CN115416669A

CN115416669A - Method and system for predicting gradient of running road surface of vehicle and vehicle

Info

Publication number: CN115416669A
Application number: CN202211094710.6A
Authority: CN
Inventors: 洪日; 张建; 谢飞; 王御; 杜杰; 韩亚凝; 张苏铁; 王珊
Original assignee: FAW Group Corp
Current assignee: FAW Group Corp
Priority date: 2022-09-08
Filing date: 2022-09-08
Publication date: 2022-12-02
Also published as: WO2024051188A1

Abstract

The invention discloses a method and a system for predicting the gradient of a running road surface of a vehicle and the vehicle, wherein the method specifically comprises the following steps: establishing a space slope curved surface, setting a vehicle running area, acquiring a ground slope value and a vehicle downhill value in front of a vehicle, calculating a length value of an imperceptible area, and setting a slope perceptible area and a slope estimable area as observed values; based on the observation value, carrying out smoothing processing and weight distribution on the length value of the imperceptible region, and calculating a longitudinal gradient angle and a tendency gradient angle of the imperceptible region; predicting a vehicle track, determining the position and the course angle of the vehicle at a future moment, fusing a slope sensible area and a slope estimable area, predicting a slope value of the vehicle at a future moment, wherein the system, the vehicle and the method correspond to each other. The invention can predict the gradient of the road surface in front of the vehicle, realizes the continuous prediction of the gradient of the road surface in front of the vehicle, and provides continuous and accurate gradient signals for the functions of vehicle dynamic state prediction, intelligent driving and the like.

Description

Method and system for predicting gradient of running road surface of vehicle and vehicle

Technical Field

The invention relates to a road surface gradient prediction method and system and a vehicle thereof, in particular to a road surface gradient prediction method and system for vehicle running and a vehicle thereof.

Background

The prior art mainly includes two aspects for the technology of vehicle front road surface slope prediction:

one method is to use positioning means such as GPS and the like to predict the gradient based on map information, but the precision of the current positioning technology is lower and depends on the precision of a map, the method can only predict the gradient in a larger interval on a road at present and cannot be used for a real-time operation scene with higher precision requirement, and the application of a high-precision map brings cost improvement, so that the current application is less and the environmental adaptability is poorer.

The other gradient prediction means is based on a vision sensor of the vehicle, and uses equipment with depth information such as a binocular camera, a laser radar and the like to sense the relative gradient of the road surface in front of the vehicle, but has the problems that the sensor has a blind area, sensing cannot be realized in a range close to the front of the vehicle, namely, the gradient of a long distance in the front of the vehicle can only be recognized, the visual angle of the sensor is limited, gradient information of the left side and the right side of the vehicle in a large range cannot be obtained, and namely, when the vehicle turns at a large turning angle, the gradient information on the advancing track of the vehicle cannot be known.

Disclosure of Invention

The invention aims to provide a method and a system for predicting the gradient of a running road surface of a vehicle and the vehicle, which can predict the gradient of the road surface in front of the vehicle, realize continuous prediction of the gradient of the road surface in front of the vehicle and provide continuous and accurate gradient signals for functions of vehicle dynamic state prediction, intelligent driving and the like.

The invention aims to solve another technical problem of realizing the perception in the area near the front of the vehicle aiming at the blind area of the vehicle-mounted vision sensor and breaking through the visual angle limitation of the vision sensor.

The invention aims to solve the technical problem that a continuous space curved surface in front of a vehicle is constructed in real time by combining the downward gradient of a vehicle wheel and the sensed front gradient information, and the vehicle track and the future gradient information are predicted by predicting vehicle dynamics parameters.

The invention can also solve the technical problem that the vehicle running road gradient can be predicted based on the dynamic state aiming at the characteristic that different vehicles have or do not have the vehicle track prediction function.

The invention provides the following scheme:

a method for predicting the gradient of a running road surface of a vehicle specifically comprises the following steps:

establishing a space slope curved surface, and setting a vehicle running area as follows: a gradient perceptible area, a gradient imperceptible area and a gradient estimable area;

setting the slope angles of the slope sensible area and the slope estimable area as observed values, and acquiring a ground slope value and a vehicle downhill slope value in front of a vehicle for calculating an actual slope value of the slope imperceptible area at the current moment;

setting a weight-based constraint condition based on the observed value, and determining the minimum value of errors by calculating weighted comprehensive measurement errors;

obtaining an optimal weight value of a constraint condition in real time, and making a decision according to the optimal weight value to obtain a longitudinal slope angle and a lateral slope angle of a certain point in a slope imperceptible area at a future moment;

predicting a vehicle track, establishing a longitudinal gradient vector and a lateral gradient vector by combining a longitudinal gradient angle and a lateral gradient angle of a certain point in a gradient-imperceptible area at a future moment, and establishing a unit normal vector of a gradient plane of the gradient-imperceptible area at the future moment;

and rotating the longitudinal gradient vector and the lateral gradient vector around the unit normal vector, and performing matrix calculation to obtain the gradient value of the vehicle at a certain point in the future.

Further, the gradient perceivable area is specifically as follows: a vehicle front gradient area obtained based on a visual sensor field angle;

the region in which the gradient can be estimated is a gradient region under the wheels of the vehicle;

the gradient imperceptible region is located between the gradient perceptible region and the gradient estimable region.

