used for acquiring the vehicle response of the target vehicle, wherein the vehicle response comprises a vehicle body acceleration response and a wheel acceleration response.
Specifically, the vehicle response acquiring sub-module may include:
a vehicle body acceleration response acquisition unit configured to acquire a vehicle body acceleration response of the target vehicle, wherein the vehicle body acceleration response includes a vertical acceleration, a pitch rotational acceleration, and a roll rotational acceleration at a vehicle body centroid of the target vehicle;
A wheel acceleration response obtaining unit, configured to obtain a wheel acceleration response of the target vehicle, where the wheel acceleration response includes a vertical acceleration at a junction between each wheel and an axle of the target vehicle.
More specifically, the vehicle body acceleration response acquiring unit may include:
the vehicle body acceleration acquisition subunit is used for acquiring vehicle body acceleration of the target vehicle, wherein the vehicle body acceleration comprises vertical acceleration of the front side, the rear side, the left side and the right side of the vehicle body of the target vehicle;
and the vehicle body acceleration response calculating subunit is used for obtaining the vehicle body acceleration response of the target vehicle by utilizing the vehicle geometric relationship according to the vehicle body acceleration.
Specifically, the contact force calibration equation in the contact force acquisition module is as follows:
wherein Δ F (t) represents a tire contact force, p0Indicating that the target vehicle is stationaryTime tire pressure, Δ p (t) represents dynamic tire pressure;
denotes the derivative of Δ p (t), θ1、θ2、θ3Respectively, constants obtained according to a calibration test, wherein the calibration test is performed based on an extended kalman filter method.
In one embodiment, the apparatus may further include:
The model establishing module is used for placing the unknown quantity in a standard Kalman filtering equation to obtain an unknown quantity Kalman filtering equation, wherein the standard Kalman filtering equation comprises a system state space equation and a system measurement equation; and establishing an inverse operation model based on an unknown Kalman filtering method according to the unknown Kalman filtering equation.
Specifically, as shown in the detailed functional block diagram of fig. 7, the road flatness acquiring module may include:
the measured data submodule is used for obtaining measured data according to the vehicle body response and the tire contact force;
the parameter prediction submodule is used for performing prediction based on an unknown Kalman filtering equation according to the system state optimal value and the system covariance matrix optimal value at the current moment and correspondingly obtaining a system state predicted value and a system covariance matrix predicted value at the next moment;
the intermediate value operator module is used for obtaining Kalman increment according to the system covariance matrix predicted value;
and the road flatness calculation submodule is used for obtaining the road flatness at the current moment by utilizing the measurement data according to the system state predicted value and the Kalman increment.
Specifically, the road flatness acquiring module may further include:
and the model updating submodule is used for correcting the system state predicted value and the system covariance matrix predicted value based on the measured data to obtain a system state optimal value and a system covariance matrix optimal value at the next moment so as to obtain the road flatness at the current moment.
It should be noted that, in the road flatness detecting apparatus provided in this embodiment, for specific functions that can be realized by each functional module and further implementation details in the specific implementation, reference may be made to the description of the specific implementation in the first embodiment, and for brevity of the description, repeated descriptions are not repeated here.
EXAMPLE III
Based on the same inventive concept, this embodiment provides a road flatness detecting apparatus, which may be an electronic device, where the electronic device may be a mobile phone, a computer, or a tablet computer, and the electronic device includes a memory and a processor, where the memory stores a computer program, and when the computer program is executed by the processor, the road flatness detecting method described in the above embodiment is implemented.
It is understood that the electronic device may also include multimedia components, input/output (I/O) interfaces, and communication components.
Wherein, the processor is used for executing all or part of the steps in the method for detecting the road flatness as described in the first embodiment. The memory is used to store various types of data, which may include, for example, instructions for any application or method in the electronic device, as well as application-related data.
The Processor may be an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and is configured to perform all or part of the steps of the method for detecting road flatness according to the first embodiment.
The Memory may be implemented by any type or combination of volatile and non-volatile Memory devices, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
The multimedia component may include a screen, which may be a touch screen, and an audio component for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in a memory or transmitted through a communication component. The audio assembly also includes at least one speaker for outputting audio signals.
The I/O interface provides an interface between the processor and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons.
The communication component is used for carrying out wired or wireless communication between the electronic equipment and other equipment. Wireless Communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so that the corresponding Communication component may include: Wi-Fi module, bluetooth module, wireless communication modules such as NFC module.
Example four
Based on the same inventive concept, the present embodiment provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application store, etc., on which a computer program is stored, which when executed by a processor, can implement the method for detecting the road flatness as described in the first embodiment.
Through the description of the foregoing embodiments, it is clear to those skilled in the art that the method of the foregoing embodiments may be implemented by software plus a necessary general hardware platform, and certainly may also be implemented by hardware, but in many cases, the former is a better implementation. Based on such understanding, the technical solutions of the present invention or portions thereof contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method of the embodiments of the present invention.
In this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or system in which the element is included.
