CN113335294B - Method and device for estimating longitudinal gradient of road surface, electronic device and storage medium - Google Patents
Method and device for estimating longitudinal gradient of road surface, electronic device and storage medium Download PDFInfo
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- CN113335294B CN113335294B CN202110905651.5A CN202110905651A CN113335294B CN 113335294 B CN113335294 B CN 113335294B CN 202110905651 A CN202110905651 A CN 202110905651A CN 113335294 B CN113335294 B CN 113335294B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
- B60W40/076—Slope angle of the road
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/10—Longitudinal speed
- B60W2520/105—Longitudinal acceleration
Abstract
The application discloses a method and a device for estimating a longitudinal gradient of a road surface, electronic equipment and a storage medium, and belongs to the technical field of vehicle engineering. The method for estimating the longitudinal gradient of the road surface comprises the following steps: acquiring a longitudinal acceleration acquisition value of the vehicle, and acquiring a longitudinal acceleration calculation value of the vehicle; establishing a state equation aiming at the longitudinal speed of the vehicle, wherein the state equation is an equation taking the speed of the vehicle at the last moment and the acceleration of the vehicle at the last moment as independent variables and the speed of the vehicle at the current moment as dependent variables, and the acceleration of the vehicle at the last moment is determined according to a longitudinal acceleration acquisition value and a longitudinal acceleration calculation value; and obtaining a longitudinal gradient estimated value of the running road surface of the vehicle according to the state equation through a particle filter algorithm. The method and the device can improve the accuracy of estimation of the longitudinal gradient of the road surface.
Description
Technical Field
The application belongs to the technical field of vehicle engineering, and particularly relates to a method and a device for estimating a longitudinal gradient of a road surface, electronic equipment and a storage medium.
Background
The longitudinal gradient is one of the most important external loads of the vehicle and is one of the parameters which are important for a whole vehicle control system and a transmission control system, and the longitudinal gradient is used in a plurality of control scenes such as hill starting, creep torque correction, sliding energy motor feedback torque control, gear shifting line correction of climbing working conditions and the like.
However, longitudinal slope is difficult to measure directly by low cost sensors. Therefore, the longitudinal gradient can be quickly and accurately estimated, and the method is very important for improving the control effect of each controller of the vehicle.
In the related technology, a Bayesian filtering method can be adopted to realize the estimation of the longitudinal gradient. When the system is a linear system and the noise meets the Gaussian distribution, the state can be estimated by directly adopting a Kalman filtering algorithm. However, the vehicle is generally a complex nonlinear system, and its noise does not always satisfy a fixed gaussian distribution. Therefore, the filtering method described above is less accurate for longitudinal slope estimation.
Therefore, how to accurately estimate the longitudinal gradient of the traveling road surface of the vehicle is an urgent problem to be solved by those skilled in the art, and is also the key to realizing safe and effective control of the vehicle.
Disclosure of Invention
An object of the embodiments of the present application is to provide a method, an apparatus, an electronic device and a storage medium for estimating a longitudinal gradient of a road surface, which can solve the problem of how to accurately estimate the longitudinal gradient of the road surface on which a vehicle travels.
In a first aspect, an embodiment of the present application provides a method for estimating a longitudinal gradient of a road surface, where the method includes:
acquiring a longitudinal acceleration acquisition value of the vehicle, and acquiring a longitudinal acceleration calculation value of the vehicle;
establishing a state equation aiming at the longitudinal speed of the vehicle, wherein the state equation is an equation taking the speed of the vehicle at the last moment and the acceleration of the vehicle at the last moment as independent variables and the speed of the vehicle at the current moment as dependent variables, and the acceleration of the vehicle at the last moment is determined according to a longitudinal acceleration acquisition value and a longitudinal acceleration calculation value;
and obtaining a longitudinal gradient estimated value of the running road surface of the vehicle according to the state equation through a particle filter algorithm.
In addition, according to the technical scheme of the application, the method can also have the following technical characteristics:
in any one of the above technical solutions, obtaining a longitudinal acceleration acquisition value of the vehicle includes:
judging whether the vehicle starts on a slope or not;
under the condition that the vehicle starts on a slope, acquiring a longitudinal acceleration acquisition value according to a first acceleration signal acquired by an acceleration sensor of the vehicle;
under the condition that the vehicle starts on a non-slope road, acquiring a longitudinal acceleration acquisition value according to a first acceleration signal and a second acceleration signal acquired by an acceleration sensor of the vehicle;
the first acceleration signal is an acceleration signal when the vehicle normally runs, and the second acceleration signal is an acceleration signal when the acceleration sensor is electrified.
In any of the above technical solutions, obtaining a longitudinal acceleration calculation value of the vehicle includes:
the longitudinal acceleration calculation is obtained by deriving a speed signal acquired by a speed sensor of the vehicle.