Further, the obtaining of the ground gradient value and the vehicle downhill gradient value in front of the vehicle specifically includes: front road surface longitudinal slope included angle alpha _c Actual front road surface slope angle alpha _f The included angle alpha of the vehicle body relative to the ground _s And the actual slope angle alpha of the ground under the vehicle _r Wherein:

the included angle of the longitudinal gradient of the front road surface is specifically the included angle between the plane of the vehicle body and the longitudinal gradient of the front road surface;

the actual front road surface gradient angle is an included angle between a front road surface plane and a horizontal plane in the longitudinal direction of the vehicle;

the included angle of the vehicle body relative to the ground is the included angle of the vehicle body relative to the ground under the vehicle;

the actual grade angle of the under-vehicle ground is the actual grade angle of the under-vehicle ground and the horizontal plane;

the ground gradient value and the vehicle gradient value satisfy the following formula:

the actual front road surface slope angle = the front road surface longitudinal slope included angle + the vehicle body included angle relative to the ground + the actual slope angle of the ground under the vehicle;

included angle alpha between vehicle body and ground _s Satisfies the formula:

wherein: h _fl Is the left front suspension height, H _fr Is the right front suspension height, H _rl Is the left rear suspension height, H _rr Is the right rear suspension height, L _axis Is the vehicle wheelbase;

further comprising: side slope angle beta of front road surface plane _f Side slope angle beta _f Satisfies the formula:

β _f ＝β _c +β _s +β _r

wherein beta is _s The angle is determined by:

wherein: beta is a _f Is a lateral slope angle of the front pavement plane; beta is a _c The lateral included angle between the plane of the vehicle body and the plane of the front road surface; beta is a beta _r Is a side slope angle under the vehicle wheel; l is _wheelbase The wheel tracks on the two sides of the vehicle.

Further, the length L of the imperceptible region _sg The following formula is satisfied:

in the above formula: h _g The vertical distance between the sensor and the ground under the vehicle; h _b The distance between the sensor and the ground of the vehicle body is a fixed value; r is _w Is the wheel radius; alpha is alpha _sd The included angle between the lower sideline of the visual angle of the camera and the vertical line of the vehicle floor is a fixed value; alpha is alpha _sg The included angle between the lower side line of the visual angle of the camera and the plane of the ground surface under the vehicle is set; l is _sd Depth information perceived for the lower line of the camera view angle; l is _wc The distance between the sensor and the axis of the front wheel in the advancing direction of the vehicle is approximately a fixed value; l is _sg The length of the imperceptible area is composed of two sections of road surface lengths.

Further, for the gradient value of the imperceptible region, the longitudinal plane of the front and rear planes are respectively usedLateral slope angle along length L of imperceptible region _sg Carrying out weighting integration, and determining a weighting curve coefficient by using the gradient signal covariance;

regarding the front plane slope angle and the rear plane slope angle as two observed values of the middle imperceptible region slope angle, assuming that the middle imperceptible region is a middle plane, and obtaining an estimated value expression of the middle plane:

r ₁ 、r ₂ weight of the front and rear plane slope angles, O ^* (k) The weighted mid-plane slope; and calculating the measurement error of the front and rear gradient measurement values at the k moment:

e _j (k)＝X(k|k-1)-O _j (k)j＝1，2

wherein e is _j (k) To measure one component of the error vector, X (k | k-1) is the slope value at time k predicted from the previous time;

the integrated measurement error after weighting is:

e ^* (k)＝[r ₁ (k)e _j (k)，r ₂ (k)e ₂ (k)] ^T

wherein T is a transposed symbol;

and selecting the optimal weighting weight by using a least square method to obtain the error square sum:

e ^*T (k)e ^* (k)＝(r ₁ (k)e _j (k)) ² +(r ₂ (k)e ₂ (k)) ²

using r ₁ +r ₂ With =1 as a constraint condition, the minimum value is obtained for the above equation, and the minimum value can be obtained by using the lagrange extreme method:

and obtaining the optimal weight for fusing the front and rear slope planes at the moment k, wherein the optimal weight represents the confidence level of the front and rear planes, and the value is changed in real time along with time.

Further, along the length L of the non-perceptible area _sg And performing smoothing treatment, taking the direction of the vehicle head as a coordinate starting point, performing normalization treatment on the length of the region, and distributing weight by a quadratic function to obtain the following formula:

in the above formula: e is the front and back plane weighting weight that changes with distance; k is a radical of _r Determining the curvature of the weighting curve for the weighting coefficients; x is L _sg The distance in the direction of the vehicle head after normalization; s is the distance of the gradient prediction point from the front axle of the vehicle in the longitudinal direction of the vehicle.

In summary, in the longitudinal direction of the vehicle, the longitudinal side slope angle at a certain point where the front distance of the front axle of the vehicle is s is:

wherein: alpha is alpha _m Longitudinal slope of the predicted point position; beta is a _m The lateral slope of the predicted point location is determined.

Further, when the vehicle track is predicted, if the vehicle does not have the track prediction function, the current kinematic parameters and the vehicle dynamic model of the vehicle are used for estimating the longitudinal driving mileage and the course deflection angle at the future moment;

predicting a driving force/braking force of the vehicle at a future time using a driving force/longitudinal force prediction model with an accelerator pedal position and a brake pedal position as input signals;

obtaining a front wheel steering angle time sequence at a future moment through a vehicle steering wheel steering angle prediction module;

according to the longitudinal speed and the yaw rate of the vehicle at the current moment, predicting the longitudinal travel mileage and the longitudinal course deflection angle of the vehicle under the current coordinate system at the future moment;

obtaining a space gradient plane of the vehicle at a future moment, and establishing a longitudinal gradient vector, a transverse gradient vector and a unit normal vector of the space gradient plane at the future moment by combining a longitudinal and lateral gradient angle and a vehicle position at the future moment;

and rotating the longitudinal gradient vector and the transverse gradient vector to obtain a rotation matrix and obtain a longitudinal gradient angle and a transverse gradient angle at a future moment.

