WO2024051188A1 - 一种车辆行驶路面坡度预测方法、系统及其车辆 - Google Patents

一种车辆行驶路面坡度预测方法、系统及其车辆 Download PDF

Info

Publication number
WO2024051188A1
WO2024051188A1 PCT/CN2023/092417 CN2023092417W WO2024051188A1 WO 2024051188 A1 WO2024051188 A1 WO 2024051188A1 CN 2023092417 W CN2023092417 W CN 2023092417W WO 2024051188 A1 WO2024051188 A1 WO 2024051188A1
Authority
WO
WIPO (PCT)
Prior art keywords
slope
vehicle
angle
area
value
Prior art date
Application number
PCT/CN2023/092417
Other languages
English (en)
French (fr)
Inventor
洪日
张建
王超
谢飞
王御
韩亚凝
李雅欣
闫善鑫
李扬
Original Assignee
中国第一汽车股份有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 中国第一汽车股份有限公司 filed Critical 中国第一汽车股份有限公司
Publication of WO2024051188A1 publication Critical patent/WO2024051188A1/zh

Links

Classifications

    • 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
    • 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
    • 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
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/10—Accelerator pedal position
    • 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
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/12—Brake pedal position
    • 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
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/18—Steering angle

Definitions

  • the present invention relates to a method and system for predicting the slope of a road surface and a vehicle thereof, and in particular to a method and system for predicting the slope of a vehicle driving road surface and a vehicle thereof.
  • Another slope prediction method is based on the vehicle's own visual sensor, using devices with depth information such as binocular cameras and lidar to perceive the relative slope of the road ahead of the vehicle.
  • the problem is that the sensor has a blind spot in front of the vehicle. Perception cannot be achieved in a relatively close range, that is, it can only recognize the slope far in front of the vehicle, and the sensor's viewing angle is limited, and it cannot obtain slope information in a large range on the left and right sides of the vehicle, that is, when the vehicle turns at a large angle, it cannot Get the slope information on the vehicle's forward trajectory.
  • the purpose of the present invention is to provide a method and system for predicting the slope of the road surface in front of the vehicle and its vehicle, which can predict the slope of the road surface in front of the vehicle, realize continuous prediction of the slope of the road surface in front of the vehicle, and provide continuous prediction for vehicle dynamic state prediction, intelligent driving and other functions. , accurate slope signal.
  • Another technical problem to be solved by the present invention is to target the blind area of the vehicle-mounted visual sensor, realize the perception of the area close to the front of the vehicle, and break through the viewing angle limitation of the visual sensor.
  • Another technical problem to be solved by the present invention is to construct a continuous spatial surface in front of the vehicle in real time by combining the vehicle wheel slope and the perceived front slope information, and realize the prediction of the vehicle trajectory and future slope information through vehicle dynamics parameter prediction.
  • the technical problem that can also be solved by the present invention is that different vehicles have or do not have the vehicle trajectory prediction function and can predict the road gradient of the vehicle based on the dynamic state.
  • the present invention provides the following solutions:
  • a method for predicting the slope of a vehicle's driving road surface specifically including:
  • Predict the vehicle trajectory combine the longitudinal slope angle and lateral slope angle at a certain point in the future time in the slope-imperceptible area, establish the longitudinal slope vector and lateral slope vector, and establish the unit normal vector of the slope plane in the future time-insensible area;
  • the slope-perceivable area is specifically: the slope area in front of the vehicle obtained based on the field of view angle of the visual sensor;
  • the slope estimable area is the slope area under the vehicle wheels
  • the slope-imperceptible area is located between the slope-perceivable area and the slope-estimateable area.
  • the acquisition of the ground slope value in front of the vehicle and the slope value under the vehicle specifically includes: the longitudinal slope angle ⁇ c of the road ahead, the actual slope angle ⁇ f of the road ahead, the angle ⁇ s between the vehicle body and the ground, and the actual ground angle ⁇ s under the vehicle.