In any of the above technical solutions, establishing an equation of state for the longitudinal speed of the vehicle includes:
establishing a target mathematical model by adopting a longitudinal acceleration acquisition value and a longitudinal acceleration calculation value;
establishing a state equation by discretizing a target mathematical model;
wherein the target mathematical model is a mathematical model relating to a relationship between a longitudinal acceleration collection value, a longitudinal acceleration calculation value, a vehicle body pitch angle of the vehicle, a longitudinal gradient of a running road surface of the vehicle, and an acceleration sensor error of the vehicle.
In any of the above technical solutions, obtaining the estimated value of the longitudinal gradient of the running road surface for the vehicle according to the state equation by using a particle filter algorithm includes:
at an initial moment, giving an initial value of a particle sample, an initial value of particle weight, the number of particles and an initial value of estimation;
substituting the particle value at the previous moment and the particle value at the current moment into a state equation based on the initial value of the particle sample so as to obtain a longitudinal gradient decoupling result of the vehicle through a particle filter algorithm;
based on the initial value of the particle weight, recursion is carried out on the particle weight at the current moment according to the particle weight at the previous moment so as to obtain all the particle weights;
according to the number of the particles, all the particle weights are normalized to obtain the normalized particle weight at the current moment;
and obtaining the longitudinal gradient estimated value at the current moment according to the estimated initial value and the normalized particle weight at the current moment based on the longitudinal gradient decoupling result.
In any of the above technical solutions, before obtaining the longitudinal gradient estimation value at the current time according to the initial estimation value and the normalized particle weight at the current time, the method further includes:
judging whether particle degradation occurs or not;
in the case where particle degradation occurs, a particle resampling operation is performed.
In any of the above technical solutions, the determining whether particle degradation occurs includes:
determining the number of effective particles according to the number of the particles and the weight of all the particles;
judging whether the particle degradation occurs or not according to the number of the effective particles;
and judging that the particle degradation occurs under the condition that the number of the effective particles is less than the threshold value of the number of the effective particles.
The method for estimating the longitudinal gradient of the road surface provided by the embodiment of the application firstly obtains a longitudinal acceleration acquisition value and a longitudinal acceleration calculation value of a vehicle. Further, an equation of state for the longitudinal speed of the vehicle is established. The state equation is an equation which takes the speed and the acceleration of the vehicle at the last moment as independent variables and the speed of the vehicle at the current moment as dependent variables, and the acceleration at the last moment is determined according to the longitudinal acceleration acquisition value and the longitudinal acceleration calculation value. And finally, obtaining a longitudinal gradient estimated value aiming at the running road surface of the vehicle through a particle filter algorithm. The particle filter algorithm can be used for a nonlinear system, and the noise of the particle filter algorithm does not need to meet Gaussian distribution, so that the longitudinal gradient of the road surface on which the vehicle runs can be estimated more accurately by the method for estimating the longitudinal gradient of the road surface, and the vehicle can be controlled more safely and effectively.
In a second aspect, an embodiment of the present application provides an estimation device of a longitudinal gradient of a road surface, the estimation device including:
the acquisition module is used for acquiring a longitudinal acceleration acquisition value of the vehicle and acquiring a longitudinal acceleration calculation value of the vehicle;
the system comprises an establishing module, a calculating module and a calculating module, wherein the establishing module is used for establishing a state equation aiming at the longitudinal speed of a vehicle, the state equation is an equation taking the speed and the acceleration of the vehicle at the last moment as independent variables and the speed of the vehicle at the current moment as dependent variables, and the acceleration at the last moment is determined according to a longitudinal acceleration acquisition value and a longitudinal acceleration calculated value;
and the estimation module is used for obtaining a longitudinal gradient estimation value aiming at the running road surface of the vehicle according to the state equation through a particle filter algorithm.
The estimation device for the longitudinal gradient of the road surface provided by the embodiment of the application adopts the estimation method for the longitudinal gradient of the road surface according to any one of the above technical schemes, so that the estimation device has all the beneficial effects of the estimation method for the longitudinal gradient of the road surface according to any one of the above technical schemes, and is not repeated herein.
In a fourth aspect, embodiments of the present application provide an electronic device, which includes a processor, a memory, and a program or instructions stored on the memory and executable on the processor, where the program or instructions, when executed by the processor, implement the steps of the method according to any one of the above-mentioned technical solutions.
The electronic device provided in the embodiment of the present application implements the method for estimating the longitudinal gradient of the road surface according to any one of the above technical solutions, so that the electronic device has all the beneficial effects of the method for estimating the longitudinal gradient of the road surface according to any one of the above technical solutions, and details are not repeated here.