A system for predicting a gradient of a road surface on which a vehicle is traveling, comprising:

the space slope curved surface establishing module is used for establishing a space slope curved surface and setting a vehicle running area as follows: a gradient perceptible area, a gradient imperceptible area and a gradient estimable area;

the slope angle observation value setting module is used for setting slope angles of the slope perceptible area and the slope estimable area as observation values, acquiring a ground slope value and a vehicle downhill slope value in front of the vehicle, and calculating an actual slope value of the slope imperceptible area at the current moment;

the weighted comprehensive measurement error calculation module is used for setting a constraint condition based on weight based on the observed value and determining the minimum value of the error by calculating the weighted comprehensive measurement error;

the optimal weight value decision module is used for acquiring the optimal weight value of the constraint condition in real time, and making a decision according to the optimal weight value to obtain a longitudinal slope angle and a lateral slope angle of a certain point in a slope imperceptible area at a future moment;

the unit normal vector calculation module of the slope vector and the slope plane predicts the track of the vehicle, establishes a longitudinal slope vector and a lateral slope vector by combining a longitudinal slope angle and a lateral slope angle at a certain point in the future time in the slope imperceptible area, and establishes a unit normal vector of the slope plane of the imperceptible area at the future time;

and the vector rotation and matrix calculation module is used for rotating the longitudinal gradient vector and the lateral gradient vector around the unit normal vector, performing matrix calculation and solving the gradient value of the vehicle at a certain point in the future.

An electronic device, comprising: the system comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus; the memory has stored therein a computer program which, when executed by the processor, causes the processor to perform the steps of the method.

A computer readable storage medium storing a computer program executable by an electronic device, the computer program, when run on the electronic device, causing the electronic device to perform the steps of the method.

A vehicle, comprising in particular:

an electronic device for implementing the method of any one of claims 1 to 7;

a processor running a program which, when executed, performs the steps of the method of any one of claims 1 to 7 on data output from the electronic device;

a storage medium for storing a program which, when executed, performs the steps of the method on data output from an electronic device.

Compared with the prior art, the invention has the following advantages:

the invention can predict the gradient of the road surface in front of the vehicle, realizes the continuous prediction of the gradient of the road surface in front of the vehicle, and provides continuous and accurate gradient signals for the functions of vehicle dynamic state prediction, intelligent driving and the like. The significance of the slope estimation and prediction is not only in the perception of the road terrain, but also in the estimation and prediction of the slope of the wheel of the vehicle at the moment or in the future, thereby providing a basis for accurately identifying the future dynamic state of the vehicle.

Aiming at the blind area of the vehicle-mounted vision sensor, the invention establishes a space gradient curved surface and sets the vehicle running area as follows: the method comprises the steps of calculating the length value and the gradient angle of an imperceptible area, combining a predicted vehicle track, fusing the gradient perceptible area and the gradient estimable area, and finally predicting the gradient value of a vehicle at a certain future moment, so that perception in the area range near the front of the vehicle is realized, and the visual angle limit of a visual sensor is broken through.

After the space curved surface is obtained, the position and the course angle of the vehicle at a future moment need to be predicted, track points of the vehicle at a future state need to be estimated based on the vehicle controller according to the dynamic parameter prediction information, the vehicle without a track prediction function can be predicted according to the current state of the vehicle, a corresponding technical effect is achieved, and the adaptability of the vehicle in different types of vehicles is expanded.

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 embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.

Fig. 1 is a flowchart of a method of predicting a gradient of a road surface on which a vehicle travels according to the present invention.

Fig. 2 is a schematic diagram of a road surface gradient prediction system for vehicle running of the present invention.

Fig. 3 is a schematic structural view of a vehicle gradient sensible area.

FIG. 4 is a schematic diagram of the relationship between the sensor sensing angle and the actual slope angle.

FIG. 5 is a schematic diagram of the angular dimensions of the calculated grade imperceptible region.

FIG. 6 is a schematic representation of vehicle motion parameters required for grade prediction.

FIG. 7 is a block diagram of a single iteration cycle in making a continuous prediction of vehicle slope conditions at a future time.

Fig. 8 is a system architecture diagram of an electronic device.

Detailed Description

The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. 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.

As shown in fig. 1, the flowchart of the method for predicting the gradient of the traveling road surface of the vehicle specifically includes:

step S1, establishing a space gradient curved surface, and setting a vehicle running area as follows: a gradient perceptible area, a gradient imperceptible area and a gradient estimable area;

specifically, the gradient sensible area is specifically: a vehicle front gradient area obtained based on a visual sensor field angle;

the gradient estimable area is a gradient area under the wheels of the vehicle;

Specifically, obtaining a ground slope value and a vehicle downhill slope value in front of the vehicle specifically includes: front road surface longitudinal slope included angle alpha _c Actual front road surface slope angle alpha _f The included angle alpha of the vehicle body relative to the ground _s And the actual slope angle alpha of the ground under the vehicle _r Wherein:

the actual front road surface gradient angle is specifically an included angle between a front road surface plane and a horizontal plane in the longitudinal direction of the vehicle;

the actual slope angle of the ground under the vehicle is the actual slope angle between the ground under the vehicle and the horizontal plane;

included angle alpha between vehicle body and ground _s Satisfies the formula:

wherein: h _fl Is the left front suspension height, H _fr Is the right front suspension height, H _rl Left rear suspension height, H _rr Is the right rear suspension height, L _axis Is the vehicle wheelbase;

β _f ＝β _c +β _s +β _r

wherein beta is _s The angle is determined by:

wherein: beta is a _f Is a lateral slope angle of the front pavement plane; beta is a _c The lateral included angle between the plane of the vehicle body and the plane of the front pavement; beta is a _r Is a side slope angle under the vehicle wheel; l is _wheelbase The wheel tracks on the two sides of the vehicle.