  • Slope angle ⁇ r where:
  • the angle between the longitudinal slope of the road ahead is specifically the angle between the vehicle body plane and the longitudinal slope of the road ahead;
  • the actual slope angle of the road ahead is specifically the angle between the plane of the road ahead and the horizontal plane in the longitudinal direction of the vehicle;
  • the angle between the vehicle body and the ground is the angle between the vehicle body and the ground under the vehicle;
  • 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;
  • the actual slope angle of the road ahead the longitudinal slope angle of the road ahead + the angle between the car body and the ground + the actual slope angle of the ground under the car;
  • 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
  • the ⁇ s angle is determined by the following formula:
  • ⁇ f is the lateral slope angle of the front road surface
  • ⁇ c is the lateral angle between the vehicle body plane and the front road surface plane
  • ⁇ r is the lateral slope angle under the vehicle wheels
  • L wheelbase is the wheelbase on both sides of the vehicle.
  • H g is the vertical distance between the sensor and the ground under the car
  • H b is the distance between the sensor and the ground of the car body, which is a fixed value
  • r w is the wheel radius
  • ⁇ sd is the lower edge of the camera's perspective and the vehicle floor
  • the angle between vertical lines is a fixed value
  • ⁇ sg is the angle between the lower edge of the camera's perspective and the ground plane under the car
  • L sd is the depth information sensed by the lower edge of the camera's perspective
  • L wc is the sensor in the forward direction of the vehicle
  • the distance from the front wheel axis is approximately a fixed value
  • L sg is the length of the imperceptible area, which consists of two sections of road surface length.
  • the longitudinal and lateral slope angles of the front and rear planes are used for weighted integration along the length of the imperceptible area L sg , and the slope signal covariance is used to determine the weighted curve coefficient;
  • k-1)-O j (k)j 1,2
  • e j (k) is a component in the measurement error vector
  • k-1) is the slope value at time k predicted by the previous time
  • T is the transposition symbol
  • the optimal weight for merging the front and rear slope planes at time k is obtained, which represents the degree of acceptance of the front and rear slope planes. This value changes in real time with time.
  • e is the weight of the front and rear planes that changes with distance
  • k r is the weight coefficient, which determines the curvature of the weighted curve
  • x is the distance in the front direction of the vehicle after normalization of L sg
  • s is the longitudinal direction of the vehicle , the slope prediction point is based on the distance from the front axle of the vehicle.
  • the longitudinal and lateral slope angle of a point at a distance s in front of the front axle of the vehicle is:
  • ⁇ m is the longitudinal slope of the predicted point position
  • ⁇ m is the lateral slope of the predicted point position
  • the vehicle's current kinematic parameters and vehicle dynamics model are used to estimate the longitudinal mileage and heading deflection angle in the future;
  • a vehicle road surface slope prediction system specifically including:
  • the spatial gradient surface creation module is used to establish the spatial gradient surface and set the vehicle driving area as: the slope-perceivable area, the slope-imperceptible area and the slope-estimateable area;
  • the slope angle observation value setting module is used to set the slope angle of the slope perceptible area and the slope estimable area as the observation value, obtain the ground slope value in front of the vehicle and the slope value under the vehicle, and is used to calculate the non-perceivable slope at the current moment.
  • the actual slope value of the area
  • a weighted comprehensive measurement error calculation module used to set weight-based constraints based on the observation values, and determine the minimum value of the error by calculating the weighted comprehensive measurement error
  • the optimal weight value decision-making module is used to obtain the optimal weight value of the constraint conditions in real time, make decisions based on the optimal weight value, and obtain the longitudinal slope angle and lateral slope angle at a certain point in the future in the slope-imperceptible area;
  • the slope vector and slope plane unit normal vector calculation module predicts the vehicle trajectory, combines the longitudinal slope angle and lateral slope angle at a certain point in the future moment in the slope-imperceptible area, establishes the longitudinal slope vector and lateral slope vector, and establishes the imperceptible future moment.
  • the vector rotation and matrix calculation module rotates the longitudinal slope vector and the lateral slope vector around the unit normal vector, performs matrix calculations, and obtains the slope value of the vehicle at a certain point in the future.
  • An electronic device including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory complete communication with each other through the communication bus; a computer program is stored in the memory.
  • the steps of the method are caused by the processor.
  • a computer-readable storage medium stores a computer program that can be executed by an electronic device, and when the computer program is run on the electronic device, the electronic device performs the steps of the method.
  • a vehicle specifically including:
  • a processor runs a program, and when the program is run, the steps of the method according to any one of claims 1 to 7 are performed on the data output from the electronic device;
  • a storage medium is used to store a program that, when running, performs the steps of the method on data output from the electronic device.