In a fifth aspect, embodiments of the present application provide a readable storage medium, on which a program or instructions are stored, where the program or instructions, when executed by a processor, implement the steps of the method for estimating the longitudinal gradient of a road surface according to any one of the above-mentioned technical solutions.
The readable storage medium provided in the embodiment of the present application implements the method for estimating the longitudinal gradient of the road surface according to any one of the above technical solutions, so that the method has all the advantages of the method for estimating the longitudinal gradient of the road surface according to any one of the above technical solutions, and details are not described here.
Drawings
FIG. 1 is one of flowcharts of steps of a method for estimating a longitudinal gradient of a road surface according to an embodiment of the present application;
FIG. 2 is a second flowchart illustrating the steps of a method for estimating a longitudinal gradient of a road surface according to an embodiment of the present invention;
FIG. 3 is a third flowchart illustrating the steps of a method for estimating the longitudinal gradient of a road surface according to an embodiment of the present invention;
FIG. 4 is a flow chart illustrating the fourth step of the method for estimating the longitudinal gradient of a road surface according to the embodiment of the present application;
FIG. 5 is a flowchart showing the fifth step of the method for estimating the longitudinal gradient of the road surface according to the embodiment of the present application;
FIG. 6 is a flowchart showing the sixth step of the method for estimating the longitudinal gradient of the road surface according to the embodiment of the present application;
FIG. 7 is a block diagram schematically showing the composition of a road surface longitudinal gradient estimation device according to an embodiment of the present application;
fig. 8 is a block diagram schematically illustrating the components of the electronic device according to the embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described clearly below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments given herein are intended to be within the scope of the present disclosure.
The terms first, second and the like in the description and in the claims of the present application are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that embodiments of the application may be practiced in sequences other than those illustrated or described herein, and that the terms "first," "second," and the like are generally used herein in a generic sense and do not limit the number of terms, e.g., the first term can be one or more than one. In addition, "and/or" in the specification and claims means at least one of connected objects, a character "/" generally means that a preceding and succeeding related objects are in an "or" relationship.
The method, the apparatus, the electronic device, and the storage medium for estimating the longitudinal gradient of a road surface provided in the embodiments of the present application are described in detail below with reference to the accompanying drawings.
It should be noted that, in the estimation method of the longitudinal gradient of the road surface provided in the embodiment of the present application, the execution subject may be an estimation device of the longitudinal gradient of the road surface, or a control module of the estimation device of the longitudinal gradient of the road surface for executing the estimation method of the longitudinal gradient of the road surface. The method and the device for estimating the longitudinal gradient of the road surface provided by the embodiment of the application are described by taking the method for estimating the longitudinal gradient of the road surface by the device for estimating the longitudinal gradient of the road surface as an example.
The vehicle to which the method for estimating the longitudinal gradient of the road surface provided by the embodiment of the application is applicable can be an electric vehicle and can also be a vehicle powered by steam.
As shown in fig. 1, the method for estimating the longitudinal gradient of the road surface according to the embodiment of the present application includes the following steps S101 to S103:
s101, the estimation device acquires a longitudinal acceleration acquisition value of the vehicle and acquires a longitudinal acceleration calculation value of the vehicle.
In the embodiment of the application, the longitudinal acceleration acquisition value of the vehicle CAN be acquired and measured through a Controller Area Network (CAN) bus sensor.
Specifically, the acceleration sensor is provided in a vehicle, and is generally composed of a mass, a damper, an elastic element, a sensing element, an adjustment circuit, and the like. In the acceleration process, the acceleration sensor obtains an acceleration value (namely an acceleration signal) by measuring the inertial force borne by the mass block and utilizing Newton's second law.
For example, the acceleration sensor may be at least one of a capacitive acceleration sensor, an inductive acceleration sensor, a strain-type acceleration sensor, a piezoresistive acceleration sensor, and a piezoelectric acceleration sensor.
Therefore, the estimation device can receive and acquire the acceleration signal and further acquire a longitudinal acceleration acquisition value of the vehicle according to the acceleration signal.
In the embodiment of the application, the longitudinal acceleration calculated value of the vehicle can be obtained according to a speed signal acquired by a speed sensor of the vehicle.
Specifically, the above acquiring the calculated value of the longitudinal acceleration of the vehicle specifically includes:
the longitudinal acceleration calculation is obtained by deriving a speed signal acquired by a speed sensor of the vehicle.
As shown in fig. 2, optionally, in the embodiment of the present application, the estimation device in S101 obtains the longitudinal acceleration acquisition value of the vehicle, and specifically includes the following S101a to S101 c:
s101, 101a, the estimation device judges whether the vehicle starts on a slope.
Because of the cost limitation in the production process of the vehicle, the acceleration sensors equipped in mass production vehicles generally have poor precision, and are easy to be interfered by the outside and have zero drift. Therefore, in order to improve the accuracy of estimating the longitudinal gradient, it is necessary to process the signal acquired by the acceleration sensor.