In particular, the length L of the imperceptible region _sg The following formula is satisfied:

in the above formula: h _g The vertical distance between the sensor and the ground under the vehicle; h _b The distance between the sensor and the ground of the vehicle body is a fixed value; r is a radical of hydrogen _w Is the wheel radius; alpha is alpha _sd The included angle between the lower side line of the visual angle of the camera and the vertical line of the vehicle bottom plate is a fixed value; alpha is alpha _sg The included angle between the lower side line of the visual angle of the camera and the plane of the ground surface under the vehicle is set; l is _sd Depth information perceived for the lower line of the camera view angle; l is a radical of an alcohol _wc The distance between the sensor and the axis of the front wheel in the advancing direction of the vehicle is approximately a fixed value; l is _sg The length of the imperceptible area is composed of two sections of road surface lengths.

It is to be noted that: l mentioned above _wc In order to approximate the distance between the sensor and the axis of the front wheel in the forward direction of the vehicle to a fixed value, not an unclear description, the person skilled in the art can combine L with engineering practice, technical manuals, and textbooks according to his general technical knowledge _wc Is determined to be within a certain range because of L _wc Is well defined, L _wc Is the distance of the sensor from the axis of the front wheel in the direction of travel of the vehicle, although L may be different due to different vehicle models or actual road conditions _wc There may be some differences, which are common, expected and calculable in engineering practice, that is, a fixed value is not an ambiguous description, but means that the distance can be described by a range of intervals.

S2, setting slope angles of a slope sensible area and a slope estimable area as observed values, and acquiring a ground slope value and a vehicle descending slope value in front of a vehicle for calculating an actual slope value of a slope insensible area at the current moment;

specifically, for the gradient value of the imperceptible region, the longitudinal side gradient angles of the front plane and the rear plane are respectively used along the length L of the imperceptible region _sg Carrying out weighting integration, and determining a weighting curve coefficient by using the gradient signal covariance;

e _j (k)＝X(k|k-1)-O _j (k)j＝1，2

wherein e is _j (k) To measure one component in the error vector, X (k | k-1) is the slope value at time k predicted by the previous time;

the integrated measurement error after weighting is:

e ^* (k)＝[r ₁ (k)e _j (k)，r ₂ (k)e ₂ (k)] ^T

wherein T is a transposed symbol;

the least square method is utilized to select the optimal weighting weight, and the sum of squares of errors can be obtained:

e ^*T (k)e ^* (k)＝(r ₁ (k)e _j (k)) ² +(r ₂ (k)e ₂ (k)) ²

using r ₁ +r ₂ And =1 as a constraint condition, the minimum value is obtained from the above equation, and the minimum value can be obtained by using a lagrange extreme method:

In particular, along the length L of the imperceptible region _sg Performing smoothing processing, taking the direction of the vehicle head as a coordinate starting point, performing normalization processing on the length of the region, and distributing weight by a quadratic function to obtain the following formula:

in the above formula: e is the front and back plane weighting weight that changes with distance; k is a radical of _r Is a weightA coefficient determining the curvature of the weighting curve; x is L _sg The distance in the direction of the vehicle head after normalization; s is the distance in the longitudinal direction of the vehicle from the front axle of the vehicle at the gradient prediction point.

S3, setting a constraint condition based on the weight based on the observed value, and determining the minimum value of the error by calculating the weighted comprehensive measurement error;

s4, obtaining an optimal weight value of a constraint condition in real time, and making a decision according to the optimal weight value to obtain a longitudinal slope angle and a lateral slope angle of a certain point in a slope imperceptible area at a future moment;

step S5, predicting a vehicle track, establishing a longitudinal gradient vector and a lateral gradient vector by combining a longitudinal gradient angle and a lateral gradient angle of a certain point in a gradient imperceptible area at a future moment, and establishing a unit normal vector of a gradient plane of the gradient imperceptible area at the future moment;

specifically, when the vehicle track is predicted, if the vehicle does not have the track prediction function, the current kinematic parameters of the vehicle and a vehicle dynamic model are used for estimating the longitudinal driving mileage and the course deflection angle at the future moment;

And S6, rotating the longitudinal gradient vector and the lateral gradient vector around the unit normal vector, and performing matrix calculation to obtain the gradient value of the vehicle at a certain point in the future.