It should be noted that the above numbers of the embodiments of the present invention are only for description, and do not represent the advantages or disadvantages of the embodiments.
The above description is only an alternative embodiment of the present invention, and is not intended to limit the scope of the present invention, and all modifications, equivalents and flow changes made by the present invention as described in the specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (9)
1. A road flatness detection method is characterized by comprising the following steps:
acquiring the tire pressure and vehicle response of a target vehicle;
obtaining the tire contact force by utilizing a contact force calibration equation according to the tire pressure of the wheel;
according to the vehicle response and the tire contact force, obtaining the road surface evenness by utilizing an inverse operation model, wherein the inverse operation model is established based on an unknown Kalman filtering method;
the step of obtaining the road surface flatness by using an inverse operation model according to the vehicle response and the tire contact force specifically comprises the following steps of:
obtaining measurement data based on the vehicle response and the tire contact force;
predicting based on an unknown Kalman filtering equation according to the system state optimal value and the system covariance matrix optimal value at the current moment, and correspondingly obtaining a system state predicted value and a system covariance matrix predicted value at the next moment;
Acquiring Kalman increment according to the system covariance matrix predicted value;
and according to the predicted value of the system state and the Kalman increment, obtaining the road flatness at the current moment by using the measurement data.
2. The road flatness detection method of claim 1, wherein said vehicle response includes a vehicle body acceleration response and a wheel acceleration response;
the step of obtaining the vehicle response of the target vehicle specifically includes:
acquiring a body acceleration response and a wheel acceleration response of the target vehicle, wherein the body acceleration response comprises a vertical acceleration, a pitch rotation acceleration and a roll rotation acceleration at a body mass center of the target vehicle, and the wheel acceleration response comprises a vertical acceleration at a connection of each wheel and an axle of the target vehicle.
3. The method for detecting flatness of a road according to claim 2, wherein the step of obtaining the vehicle body acceleration response of the target vehicle specifically includes:
acquiring the vehicle body acceleration of the target vehicle, wherein the vehicle body acceleration comprises the vertical acceleration of the front, the rear, the left side and the right side of the vehicle body of the target vehicle;
And obtaining the vehicle body acceleration response of the target vehicle by using the vehicle geometric relation according to the vehicle body acceleration.
4. The method for detecting flatness of a road surface of claim 1, wherein the contact force calibration equation is:
wherein Δ F (t) represents a tire contact force, p0Represents a wheel tire pressure when the target vehicle is stationary, Δ p (t) represents a dynamic wheel tire pressure;
denotes the derivative of Δ p (t), θ1、θ2、θ3Respectively, constants obtained from a calibration test, wherein the calibration test is performed based on an extended kalman filter method.
5. The method for detecting flatness of a road surface in accordance with claim 1, wherein prior to the step of obtaining flatness of a road surface using an inverse operation model based on the vehicle response and the tire contact force, the method further comprises:
placing the unknown quantity in a standard Kalman filtering equation to obtain an unknown quantity Kalman filtering equation, wherein the standard Kalman filtering equation comprises a system state space equation and a system measurement equation;
and establishing an inverse operation model based on an unknown Kalman filtering method according to the unknown Kalman filtering equation.
6. The method of claim 1, wherein after the step of obtaining the road flatness at the current time using the measurement data based on the predicted system state value and the kalman gain, the method further comprises:
And correcting the system state predicted value and the system covariance matrix predicted value based on the measured data to obtain a system state optimal value and a system covariance matrix optimal value at the next moment so as to obtain the road surface evenness at the current moment.
7. A road flatness detection apparatus, the apparatus comprising:
the parameter acquisition module is used for acquiring the wheel tire pressure and the vehicle response of the target vehicle;
the contact force acquisition module is used for acquiring a tire contact force by utilizing a contact force calibration equation according to the tire pressure of the wheel;
the road flatness acquisition module is used for acquiring the road flatness by utilizing an inverse operation model according to the vehicle response and the tire contact force, wherein the inverse operation model is established based on an unknown Kalman filtering method;
wherein, the road flatness acquisition module includes:
a measured data submodule for obtaining measured data from the vehicle response and the tire contact force;
the parameter prediction submodule is used for performing prediction based on an unknown Kalman filtering equation according to the system state optimal value and the system covariance matrix optimal value at the current moment, and correspondingly obtaining a system state predicted value and a system covariance matrix predicted value at the next moment;
The intermediate value operator module is used for obtaining Kalman increment according to the system covariance matrix predicted value;
and the road flatness calculation submodule is used for obtaining the road flatness at the current moment by utilizing the measurement data according to the system state predicted value and the Kalman increment.
8. A road flatness detection apparatus, characterized in that the apparatus comprises a memory and a processor, the memory having stored thereon a computer program which, when executed by the processor, implements the road flatness detection method of any of claims 1 to 6.
9. A storage medium having stored thereon a computer program executable by one or more processors to implement the method of detecting flatness of a road surface according to any one of claims 1 to 6.
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