  • the present invention has the following advantages:
  • the invention can predict the slope of the road surface in front of the vehicle, realize continuous prediction of the slope of the road surface in front of the vehicle, and provide continuous and accurate slope signals for vehicle dynamic state prediction, intelligent driving and other functions.
  • the significance of slope estimation and prediction in this invention is not only the perception of the road terrain itself; It lies in the estimation and prediction of the vehicle wheel slope at the current or future moment, thereby providing a basis for accurately identifying the future dynamic state of the vehicle.
  • the present invention establishes a spatial gradient surface and sets the vehicle driving area as: gradient perceptible area, gradient non-perceivable area and slope estimable area.
  • the vehicle driving area By calculating the length value and slope angle of the non-perceivable area, combined with Predict the vehicle trajectory, fuse the slope-perceivable area and the slope-estimated area, and finally predict the slope value of the vehicle at a certain time in the future, realizing the perception of the area close to the front of the vehicle and breaking through the viewing angle limitations of the visual sensor.
  • the present invention needs to predict the vehicle's position and heading angle in the future.
  • the prediction needs to be based on the trajectory points of the vehicle in the future state estimated by the vehicle controller based on the dynamic parameter prediction information. For those who do not have the trajectory prediction function , it can also be predicted based on the current status of the vehicle, and the corresponding technical effect is achieved, which expands the adaptability of the present invention in different types of vehicles.
  • Fig. 1 is a flow chart of the vehicle driving road surface gradient prediction method of the present invention.
  • Figure 2 is an architecture diagram of the vehicle road surface gradient prediction system of the present invention.
  • Figure 3 is a schematic structural diagram of the vehicle gradient sensing area.
  • Figure 4 is a schematic diagram of the relationship between the sensor sensing angle and the actual slope angle.
  • Figure 5 is a schematic diagram of calculating the angular dimensions of the slope-imperceptible area.
  • Figure 6 is a schematic diagram of vehicle motion parameters required for slope prediction.
  • Figure 7 is a calculation block diagram of a single iteration cycle when continuously predicting the vehicle gradient situation in the future.
  • Figure 8 is a system architecture diagram of the electronic device.
  • the flow chart of the vehicle road surface slope prediction method shown in Figure 1 specifically includes:
  • Step S1 establish a spatial gradient surface, and set the vehicle driving area as: the slope-perceivable area, the slope-imperceptible area, and the slope-estimated area;
  • the slope perceptible area is specifically: the slope area in front of the vehicle obtained based on the field of view of the visual sensor;
  • the slope estimable area is the slope area under the vehicle wheels
  • the slope-imperceptible region is located between the slope-perceivable region and the slope-estimated region.
  • the ground slope value in front of the vehicle and the slope value under the vehicle are obtained, including: the longitudinal slope angle of the road ahead ⁇ c , the actual slope angle of the road ahead ⁇ f , the angle ⁇ s between the vehicle body and the ground, and the actual slope angle of the ground under the vehicle.
  • ⁇ r the ground slope value in front of the vehicle and the slope value under the vehicle are obtained, including: the longitudinal slope angle of the road ahead ⁇ c , the actual slope angle of the road ahead ⁇ f , the angle ⁇ s between the vehicle body and the ground, and the actual slope angle of the ground under the vehicle.
  • the angle between the longitudinal slope of the road ahead is specifically the angle between the vehicle body plane and the longitudinal slope of the road ahead;
  • the actual slope angle of the road ahead is specifically the angle between the plane of the road ahead and the horizontal plane in the longitudinal direction of the vehicle;
  • the angle between the vehicle body and the ground is the angle between the vehicle body and the ground under the vehicle;
  • the actual slope angle of the ground under the car is the actual slope angle of the ground under the car and the horizontal plane;
  • the actual slope angle of the road ahead the longitudinal slope angle of the road ahead + the angle between the car body and the ground + the actual slope angle of the ground under the car;
  • 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
  • the ⁇ s angle is determined by the following formula:
  • ⁇ f is the lateral slope angle of the front road surface
  • ⁇ c is the lateral angle between the vehicle body plane and the front road surface plane
  • ⁇ r is the lateral slope angle under the vehicle wheels
  • L wheelbase is the wheelbase on both sides of the vehicle.