For the method steps disclosed in the above embodiments, the method steps are expressed as a series of action combinations for simplicity of description, but those skilled in the art should understand that the embodiments are not limited by the described action sequences, because some steps can be performed in other sequences or simultaneously according to the embodiments of the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.

predicting a driving force/braking force of the vehicle at a future time using the driving force/longitudinal force prediction model with an accelerator pedal position and a brake pedal position as input signals;

according to the longitudinal speed and the yaw angular speed of the vehicle at the current moment, predicting the longitudinal travel mileage and the course deflection angle of the vehicle under the current coordinate system at the future moment;

The system for predicting the gradient of the road surface on which a vehicle travels, as shown in fig. 2, specifically includes:

the slope vector and slope plane unit normal vector calculation module is used for predicting the track of the vehicle, establishing a longitudinal slope vector and a lateral slope vector by combining a longitudinal slope angle and a lateral slope angle of a certain point in a slope imperceptible area at a future moment, and establishing a unit normal vector of a slope plane of the slope plane in the slope imperceptible area at the future moment;

It should be noted that, although only the basic function blocks of the traveling road surface gradient prediction system of the vehicle are disclosed in the present embodiment, the composition of the present system is not meant to be limited to the above basic function blocks, but rather, the present embodiment is intended to express the meaning that: on the basis of the basic functional modules, a person skilled in the art can combine the prior art to add one or more functional modules arbitrarily to form an infinite number of embodiments or technical solutions, that is, the present system is open rather than closed, and the protection scope of the present invention claims should not be considered to be limited to the disclosed basic functional modules because the present embodiment discloses only individual basic functional modules. Meanwhile, for convenience of description, the above devices are described as being divided into various units and modules by functions, respectively. Of course, the functions of the units and modules may be implemented in one or more software and/or hardware when implementing the invention.

The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.

As shown in fig. 3, the relationship between the sensor sensing angle and the actual slope angle is schematically illustrated, the vision sensor can obtain the relative angle between the front road surface and the vehicle body plane, and the specific image processing and calculating method belongs to the mature prior art. The vehicle is influenced by a visual angle FOV of a vision sensor, a sensor blind area and the like, and a sensible area of the front slope is limited, so that a continuous slope value in front of the vehicle cannot be obtained. The method disclosed by the embodiment of the invention comprises two steps of establishing a space slope curved surface, respectively constructing a space plane in front of a vehicle and a space plane under a wheel by using a vehicle front sensing slope signal and a vehicle under-wheel estimating slope signal based on kinematics, then obtaining a weighting curve coefficient based on a slope signal covariance, and further obtaining a continuous weighting curve to realize fusion of the front space plane and the rear space plane in a slope insensible area, thereby obtaining the continuous space curved surface. And then predicting the gradient value of the vehicle at a certain future moment based on the calculated space gradient curved surface and the predicted running track.

According to the common sense of driving, life and traffic, the following information: the vehicle driving area comprises the front part of the vehicle and the lower part of the vehicle body, the gradient value of the road surface does not depend on the road surface, and conversion is needed according to a vehicle coordinate system, for example, the gradient of the vehicle is positive when facing an uphill slope and the gradient of the vehicle is negative when facing a downhill slope.

FIG. 4 is a diagram of the relationship between the sensing angle of the sensor and the actual grade angle, where α is _c The included angle between the plane of the vehicle body and the longitudinal gradient of the front road surface is the front longitudinal gradient signal output by the vision sensor; alpha is alpha _f The included angle between the plane of the front road surface and the horizontal plane in the longitudinal direction of the vehicle is the actual slope angle of the front road surface; alpha is alpha _s Is the included angle of the vehicle body relative to the ground under the vehicle; alpha is alpha _r The actual slope angle of the ground under the vehicle is calculated and output by the vehicle controller, and the value alpha of the vision sensor can be obtained _c The formula converted into the actual front road surface slope angle is as follows:

α _f ＝α _c +α _s +α _r

wherein alpha is _s The angle is determined by:

wherein H _fl Is the left front suspension height, H _fr Is the right front suspension height, H _rl Left rear suspension height, H _rr Is the right rear suspension height, L _axis Is the vehicle wheelbase.

Similarly, the lateral slope angle beta of the front road surface plane can be obtained _f ：

β _f ＝β _c +β _s +β _r

Wherein: beta is a _s The angle is determined by:

As shown in the schematic diagram of calculating the size of each angle of the gradient imperceptible region in fig. 5, if the gradient information in the imperceptible region is desired to be obtained, the length of the imperceptible region is also determined. The mounting position of the vision sensor is fixed with the height of the vehicle body bottom plate, the position of the angle of view of the sensor is fixed relative to the vehicle body, and the length L of the imperceptible area under typical terrain _sg Can be determined by the following formula:

in the formula: h _g The vertical distance between the sensor and the ground under the vehicle; h _b The distance between the sensor and the ground of the vehicle body is a fixed value; r is _w Is the wheel radius; alpha is alpha _sd The included angle between the lower sideline of the visual angle of the camera and the vertical line of the vehicle floor is a fixed value; alpha is alpha _sg The included angle between the lower side line of the visual angle of the camera and the plane of the ground surface under the vehicle is set; l is _sd Depth information perceived for the lower line of the camera view angle; l is _wc The distance between the sensor and the axis of the front wheel in the advancing direction of the vehicle is approximately a fixed value; l is a radical of an alcohol _sg The length of the imperceptible area is composed of two sections of road surface lengths.

The above formula is a method for calculating the length of an imperceptible area where a vehicle runs at the junction of two typical slopes, the actual road surface gradient change condition is more, but the error of the method is smaller under different working conditions, and the method can be used for subsequent calculation.

After the calculation, the longitudinal slope angle of the slope plane closest to the vehicle in the front sensing area is alpha _f0 The side slope angle is beta _f0 The longitudinal slope angle of the lower plane of the vehicle wheel is alpha _r The lateral slope angle of the lower plane of the vehicle wheel is beta _r . For the gradient value of the intermediate gradient imperceptible region, respectively using the longitudinal side gradient angles of the front plane and the rear plane along the length L of the imperceptible region _sg And performing weighted fusion, and determining a weighted curve coefficient by using the slope signal covariance. Can regard as two observed values to middle imperceptible district's slope angle with place ahead plane slope angle, back plane slope angle, assume that middle imperceptible district is a plane, then has:

O _j (k)＝X(k)+n _j (k)j＝1，2

wherein: o is _j (k) Is a slope observed value at the moment k; x (k) the actual grade value of the middle plane; n is _j (k) And observing noise.