Therefore, in order to reasonably remove the zero drift under appropriate conditions, the embodiment of the present application determines whether the zero drift removal process needs to be performed on the vehicle through S101 a.
It can be understood that the zero drift elimination processing is not needed to be carried out on the vehicle when the vehicle starts on a slope, and the zero drift elimination processing is needed to be carried out on the vehicle when the vehicle starts on a non-slope.
It will be appreciated that the above described hill start may be an uphill start or a downhill start.
It will be appreciated that the above described non-hill starts may be on a flat road or on a relatively flat road.
Optionally, in the embodiment of the present application, an estimation signal of an electronic Parking system (EPB) may be read, and then whether the vehicle starts on a slope may be determined according to the estimation signal.
S101b, the estimation device acquires a longitudinal acceleration acquisition value according to a first acceleration signal acquired by an acceleration sensor of the vehicle when the vehicle starts on a slope.
The first acceleration signal is an acceleration signal when the vehicle runs normally.
It can be understood that, under the condition that the vehicle starts on a slope, the longitudinal acceleration acquisition value can be directly acquired according to the acceleration signal acquired by the acceleration sensor of the vehicle when the vehicle normally runs. Further, after the longitudinal acceleration acquisition value is obtained, S102 may be directly performed.
S101c, the estimation device acquires a longitudinal acceleration acquisition value according to the first acceleration signal and the second acceleration signal acquired by the acceleration sensor of the vehicle under the condition that the vehicle starts on a non-slope road.
The first acceleration signal is an acceleration signal when the vehicle normally runs, and the second acceleration signal is an acceleration signal when the acceleration sensor is electrified.
It can be understood that, under the condition that the vehicle starts on a non-slope road, the zero drift removal processing needs to be carried out on the signals collected by the acceleration sensor of the vehicle, so as to avoid the zero drift problem caused by external interference.
For example, the longitudinal acceleration acquisition value may be obtained from a difference between the subtraction of the first acceleration signal and the second acceleration signal.
Specifically, it is assumed that the acceleration signal (i.e., the first acceleration signal) of the vehicle during normal running isa x_sen_bias Assuming that the acceleration signal (i.e. the second acceleration signal) when the acceleration sensor is powered on isa x_bias Then, the longitudinal acceleration sensor signal after calculation correctiona x_sen It can be obtained by the following formula:
a x_sen =a x_sen_bias -a x_bias 。
thus, the longitudinal acceleration sensor signal after calculation correction can be adopteda x_sen And the longitudinal acceleration is taken as a longitudinal acceleration acquisition value.
S102, the estimation device establishes a state equation aiming at the longitudinal speed of the vehicle.
The state equation is an equation which takes the speed and the acceleration of the vehicle at the last moment as independent variables and the speed of the vehicle at the current moment as dependent variables, and the acceleration at the last moment is determined according to the longitudinal acceleration acquisition value and the longitudinal acceleration calculation value.
It is understood that by establishing a state equation for the longitudinal speed of the vehicle, the longitudinal gradient estimated value for the running surface of the vehicle can be obtained by subjecting the state equation to particle filter processing in the subsequent step.
As shown in fig. 3, optionally, in this embodiment of the application, S102 specifically includes the following S102a to S102 b:
s102a, the estimation device adopts the longitudinal acceleration acquisition value and the longitudinal acceleration calculation value to establish a mathematical model.
The target mathematical model is a target mathematical model related to the relationship among a longitudinal acceleration acquisition value, a longitudinal acceleration calculation value, a vehicle body pitch angle of the vehicle, a longitudinal gradient of a running road surface of the vehicle and an acceleration sensor error of the vehicle.
It will be appreciated that the objective of building the target mathematical model is to: the coupling of the mass and the longitudinal gradient is realized by utilizing a longitudinal acceleration acquisition value acquired by an acceleration sensor and combining a longitudinal acceleration calculation value of the vehicle. Furthermore, the non-linear system and the non-gaussian distribution noise of the vehicle can be estimated by utilizing a particle filter algorithm in the subsequent step, so that the coupling of the longitudinal gradient and the pitch angle of the vehicle body is realized.
Illustratively, the above-described target mathematical model may be established as follows.
First, the estimation device may establish a first model as follows:
a x_sen =gsinq+a x +e；
wherein the content of the first and second substances,a x_sen the values are collected for the longitudinal acceleration,a x the calculated value is the longitudinal acceleration,gin order to be the acceleration of the gravity,qas the overall pitch angle of the vehicle,eis the sensor error.
In the first model described above, the vehicle collective pitch angleqFor road gradeq r And vehicle body pitch angleq v The sum of the additions of (a).