  • the length L sg of the imperceptible area satisfies the following formula:
  • H g is the vertical distance between the sensor and the ground under the car
  • H b is the distance between the sensor and the ground of the car body, which is a fixed value
  • r w is the wheel radius
  • ⁇ sd is the lower edge of the camera angle of view and The angle between the vertical lines of the vehicle floor is a fixed value
  • ⁇ sg is the angle between the lower edge of the camera's perspective and the ground plane under the vehicle
  • L sd is the depth information sensed by the lower edge of the camera's perspective
  • L wc is the sensor's movement of the vehicle forward
  • the distance from the front wheel axis in the direction is approximately a fixed value
  • L sg is the length of the imperceptible area, which consists of two sections of road surface length.
  • L wc is the distance between the sensor and the front wheel axis in the forward direction of the vehicle, which is approximately a fixed value. It is not an unclear description. Those skilled in the art can use the knowledge they have mastered to Common technical knowledge in the field, combined with engineering practice, technical manuals, and textbooks, determine L wc within a certain range, because the definition of L wc is very clear, L wc is the distance between the sensor and the front wheel axis in the forward direction of the vehicle, although There will be certain differences in L wc due to different vehicle models or actual road conditions, but this difference is common, expected and calculable in engineering practice. The approximate fixed value is not an ambiguous description, but It means that the distance can be described by an interval range.
  • Step S2 set the slope angle of the slope-perceptible area and the slope-estimateable area as the observed value, obtain the ground slope value in front of the vehicle and the slope value under the vehicle, which are used to calculate the actual slope value of the slope-unperceivable area at the current moment;
  • the longitudinal and lateral slope angles of the front and rear planes are used for weighted integration along the length of the imperceptible area L sg , and the slope signal covariance is used to determine the weighted curve coefficient;
  • k-1)-O j (k)j 1,2
  • e j (k) is a component in the measurement error vector
  • k-1) is the slope value at time k predicted by the previous time
  • T is the transposition symbol
  • the optimal weight for merging the front and rear slope planes at time k is obtained, which represents the degree of acceptance of the front and rear slope planes. This value changes in real time with time.
  • e is the weight of the front and rear planes that changes with distance
  • k r is the weight coefficient, which determines the curvature of the weighted curve
  • x is the distance in the front direction of the vehicle after normalization of L sg
  • s is the longitudinal direction of the vehicle , the slope prediction point is based on the distance from the front axle of the vehicle.
  • the longitudinal and lateral slope angle of a point at a distance s in front of the front axle of the vehicle is:
  • ⁇ m is the longitudinal slope of the predicted point position
  • ⁇ m is the lateral slope of the predicted point position
  • Step S3 Set weight-based constraints based on the observed values, and determine the minimum value of the error by calculating the weighted comprehensive measurement error;
  • Step S4 Obtain the optimal weight value of the constraint condition in real time, make decisions based on the optimal weight value, and obtain the longitudinal slope angle and lateral slope angle at a certain point in the future in the slope-imperceptible area;
  • Step S5 predict the vehicle trajectory, combine the longitudinal slope angle and lateral slope angle at a certain point in the future moment in the slope-imperceptible area, establish the longitudinal slope vector and lateral slope vector, and establish the unit normal vector of the slope plane in the future-unperceivable area. ;
  • the current kinematic parameters of the vehicle and the vehicle dynamics model are used to estimate the longitudinal mileage and heading deflection angle in the future;
  • Step S6 Rotate the longitudinal slope vector and the lateral slope vector around the unit normal vector, perform matrix calculation, and obtain the slope value of the vehicle at a certain point in the future.
  • the method steps are expressed as a series of action combinations for the purpose of simple description.
  • the embodiments of the present invention are not limited by the described action sequence because According to embodiments of the present invention, certain steps may be performed in other orders or simultaneously.
  • those skilled in the art should also know that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily necessary for the embodiments of the present invention.
  • the current kinematic parameters of the vehicle and the vehicle dynamics model are used to estimate the longitudinal mileage and heading deflection angle in the future;
  • the method steps are expressed as a series of action combinations for the purpose of simple description.
  • the embodiments of the present invention are not limited by the described action sequence because According to embodiments of the present invention, certain steps may be performed in other orders or simultaneously.
  • those skilled in the art should also know that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily necessary for the embodiments of the present invention.