Since the front and back observations are independent of each other, the estimate of the mid-plane can be expressed as:

wherein: r is ₁ 、r ₂ Respectively weighting the front plane slope angle and the rear plane slope angle; o is ^* (k) Is the weighted mid-plane slope angle.

The measurement error of the front and rear gradient measurement values at the time k is as follows:

e _j (k)＝X(k|k-1)-O _j (k)j＝1，2

wherein e is _j (k) To measure one component of the error vector, X (k | k-1) is the slope value at time k predicted from the previous time. The integrated measurement error after weighting is:

e ^* (k)＝[r ₁ (k)e _j (k)，r ₂ (k)e ₂ (k)] ^T

and T is a transposed symbol, and the matrix written vertically is inconvenient to view and type, so that the transposed symbol is added on the matrix written transversely, and the matrix written transversely is convenient to view and type. The transposing of matrix is a common knowledge in the art, and the row (or column) of matrix a is replaced by the column (or row) of the same ordinal number to obtain a new matrix, which is called the transposing matrix of matrix a.

e ^*T (k)e ^* (k)＝(r ₁ (k)e _j (k)) ² +(r ₂ (k)e ₂ (k)) ²

and obtaining the optimal weight for fusing the front and rear slope planes at the moment k, wherein the optimal weight represents the confidence level of the front and rear planes, and the value is changed in real time along with time. In practice, the intermediate imperceptible region is not a plane but a continuous curved surface, and the length L of the imperceptible region is required _sg And carrying out smoothing treatment. Taking the direction of the vehicle head as a coordinate starting point, carrying out normalization processing on the length of the region and distributing weight by a quadratic function, so as to obtain the following formula:

in the formula: e is the front and back plane weighting weight that changes with distance; k is a radical of formula _r Determining the curvature of the weighting curve for the weighting coefficients; x is L _sg The distance in the direction of the vehicle head after normalization; s is the distance in the longitudinal direction of the vehicle from the front axle of the vehicle at the gradient prediction point.

In summary, the longitudinal side slope angle at a point where the vehicle front axle forward distance is s in the vehicle longitudinal direction should be as follows:

wherein: alpha (alpha) ("alpha") _m Longitudinal slope of the predicted point position; beta is a _m The lateral slope of the predicted point location is determined.

Due to the factors that the lateral speed of the vehicle is small and the lateral sensing range of the sensor is small, the method only considers the distribution situation of the weight in the longitudinal direction. By changing the position of s, a continuous curved surface can be obtained within the imperceptible region. The shape of the curved surface is dynamically adjusted by comparing the observation noise of the front plane and the observation noise of the rear plane, namely, if the front sensor senses that the gradient noise is low, the curved surface is more informed to the front plane; if the estimated plane noise of the rear vehicle is lower, the curved surface is more informed to the rear plane.

As shown in fig. 6, the vehicle motion parameter diagram required for slope prediction, the slope may be predicted based on the dynamic state in the present embodiment:

after the space curved surface is obtained, the position and the course angle of the vehicle at the future moment need to be predicted. The track points of the vehicle in the future state, which are estimated according to the dynamic parameter prediction information by the vehicle controller, are calculated, and if the track points do not have the information, the track points can be predicted according to the current state of the vehicle.

If the vehicle does not have the track prediction function, the longitudinal driving mileage s of the vehicle at the future moment can be measured by using the current kinematic parameters of the vehicle and the vehicle dynamics model _k+n Course deflection angle theta _k+n The estimation is carried out in such a way that,the specific method comprises the following steps:

because the operation frequency of the driver is low, the operation instruction given by the driver from the moment k to the moment k + n is assumed to be unchanged, namely the values of the accelerator pedal position and the brake pedal position, namely the steering wheel rotation angle are constant. At time k, the vehicle is predicted to be [ k, k +1, k +2 … … k + n ] using the driving force/longitudinal force prediction model and the accelerator pedal position and the brake pedal position as input signals]Time series of driving force/braking force at time

Similarly, the front wheel steering angle time sequence of the vehicle at the time from k to k + n is obtained by using the steering angle input of the vehicle steering wheel to the front wheel steering angle prediction model

Using the current time longitudinal speed of the vehicle at time k

Current yaw rate

Predicting the longitudinal driving mileage of the vehicle under the current coordinate system at the moment of k +1

And course deflection angle theta ^k+1 The following formula:

wherein: t is t ₀ Is the unit time of each segment interval from the moment k to k + n.

Use of

The formula is combined to obtain the time of the vehicle at the k +1 momentThe space gradient plane of (a):

combining the longitudinal and lateral gradient angle at the moment of k +1 and the position of the vehicle to establish a longitudinal and lateral gradient vector

Establishing unit normal vector of space gradient plane at k +1 moment

Make it

Around the unit normal vector

At theta ^k+1 The angle is rotated:

finally based on

The longitudinal gradient angle of the vehicle at the k +1 moment can be successfully obtained

Side slope angle

A single iteration cycle computation block diagram for continuous prediction of vehicle grade at a future time, using data from a predictive model, as shown in FIG. 7

And inputting the gradient information at the k +1 moment which is just calculated into a dynamic model to finish the longitudinal speed at the k +1 moment

Yaw rate

And (4) updating. The calculation step is an iterative calculation step, and continuous prediction of the vehicle gradient situation at the moment k +2 and k +3 … … k + n can be realized by repeating the steps.