In the first model described above, the sensor erroreIs considered to be white noise satisfying a gaussian distribution.
Further, to achieve longitudinal gradientq r Angle of pitch with respect to vehicle bodyq v And due to decoupling ofq v Smaller, the above first mathematical model can be further simplified to the following second model:
a x_sen =gsinq r +a x +q；
wherein the content of the first and second substances,q=gq v +efor the sake of simplifying the calculation, it can be considered that the above model still satisfies the normal distribution. In practical engineering applications, it can be considered thatq v Anda x is proportional, thereforeqVariance ofa x Monotonously changes.
Subsequently, if only the longitudinal movement of the vehicle is considered, the second mathematical model can be further simplified to build the target model. Wherein the target model is:
wherein the content of the first and second substances,
is the longitudinal acceleration of the vehicle and,a x_sen the values are collected for the longitudinal acceleration,gin order to be the acceleration of the gravity,θ r in the form of a longitudinal slope,qis the sensor error.
S102b, the estimation device establishes a state equation by discretizing the target mathematical model.
It will be appreciated that for ease of operation, the above formula of the target model may be discretized using the Euler method. Since the change in the road gradient is not large in the actual situation, it can be considered that the gradient change is 0:
illustratively, through the discretization process, the following equation of state can be established:
v x （k+1）=v x （k）+[a x_sen （k）-gsinq r （k）+q（k）]Dt；
q r （k+1）=q r （k）
the estimation of the longitudinal gradient of the road surface is a continuously dynamic process. For convenience of description, in the embodiment of the present application, when estimating the longitudinal gradient of the road surface, the current time is simply referred to as "current timekThe last time compared with the current time is abbreviatedk-1, the next moment in time compared to the current moment in time is abbreviatedk+1, and the initial time is abbreviatedk 0。
Wherein the content of the first and second substances,v x （k+ 1) is the longitudinal speed at the next moment,v x （k) Longitudinal speed at the present moment, DtIn the form of a time interval,a x_sen （k）-gsinq r （k）+q（k) The longitudinal acceleration obtained according to the target model at the current moment.q r （k+ 1) is the longitudinal gradient at the next moment,q r （k) Is the longitudinal gradient at the present moment.
S103, the estimation device obtains a longitudinal gradient estimation value aiming at a running road surface of the vehicle according to a state equation through a particle filter algorithm.
It will be appreciated that the equations of state described above are discrete forms required for particle filtering. At longitudinal vehicle speedv x As a systematic measure (i.e., an optimal estimate), and with the prior distribution as the proposed distribution of the algorithm, the standard particle filtering process of S103 above can be performed on the equation for the state equation.
As shown in fig. 4, optionally, in this embodiment of the application, S103 specifically includes the following S103a to S103 e:
s103a, the estimation device gives initial values of the particle samples, the particle weight, the number of particles and the estimation initial values at the initial time.
In the embodiment of the present application, the particle filter used for performing the particle filtering process may be initialized at the initial time, and the initial value may be estimated in a given state
And generating initial values of the particle samples from the prior probability
Initial values of all particle weights
Number of particlesNWherein all the initial values of the particle weights
Is 1N。
And S103b, substituting the particle value at the previous moment and the particle value at the current moment into a state equation by the estimation device based on the initial particle sample value so as to obtain a longitudinal gradient decoupling result of the vehicle through a particle filter algorithm.
In the embodiment of the present application, the position of the particle may be changed by predicting through S103 b.
Illustratively, the body pitch angle and the longitudinal slope may be decoupled by the following equations:
wherein the content of the first and second substances,f(v) represents the equation of state:
v x （k+1）=v x （k）+[a x_sen （k）-gsinq r （k）+q（k）]Dt；
q r （k+1）=q r （k）；
wherein the content of the first and second substances,
is the value of the particle at the current time,vto follow a normal distributionqA random number of (2), andqwill be as followsa x Is increased. Therefore, self-adaptation of particle filtering can be achieved, and the pitch angle and the longitudinal gradient of the vehicle body are decoupled.
S103c, the estimating device recurs the particle weight at the current time according to the particle weight at the previous time based on the initial particle weight value to obtain all the particle weights.
In the embodiment of the present application, the updating may be performed through S103c to determine the weight of the particle at the previous time
Calculating the weight of the particles at the current moment in a recursion manner
。
S103d, the estimating device normalizes all the particle weights according to the number of particles to obtain the normalized particle weight at the current time.
In this embodiment of the present application, the normalized particle weight at the current time may be obtained by the following formula:
wherein the content of the first and second substances,
is the normalized particle weight for the current time instant,
is the weight of the particle at the current time,Nis the number of particles.