  • the vehicle road surface slope prediction system shown in Figure 2 specifically includes:
  • the spatial gradient surface creation module is used to establish the spatial gradient surface and set the vehicle driving area as: the slope-perceivable area, the slope-imperceptible area and the slope-estimateable area;
  • the slope angle observation value setting module is used to set the slope angle of the slope perceptible area and the slope estimable area as the observation value, obtain the ground slope value in front of the vehicle and the slope value under the vehicle, and is used to calculate the non-perceivable slope at the current moment.
  • the actual slope value of the area
  • a weighted comprehensive measurement error calculation module used to set weight-based constraints based on the observation values, and determine the minimum value of the error by calculating the weighted comprehensive measurement error
  • the optimal weight value decision-making module is used to obtain the optimal weight value of the constraint conditions in real time, make decisions based on the optimal weight value, and obtain the longitudinal slope angle and lateral slope angle at a certain point in the future in the slope-imperceptible area;
  • the slope vector and slope plane unit normal vector calculation module predicts the vehicle trajectory, combines the longitudinal slope angle and lateral slope angle at a certain point in the future moment in the slope-imperceptible area, establishes the longitudinal slope vector and lateral slope vector, and establishes the imperceptible future moment.
  • the vector rotation and matrix calculation module rotates the longitudinal slope vector and the lateral slope vector around the unit normal vector, performs matrix calculations, and obtains the slope value of the vehicle at a certain point in the future.
  • the device embodiments described above are only illustrative.
  • the units described as separate components may or may not be physically separated.
  • the components shown as units may or may not be physical units, that is, they may be located at One location, or it can be distributed across multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of this embodiment. Persons of ordinary skill in the art can understand and implement the method without any creative effort.
  • the schematic diagram of the relationship between the sensor sensing angle and the actual slope angle, the visual sensor can obtain the relative angle between the road ahead and the vehicle body plane.
  • the specific image processing and calculation methods are already mature existing technologies.
  • the vehicle is subject to the influence of the visual sensor field of view FOV and the sensor blind area.
  • the area where the front slope can be sensed is limited, so the continuous slope value in front of the vehicle cannot be obtained.
  • the method disclosed in the embodiment of the present invention is divided into two steps. The establishment of the spatial slope surface and the kinematics-based method.
  • the vehicle front perceived slope signal and the wheel under-wheel estimated slope signal are used to construct the vehicle front and under-wheel spatial plane respectively, and then based on the slope signal
  • the covariance obtains the weighted curve coefficient, and then obtains a continuous weighted curve to achieve the fusion of the front and rear spatial planes in the slope-imperceptible area to obtain a continuous spatial surface.
  • the vehicle's gradient value at a certain time in the future is predicted.
  • the vehicle driving area includes the front of the vehicle and under the car body.
  • the road slope value does not depend on the road surface and needs to be converted according to the vehicle coordinate system. For example, on the same stretch of road, when the vehicle faces an uphill slope, the slope is Positive, the slope is negative when facing downhill.
  • ⁇ c is the angle between the vehicle body plane and the longitudinal slope of the road ahead, that is, the front longitudinal slope signal output by the visual sensor
  • ⁇ f is the angle between the front road surface and the front road surface.
  • the angle between the longitudinal direction of the vehicle and the horizontal plane is the actual slope angle of the road ahead
  • ⁇ s is the angle between the vehicle body and the ground under the vehicle
  • ⁇ r is the actual slope angle of the ground under the vehicle, which is calculated and output by the vehicle controller.
  • the ⁇ s angle is determined by the following formula:
  • 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.
  • ⁇ f ⁇ c + ⁇ s + ⁇ r
  • ⁇ s angle is determined by the following formula:
  • ⁇ f is the lateral slope angle of the front road surface
  • ⁇ c is the lateral angle between the vehicle body plane and the front road surface plane
  • ⁇ r is the lateral slope angle under the vehicle wheels
  • L wheelbase is the wheelbase on both sides of the vehicle.
  • the schematic diagram of calculating the size of each angle of the imperceptible area of slope is shown. If you want to obtain the slope information in the imperceptible area, you also need to determine the length of the imperceptible area.
  • the installation position of the vision sensor is fixed to the height of the vehicle body floor, and the sensor field of view position is also fixed relative to the vehicle body.
  • the length of the unperceivable area L sg under typical terrain can be determined by the following formula:
  • H g is the vertical distance between the sensor and the ground under the car
  • H b is the distance between the sensor and the ground of the car body, which is a fixed value
  • r w is the wheel radius
  • ⁇ sd is the vertical line between the lower edge of the camera's perspective and the vehicle floor The angle between is a fixed value
  • ⁇ sg is the angle between the lower edge of the camera's perspective and the ground plane under the car
  • L sd is the depth information perceived by the lower edge of the camera's perspective
  • L wc is the distance between the sensor and the front in the forward direction of the vehicle.