If the current time is k, if the gradient situation of the vehicle at the time of k + n is expected to be predicted, the vehicle position at the time of k + n under the current vehicle coordinate system is needed, if the vehicle has the function of track prediction, the information is regarded as known, and the longitudinal driving mileage s at the time of k + n under the current vehicle coordinate system exists _k+n Course deflection angle theta _k+n 。

The embodiment of the invention realizes continuous estimation of information on the slope space in the front imperceptible area at any moment, then realizes the prediction of the vehicle position and the course angle based on the prediction calculation of the vehicle dynamic parameters, realizes the prediction of the vehicle slope condition at the future moment by iterative update calculation, and adopts a driving force/longitudinal force prediction model, a front wheel steering angle prediction model and a vehicle dynamic model as the prior art, so that the specific application of the driving force/longitudinal force prediction model, the front wheel steering angle prediction model and the vehicle dynamic model in the embodiment can be realized by the technical personnel in the field by means of the mastered prior art.

As shown in fig. 8, the present invention discloses an electronic device and a storage medium corresponding to the method and the system for predicting the gradient of a traveling road surface of a vehicle, on the basis of the method and the system for predicting the gradient of the traveling road surface of the vehicle:

an electronic device, comprising: the system comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus; the memory has stored therein a computer program which, when executed by the processor, causes the processor to carry out the steps of a method of predicting a road surface gradient on which a vehicle is traveling.

A computer-readable storage medium storing a computer program executable by an electronic device, when the computer program is run on the electronic device, causes the electronic device to execute steps of a method for predicting a gradient of a road surface on which a vehicle travels.

The communication bus mentioned in the electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this is not intended to represent only one bus or type of bus.

The electronic device includes a hardware layer, an operating system layer running on top of the hardware layer, and an application layer running on top of the operating system. The hardware layer includes hardware such as a Central Processing Unit (CPU), a Memory Management Unit (MMU), and a Memory. The operating system may be any one or more computer operating systems that implement control of the electronic device through a Process (Process), such as a Linux operating system, a Unix operating system, an Android operating system, an iOS operating system, or a windows operating system. In the embodiment of the present invention, the electronic device may be a handheld device such as a smart phone and a tablet computer, or may also be an electronic device such as a desktop computer and a portable computer, which is not particularly limited in the embodiment of the present invention.

The execution main body of the electronic device control in the embodiment of the present invention may be the electronic device, or a functional module capable of calling a program and executing the program in the electronic device. The electronic device may obtain the firmware corresponding to the storage medium, the firmware corresponding to the storage medium is provided by a vendor, and the firmware corresponding to different storage media may be the same or different, which is not limited herein. After the electronic device acquires the firmware corresponding to the storage medium, the firmware corresponding to the storage medium may be written into the storage medium, specifically, the firmware corresponding to the storage medium is burned into the storage medium. The process of burning the firmware into the storage medium can be realized by adopting the prior art, and details are not described in the embodiment of the present invention.

The electronic device may further acquire a reset command corresponding to the storage medium, where the reset command corresponding to the storage medium is provided by a vendor, and the reset commands corresponding to different storage media may be the same or different, and are not limited herein.

At this time, the storage medium of the electronic device is a storage medium in which the corresponding firmware is written, and the electronic device may respond to the reset command corresponding to the storage medium in which the corresponding firmware is written, so that the electronic device resets the storage medium in which the corresponding firmware is written according to the reset command corresponding to the storage medium. The process of resetting the storage medium according to the reset command can be implemented by the prior art, and is not described in detail in the embodiment of the present invention.

The invention also discloses a vehicle with a gradient prediction function, which specifically comprises:

the electronic equipment is used for realizing the method for predicting the gradient of the running road surface of the vehicle;

a processor that runs a program, and when the program is run, performs a step of a vehicle traveling road surface gradient prediction method on data output from the electronic device;

a storage medium storing a program that, when executed, executes the steps of the vehicle travel surface gradient prediction method on data output from an electronic device.

The vehicle with the slope prediction function can be combined with the forward-looking perceived slope and the self slope, so that the slope prediction of an imperceptible area is realized; fusion of front and rear gradient information is realized by using a covariance weighting mode; and predicting the position corner by using the dynamic information, determining a slope value by using the position corner information, updating the dynamic information by using the predicted value of the slope, and realizing continuous prediction of the slope at any time in the future by using a multi-iteration calculation method.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

It should be noted that certain terms are used throughout the description and following claims to refer to particular components. As one skilled in the art will appreciate, vehicle manufacturers may refer to a component by different names. The present specification and claims do not intend to distinguish between components that differ in name but not function. In the following description and in the claims, the terms "include" and "comprise" are used in an open-ended fashion, and thus should be interpreted to mean "include, but not limited to. The following description is of the preferred embodiment for carrying out the invention, but the description is made for the purpose of general principles of the specification and is not intended to limit the scope of the invention. The scope of the present invention is defined by the appended claims.

In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

In addition, the technical solutions in the embodiments of the present invention may be combined with each other, but it must be based on the realization of those skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination of technical solutions should not be considered to exist, and is not within the protection scope of the present invention.

In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

Those skilled in the art will appreciate that the modules in the devices in an embodiment may be adaptively changed and arranged in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Moreover, those skilled in the art will appreciate that although some embodiments described herein include some features included in other embodiments, not others, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.