And S103e, the estimation device obtains the longitudinal gradient estimation value at the current moment according to the initial estimation value and the normalized particle weight at the current moment based on the longitudinal gradient decoupling result.
In the embodiment of the present application, the estimated value of the longitudinal gradient may be obtained by the following formula:
wherein the content of the first and second substances,
as an estimate of the longitudinal gradient,
is the normalized particle weight for the current time instant,
the estimated value of the current time is obtained based on the estimated initial value.
As shown in fig. 5, optionally, in the embodiment of the present application, before S103e, the method for estimating the longitudinal gradient of the road surface further includes the following S103f and S103 g:
s103f, the estimating means judges whether or not particle degradation occurs.
S103g, the estimating device performs a particle resampling operation when the particle degradation occurs.
In the embodiment of the present application, the particle degradation is also called particle depletion, which means that after several iterations, the weights of many particles become small, resulting in a phenomenon that a large amount of computation cost is wasted on the particles with small weights.
It can be understood that by performing the particle resampling operation, the posterior probability density can be resampled, the particles with large copy weight are retained, and the particles with small copy weight are removed, so as to prevent or avoid the particle degradation problem.
Alternatively, in the embodiment of the present application, in order to prevent particle depletion and loss of diversity, whether particle degradation occurs or not may be determined in the following manner.
As shown in fig. 6, exemplarily, S103f specifically includes the following S103f1 and S103f 2:
s103f1, the estimation device determines the number of effective particles based on the number of particles and the weight of all particles.
Specifically, the number of effective particles can be determined by the following formula:
wherein the content of the first and second substances,N eff the number of the effective particles is,Nthe number of the particles is the number of the particles,
is the weight of the particle at the current time.
S103f2, the estimation device judges whether the particle degradation occurs according to the number of effective particles.
And judging that the particle degradation occurs under the condition that the number of the effective particles is less than the threshold value of the number of the effective particles.
It is understood that the effective particle count threshold value can be selected and adjusted by one skilled in the art.
The method for estimating the longitudinal gradient of the road surface provided by the embodiment of the application firstly obtains a longitudinal acceleration acquisition value and a longitudinal acceleration calculation value of a vehicle. Further, a state equation for the longitudinal speed of the vehicle is established using the longitudinal acceleration acquisition value and the longitudinal acceleration calculation value. And finally, performing particle filter processing on the state equation to obtain a longitudinal gradient estimated value aiming at the running road surface of the vehicle. The particle filter algorithm can be used for a nonlinear system, and the noise of the particle filter algorithm does not need to meet Gaussian distribution, so that the longitudinal gradient of the road surface on which the vehicle runs can be estimated more accurately by the method for estimating the longitudinal gradient of the road surface, and the vehicle can be controlled more safely and effectively.
As shown in fig. 7, an embodiment of the present application further provides an estimation apparatus 700 for a longitudinal gradient of a road surface, where the estimation apparatus 700 includes:
the obtaining module 710 is configured to obtain a longitudinal acceleration collecting value of the vehicle, and obtain a longitudinal acceleration calculating value of the vehicle.
The establishing module 720 is configured to establish a state equation for the longitudinal speed of the vehicle, where the state equation is an equation in which the speed of the vehicle at the previous time and the acceleration of the vehicle at the previous time are used as independent variables, and the speed of the vehicle at the current time is used as a dependent variable, and the acceleration of the vehicle at the previous time is determined according to the collected value of the longitudinal acceleration and the calculated value of the longitudinal acceleration.
And the estimation module 730 is used for obtaining the longitudinal gradient estimation value of the running road surface of the vehicle according to the state equation through a particle filter algorithm.
The estimation apparatus 700 in the embodiment of the present application may be an apparatus, or may be a component, an integrated circuit, or a chip in a terminal. The device can be mobile electronic equipment or non-mobile electronic equipment.
Optionally, in this embodiment of the present application, the obtaining module 710 is specifically configured to:
judging whether the vehicle starts on a slope or not;
under the condition that the vehicle starts on a slope, acquiring a longitudinal acceleration acquisition value according to a first acceleration signal acquired by an acceleration sensor of the vehicle;
under the condition that the vehicle starts on a non-slope road, acquiring a longitudinal acceleration acquisition value according to a first acceleration signal and a second acceleration signal acquired by an acceleration sensor of the vehicle;
the first acceleration signal is an acceleration signal when the vehicle normally runs, and the second acceleration signal is an acceleration signal when the acceleration sensor is electrified.
Optionally, in this embodiment of the present application, the obtaining module 710 is specifically configured to:
the longitudinal acceleration calculation is obtained by deriving a speed signal acquired by a speed sensor of the vehicle.