  • the distance between the wheel axes is approximately a fixed value
  • L sg is the length of the imperceptible area, which consists of two sections of road surface.
  • the above formula is a method for calculating the length of the imperceptible area when a vehicle is driving at the intersection of a typical two-slope road.
  • the actual road gradient changes a lot, but the error of this method is small under different working conditions and can be used for subsequent calculations.
  • the longitudinal slope angle of the slope plane closest to the vehicle in the front sensing area is ⁇ f0 and the lateral slope angle is ⁇ f0
  • the longitudinal slope angle of the plane under the vehicle wheels is ⁇ r
  • the lateral slope angle of the plane under the vehicle wheels is ⁇ r .
  • the longitudinal and lateral slope angles of the front and rear planes are used for weighted fusion along the length of the insensible area L sg
  • the slope signal covariance is used to determine the weighted curve coefficient.
  • O j (k) is the observed slope value at time k
  • X (k) is the actual slope value of the intermediate plane
  • n j (k) is the observation noise.
  • the estimated value of the midplane can be expressed as:
  • r 1 and r 2 are the weights of the front and rear plane slope angles respectively;
  • O * (k) is the weighted middle plane slope angle.
  • k-1)-O j (k)j 1,2
  • e j (k) is a component in the measurement error vector
  • k-1) is the slope value at time k predicted by the previous time.
  • T is the transpose symbol. Since the matrix written vertically is not convenient for viewing and typesetting, the transpose symbol is added to the matrix written horizontally to make it convenient for viewing and typesetting.
  • the transpose of a matrix is common knowledge in this field. Replace the rows (or columns) of matrix A with columns (or rows) of the same order to obtain a new matrix, which is called the transposed matrix of matrix A.
  • the optimal weight for merging the front and rear slope planes at time k is obtained, which represents the degree of acceptance of the front and rear slope planes. This value changes in real time with time. But in fact, the middle imperceptible area is not a plane, but a continuous surface, and it needs to be smoothed along the length of the imperceptible area L sg . Taking the head direction of the vehicle as the starting point of the coordinates, normalizing the area length and assigning weights with a quadratic function, the following formula can be obtained:
  • e is the weight of the front and rear planes that changes with distance
  • k r is the weight coefficient, which determines the curvature of the weighted curve
  • x is the distance in the front direction of the vehicle after normalization of L sg
  • s is the slope in the longitudinal direction of the vehicle The prediction point is based on the distance to the front axle of the vehicle.
  • the longitudinal and lateral slope angle of a point at a distance s in front of the front axle of the vehicle should be as follows:
  • ⁇ m is the longitudinal slope of the predicted point position
  • ⁇ m is the lateral slope of the predicted point position
  • the above method only considers the distribution of weights in the longitudinal direction. By changing the position of s, a continuous surface can be obtained in the imperceptible area.
  • the shape of the curved surface is dynamically adjusted. That is, if the slope noise perceived by the front sensor is lower, the curved surface is more reliable for the front plane; if the vehicle behind estimates that the plane noise is lower, the curved surface is more reliable for the rear plane.
  • the vehicle motion parameters required for slope prediction are schematically shown.
  • the slope can be predicted based on the dynamic state:
  • the calculation in this part needs to be based on the trajectory points of the vehicle in the future state estimated by the vehicle controller based on the dynamic parameter prediction information. If this information is not available, prediction can also be made based on the current state of the vehicle.
  • the current kinematic parameters of the vehicle and the vehicle dynamics model can be used to estimate the longitudinal mileage s k+n and the heading deflection angle ⁇ k+n in the future.
  • the specific method is as follows:
  • t 0 is the unit time of each interval from k to k+n.
  • the spatial gradient plane of the vehicle at time k+1 can be obtained:
  • the longitudinal and lateral slope vectors are established Establish the unit normal vector of the spatial slope plane at time k+1
  • the calculation block diagram of a single iteration cycle when continuously predicting the vehicle gradient situation in the future is used. And the slope information just calculated at k+1 time is input into the dynamic model to complete the longitudinal velocity at k+1 time Yaw rate of updates.