Various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components of a device for distributing messages according to embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website, or provided on a carrier signal, or provided in any other form.

Claims

1. A method for predicting the gradient of a road surface on which a vehicle runs is characterized by specifically comprising the following steps:

establishing a space slope curved surface, and setting a vehicle driving area as follows: a gradient perceptible area, a gradient imperceptible area and a gradient estimable area;

2. The method for predicting the gradient of a road surface on which a vehicle travels according to claim 1, wherein the gradient-sensible area is specifically: a vehicle front gradient area obtained based on a visual sensor field angle;

the estimated gradient area is a gradient area under the wheels of the vehicle;

3. The method of predicting a traveling surface gradient of a vehicle according to claim 1,

the method for acquiring the ground gradient value and the vehicle downhill gradient value in front of the vehicle specifically comprises the following steps: front road surface longitudinal slope included angle alpha _c Actual front road surface slope angle alpha _f The included angle alpha of the vehicle body relative to the ground _s And the actual slope angle alpha of the ground under the vehicle _r Wherein:

the actual slope angle of the ground under the vehicle is the actual slope angle of the ground under the vehicle and the horizontal plane;

included angle alpha between vehicle body and ground _s Satisfies the formula:

wherein: h _fl Left front suspension height, H _fr Is the right front suspension height, H _rl Is the left rear suspension height, H _rr Is the right rear suspension height, L _axis Is the vehicle wheelbase;

β _f ＝β _c +β _s +β _r

wherein beta is _s The angle is determined by:

wherein: beta is a _f Is a lateral slope angle of the front pavement plane; beta is a _c The lateral included angle between the plane of the vehicle body and the plane of the front road surface; beta is a _r Is a side slope angle under the vehicle wheel; l is _wheelbase The wheel tracks on the two sides of the vehicle.

4. The method of predicting a traveling surface gradient of a vehicle according to claim 1,

length L of the imperceptible region _sg The following formula is satisfied:

in the above formula: h _g The vertical distance between the sensor and the ground under the vehicle; h _b The distance between the sensor and the ground of the vehicle body is a fixed value; r is _w Is the wheel radius; alpha is alpha _sd The included angle between the lower sideline of the visual angle of the camera and the vertical line of the vehicle floor is a fixed value; alpha (alpha) ("alpha") _sg The included angle between the lower side line of the visual angle of the camera and the plane of the ground surface under the vehicle is set; l is _sd Depth information perceived for the lower line of the camera view angle; l is _wc For the sensor in the forward direction of the vehicleThe distance from the axis of the front wheel upwards is approximately a fixed value; l is _sg The length of the imperceptible area is composed of two sections of road surface lengths.

5. The method of predicting a traveling surface gradient of a vehicle according to claim 1,

for the gradient value of the imperceptible region, respectively using the longitudinal side gradient angles of the front plane and the rear plane along the length L of the imperceptible region _sg Carrying out weighting integration, and determining a weighting curve coefficient by using the gradient signal covariance;

regarding the front plane slope angle and the rear plane slope angle as two observed values of the middle imperceptible region slope angle, assuming that the middle imperceptible region is a middle plane, and solving an estimated value expression of the middle plane:

r ₁ 、r ₂ weight of the front and rear plane slope angles, O ^* (k) The weighted mid-plane slope; calculating the measurement error of the front and rear gradient measurement values at the k moment:

e _j (k)＝X(k|k-1)-O _j (k)j＝1，2

the integrated measurement error after weighting is:

e ^* (k)＝[r ₁ (k)e _j (k)，r ₂ (k)e ₂ (k)] ^T

wherein T is a transposed symbol;

e ^*T (k)e ^* (k)＝(r ₁ (k)e _j (k)) ² +(r ₂ (k)e ₂ (k)) ²

using r is ₁ +r ₂ With =1 as a constraint condition, the minimum value is obtained for the above equation, and the minimum value can be obtained by using the lagrange extreme method:

6. The method of predicting a gradient of a road surface on which a vehicle travels according to claim 5,

along the length L of the imperceptible region _sg And performing smoothing treatment, taking the direction of the vehicle head as a coordinate starting point, performing normalization treatment on the length of the region, and distributing weight by a quadratic function to obtain the following formula:

in the above formula: e is the front and back plane weighting weight that changes with distance; k is a radical of _r Determining the curvature of the weighting curve for the weighting coefficient; x is L _sg The distance in the direction of the vehicle head after normalization; s is the distance of the slope prediction point from the front axle of the vehicle in the longitudinal direction of the vehicle;

in summary, in the vehicle longitudinal direction, the longitudinal side slope angle at a certain point where the vehicle front axle front distance is s is:

wherein: alpha is alpha _m Longitudinal slope of the predicted point position; beta is a beta _m The lateral slope of the predicted point location is determined.

7. The method according to claim 1, wherein when predicting the vehicle trajectory, if the vehicle does not have a trajectory prediction function, the method estimates the longitudinal driving range and the heading deflection angle at the future time by using the current kinematic parameters of the vehicle and the vehicle dynamics model;

8. A system for predicting a gradient of a road surface on which a vehicle is traveling, comprising:

9. An electronic device, comprising: the system comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus; the memory has stored therein a computer program which, when executed by the processor, causes the processor to perform the steps of the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that it stores a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of the method of any one of claims 1 to 7.

11. A vehicle, characterized by specifically including:

an electronic device for implementing the method of any one of claims 1 to 7;

storage medium for storing a program which, when executed, performs the steps of the method of any one of claims 1 to 7 on data output from an electronic device.