Optionally, in this embodiment of the application, the establishing module 720 is specifically configured to:
establishing a target mathematical model by adopting a longitudinal acceleration acquisition value and a longitudinal acceleration calculation value;
establishing a state equation by discretizing a target mathematical model;
wherein the target mathematical model is a mathematical model relating to a relationship between a longitudinal acceleration collection value, a longitudinal acceleration calculation value, a vehicle body pitch angle of the vehicle, a longitudinal gradient of a running road surface of the vehicle, and an acceleration sensor error of the vehicle.
Optionally, in this embodiment of the present application, the estimating module 730 is specifically configured to:
at an initial moment, giving an initial value of a particle sample, an initial value of particle weight, the number of particles and an initial value of estimation;
substituting the particle value at the previous moment and the particle value at the current moment into a state equation based on the initial value of the particle sample so as to obtain a longitudinal gradient decoupling result of the vehicle through a particle filter algorithm;
based on the initial value of the particle weight, recursion is carried out on the particle weight at the current moment according to the particle weight at the previous moment so as to obtain all the particle weights;
according to the number of the particles, all the particle weights are normalized to obtain the normalized particle weight at the current moment;
and obtaining the longitudinal gradient estimated value at the current moment according to the estimated initial value and the normalized particle weight at the current moment based on the longitudinal gradient decoupling result.
Optionally, in this embodiment of the present application, the estimating module 730 is specifically configured to:
judging whether particle degradation occurs or not before acquiring a longitudinal gradient estimation value at the current moment according to the initial estimation value and the normalized particle weight at the current moment;
in the case where particle degradation occurs, a particle resampling operation is performed.
Optionally, in this embodiment of the present application, the estimating module 730 is specifically configured to:
determining the number of effective particles according to the number of the particles and the weight of all the particles;
judging whether the particle degradation occurs or not according to the number of the effective particles;
and judging that the particle degradation occurs under the condition that the number of the effective particles is less than the threshold value of the number of the effective particles.
The estimation apparatus 700 for the longitudinal gradient of the road surface according to the embodiment of the present application adopts the estimation method for the longitudinal gradient of the road surface according to any one of the embodiments described above, so that it has all the advantages of the estimation method for the longitudinal gradient of the road surface according to any one of the embodiments described above, and details thereof are not repeated herein.
As shown in fig. 8, an electronic device 800 is further provided in an embodiment of the present application, which includes a processor 810, a memory 820 and a program or instruction stored on the memory 820 and executable on the processor 810, and when the program or instruction is executed by the processor, the steps of the method according to any of the embodiments are implemented.
It should be noted that the electronic device 800 in the embodiment of the present application includes a mobile electronic device and a non-mobile electronic device.
The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and so on.
The electronic device 800 provided in the embodiment of the present application implements the method for estimating the longitudinal gradient of the road surface according to any one of the embodiments described above, so that the method has all the advantages of the method for estimating the longitudinal gradient of the road surface according to any one of the embodiments described above, and details are not described here.
The embodiment of the present application further provides a readable storage medium, where a program or an instruction is stored on the readable storage medium, and the program or the instruction, when executed by a processor, implements the steps of the method for estimating a longitudinal gradient of a road surface according to any one of the embodiments described above, so that the method has all the beneficial effects of the method for estimating a longitudinal gradient of a road surface according to any one of the embodiments described above, and details are not repeated herein.
It should be noted that, 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 apparatus 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 apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. Further, it should be noted that the scope of the methods and apparatus of the embodiments of the present application is not limited to performing the functions in the order illustrated or discussed, but may include performing the functions in a substantially simultaneous manner or in a reverse order based on the functions involved, e.g., the methods described may be performed in an order different than that described, and various steps may be added, omitted, or combined. In addition, features described with reference to certain examples may be combined in other examples.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present application.
While the present embodiments have been described with reference to the accompanying drawings, it is to be understood that the invention is not limited to the precise embodiments described above, which are meant to be illustrative and not restrictive, and that various changes may be made therein by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. A method of estimating a longitudinal gradient of a road surface, characterized by comprising:
acquiring a longitudinal acceleration acquisition value of a vehicle, and acquiring a longitudinal acceleration calculation value of the vehicle;
establishing a state equation aiming at the longitudinal speed of the vehicle, wherein the state equation is an equation taking the speed and the acceleration of the vehicle at the last moment as independent variables and the speed of the vehicle at the current moment as dependent variables, and the acceleration at the last moment is determined according to the longitudinal acceleration acquisition value and the longitudinal acceleration calculation value;
obtaining a longitudinal gradient estimation value of a running road surface of the vehicle according to the state equation through a particle filter algorithm;
the acquiring of the longitudinal acceleration acquisition value of the vehicle comprises the following steps:
judging whether the vehicle starts on a slope or not;
under the condition that the vehicle starts on a slope, acquiring a longitudinal acceleration acquisition value according to a first acceleration signal acquired by an acceleration sensor of the vehicle;
under the condition that the vehicle starts on a non-slope road, acquiring a longitudinal acceleration acquisition value according to the first acceleration signal and the second acceleration signal acquired by the acceleration sensor of the vehicle;
the first acceleration signal is an acceleration signal when the vehicle runs normally, and the second acceleration signal is an acceleration signal when the acceleration sensor is powered on.