  • the above calculation step is an iterative calculation step. Repeating the above steps can achieve continuous prediction of the vehicle slope situation at k+2, k+3...k+n.
  • the current time is time k. If you want to predict the slope of the vehicle at time k+n, you need the vehicle position at time k+n in the current vehicle coordinate system. If the vehicle has the function of trajectory prediction, then This information is regarded as known, including the vehicle's longitudinal mileage s k + n and heading deflection angle ⁇ k+ n at time k+n in the current coordinate system of the vehicle.
  • the embodiment of the present invention realizes the continuous estimation of the spatial information of the slope in the unperceivable area ahead at any time, and then realizes the prediction of the vehicle position and heading angle based on the prediction calculation of the vehicle dynamics parameters, and realizes the prediction of the vehicle position and heading angle through iterative update calculation.
  • driving force/longitudinal force prediction models, front wheel angle prediction models, and vehicle dynamics models are existing technologies. Those skilled in the art can rely on the existing technologies they master to achieve driving force/longitudinal force prediction. Specific applications of the prediction model, front wheel angle prediction model, and vehicle dynamics model in this embodiment.
  • the present invention also discloses the corresponding electronic equipment and storage media:
  • An electronic device including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory complete communication with each other through the communication bus; a computer program is stored in the memory.
  • the processor When executed by the processor, the processor is caused to execute the steps of the vehicle driving road surface gradient prediction method.
  • a computer-readable storage medium stores a computer program that can be executed by an electronic device.
  • the computer program When the computer program is run on the electronic device, the electronic device executes the steps of a method for predicting the slope of a vehicle driving road surface.
  • the communication bus mentioned in the above-mentioned electronic equipment can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc.
  • PCI Peripheral Component Interconnect
  • EISA Extended Industry Standard Architecture
  • the communication bus can be divided into address bus, data bus, control bus, etc. For ease of presentation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.
  • Electronic devices include a hardware layer, an operating system layer running on the hardware layer, and an application layer running on the operating system.
  • This hardware layer includes hardware such as a central processing unit (CPU), a memory management unit (MMU), and memory. Damn it
  • the operating system can be any one or more computer operating systems that realize control of electronic devices through processes, such as Linux operating system, Unix operating system, Android operating system, iOS operating system or windows operating system, etc.
  • the electronic device may be a handheld device such as a smartphone or a tablet computer, or may be an electronic device such as a desktop computer or a portable computer, which is not particularly limited in the embodiment of the present invention.
  • the execution subject of electronic device control in the embodiment of the present invention may be an electronic device, or a functional module in the electronic device that can call a program and execute the program.
  • the electronic device can obtain the firmware corresponding to the storage medium.
  • the firmware corresponding to the storage medium is provided by the supplier.
  • the firmware corresponding to different storage media can be the same or different, and is not limited here.
  • After the electronic device obtains the firmware corresponding to the storage medium it can write the firmware corresponding to the storage medium into the storage medium, specifically, burn the firmware corresponding to the storage medium into the storage medium.
  • the process of burning the firmware into the storage medium can be implemented using existing technology, and will not be described again in the embodiment of the present invention.
  • the electronic device can also obtain the reset command corresponding to the storage medium.
  • the reset command corresponding to the storage medium is provided by the supplier.
  • the reset commands corresponding to different storage media can be the same or different, and are not limited here.
  • the storage medium of the electronic device is a storage medium in which the corresponding firmware is written.
  • the electronic device can respond to the reset command corresponding to the storage medium in the storage medium in which the corresponding firmware is written, so that the electronic device responds to the reset command corresponding to the storage medium.
  • Reset command to reset the storage medium in which the corresponding firmware is written.
  • the process of resetting the storage medium according to the reset command can be implemented with existing technology, and will not be described again in the embodiment of the present invention.
  • the invention also discloses a vehicle with a slope prediction function, which specifically includes:
  • a processor runs a program, and when the program is run, performs the steps of the vehicle driving road surface gradient prediction method on the data output from the electronic device;
  • a storage medium is used to store a program that, when running, executes the steps of the vehicle driving road surface gradient prediction method on data output from the electronic device.
  • the vehicle with the slope prediction function disclosed by the present invention can combine the forward-looking perceived slope and its own slope to realize the slope prediction of the imperceptible area; use covariance weighting to realize the fusion of front and rear slope information; use dynamic information to predict Position angle, use the position angle information to determine the slope value, use the slope prediction value to update the dynamics information, and achieve continuous prediction of the slope at any time in the future through multiple iterative calculation methods.