2. The method of estimating a longitudinal gradient of a road surface according to claim 1, wherein the acquiring a longitudinal acceleration calculation value of the vehicle includes:
the calculated longitudinal acceleration value is obtained by deriving a speed signal collected by a speed sensor of the vehicle.
3. The method of estimating the longitudinal gradient of the road surface according to claim 1 or 2, wherein the establishing of the equation of state for the longitudinal speed of the vehicle includes:
establishing a target mathematical model by adopting the longitudinal acceleration acquisition value and the longitudinal acceleration calculation value;
establishing the state equation by discretizing the target mathematical model;
wherein the target mathematical model is a mathematical model regarding a relationship among the longitudinal acceleration collection value, the longitudinal acceleration calculation value, a body pitch angle of the vehicle, a longitudinal gradient of a running road surface of the vehicle, and an acceleration sensor error of the vehicle.
4. The method of estimating a longitudinal gradient of a road surface according to claim 1, wherein the obtaining, by a particle filter algorithm, a longitudinal gradient estimated value for a running road surface of the vehicle from the state equation includes:
at an initial moment, giving an initial value of a particle sample, an initial value of particle weight, the number of particles and an initial value of estimation;
substituting the particle value at the previous moment and the particle value at the current moment into the state equation based on the initial particle sample value so as to obtain a longitudinal gradient decoupling result of the vehicle through the particle filter algorithm;
based on the initial value of the particle weight, recursion is carried out on the particle weight at the current moment according to the particle weight at the previous moment so as to obtain all the particle weights;
according to the number of the particles, performing normalization processing on all the particle weights to obtain the normalized particle weight at the current moment;
and obtaining the longitudinal gradient estimated value at the current moment according to the estimated initial value and the normalized particle weight at the current moment based on the longitudinal gradient decoupling result.
5. The method of estimating a longitudinal gradient of a road surface according to claim 4, characterized in that, before the obtaining of the longitudinal gradient estimation value at the present time from the initial estimation value and the normalized particle weight at the present time, the method further comprises:
judging whether particle degradation occurs or not;
in case of said particle degradation, a particle resampling operation is performed.
6. The method of estimating the longitudinal gradient of a road surface according to claim 5, wherein the determining whether particle degradation has occurred includes:
determining the number of effective particles according to the number of the particles and the weight of all the particles;
judging whether the particle degradation occurs or not according to the number of the effective particles;
and judging that the particle degradation occurs under the condition that the number of the effective particles is less than the threshold value of the number of the effective particles.
7. An estimation device of a road surface longitudinal gradient, characterized by comprising:
the acquisition module is used for acquiring a longitudinal acceleration acquisition value of a vehicle and acquiring a longitudinal acceleration calculation value of the vehicle;
the system comprises an establishing module, a calculating module and a calculating module, wherein the establishing module is used for establishing a state equation aiming at the longitudinal speed of the vehicle, the state equation is an equation taking the speed and the acceleration of the vehicle at the last moment as independent variables and the speed of the vehicle at the current moment as dependent variables, and the acceleration at the last moment is determined according to the longitudinal acceleration acquisition value and the longitudinal acceleration calculation value;
the estimation module is used for obtaining a longitudinal gradient estimation value aiming at a running road surface of the vehicle according to the state equation through a particle filter algorithm;
the acquiring of the longitudinal acceleration acquisition value of the vehicle comprises the following steps:
judging whether the vehicle starts on a slope or not;
under the condition that the vehicle starts on a slope, acquiring a longitudinal acceleration acquisition value according to a first acceleration signal acquired by an acceleration sensor of the vehicle;
under the condition that the vehicle starts on a non-slope road, acquiring a longitudinal acceleration acquisition value according to the first acceleration signal and the second acceleration signal acquired by the acceleration sensor of the vehicle;
the first acceleration signal is an acceleration signal when the vehicle runs normally, and the second acceleration signal is an acceleration signal when the acceleration sensor is powered on.
8. An electronic device, characterized by comprising a processor, a memory and a program or instructions stored on the memory and executable on the processor, which program or instructions, when executed by the processor, implement the steps of the method of estimating a longitudinal gradient of a road surface according to any one of claims 1 to 6.
9. A readable storage medium, characterized in that it stores thereon a program or instructions which, when executed by a processor, implement the steps of the method of estimating the longitudinal gradient of a road surface according to any one of claims 1 to 6.
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