  • each block in the flowchart or block diagrams may represent a module, segment, or portion of code that contains one or more components for implementing the specified logical function(s). Executable instructions. 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.
  • each block of the block diagram and/or flowchart illustration, and combinations of blocks in the block diagram and/or flowchart illustration can be implemented by special purpose hardware-based systems that perform the specified functions or acts. , or can be implemented using a combination of specialized hardware and computer instructions.
  • each functional module in various embodiments of the present invention can be integrated together to form an independent part, each module can exist alone, or two or more modules can be integrated to form an independent part.
  • modules in the devices in the embodiment can be adaptively changed and arranged in one or more devices different from the embodiment.
  • the modules or units or components in 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 features disclosed in this specification including the corresponding claims, abstract and drawings) and any method so disclosed may be employed in any combination, except that at least some of such features and/or processes or units are mutually exclusive. All processes or units of the equipment are combined.
  • Each feature disclosed in this specification may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
  • Various component embodiments of the present invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof.
  • a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all functions of some or all components in a device for distributing messages according to embodiments of the present invention.
  • the invention may also be implemented as an apparatus or apparatus program (eg, computer program and computer program product) for performing part or all of the methods described herein.
  • Such a program implementing the present invention may be stored on a computer-readable medium, or may be in the form of one or more signals. Such signals may be downloaded from an Internet website, or provided on a carrier signal, or in any other form.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Mathematical Physics (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Vehicle Body Suspensions (AREA)

Abstract

本发明公开了一种车辆行驶路面坡度预测方法、系统及其车辆，方法步骤具体包括：建立空间坡度曲面，设定车辆行驶区域，获取车辆前方的地面坡度值和车下坡度值，计算不可感知区域的长度值，设定坡度可感知区域和坡度可估计区域为观测值；基于观测值对不可感知区域的长度值进行平滑处理和权重分配，计算不可感知区域的纵向坡度角和倾向坡度角；预测车辆轨迹，确定车辆在未来时刻的位置和航向角，融合坡度可感知区域和坡度可估计区域，预测车辆在未来某一时刻的坡度值，系统和车辆与方法相互对应。本发明能够预测车辆前方路面坡度，实现对车辆前方路面坡度的连续预测，为车辆动力学状态预测、智能驾驶等功能提供连续、准确的坡度信号。

Description

一种车辆行驶路面坡度预测方法、系统及其车辆 技术领域
本发明涉及一种路面坡度预测方法、系统及其车辆，尤其涉及一种车辆行驶路面坡度预测方法、系统及其车辆。
背景技术
现有技术针对车辆前方路面坡度预测的技术主要分为两个方面：
一种是使用GPS等定位手段，基于地图信息进行坡度预测，但目前定位技术的精度较低，且依赖于地图的精度，此种方法目前仅能在公路上以较大的区间进行坡度预测，无法用于精度要求较高的实时运算场景，而运用高精地图则带来成本的提高，目前应用较少，且环境适应性较差。
另一种坡度预测手段是基于车辆自身的视觉传感器，使用如双目相机、激光雷达等具有深度信息的设备，进行车辆前方路面相对坡度的感知，但存在的问题是传感器存在盲区，在车前较近的范围内无法实现感知，即仅能识别车前较远距离的坡度，且传感器视角有限，无法得到车辆左右侧较大范围内的坡度信息，即当车辆以较大转角转向时，无法得知车辆前进轨迹上的坡度信息。
发明内容
本发明的目的在于提供一种车辆行驶路面坡度预测方法、系统及其车辆，能够预测车辆前方路面坡度，实现对车辆前方路面坡度的连续预测，为车辆动力学状态预测、智能驾驶等功能提供连续、准确的坡度信号。
本发明所要解决的另一个技术问题是针对车载视觉传感器存在的盲区，实现在车前较近区域范围的感知，突破视觉传感器的视角限制。
本发明所要解决的又一个技术问题是结合车辆轮下坡度及感知到的前方坡度信息，实时构建车辆前方连续的空间曲面，并通过车辆动力学参数预测实现对车辆轨迹及未来坡度信息的预测。
本发明还可以解决的技术问题是针对不同车辆具有或不具有车辆轨迹预测功能的特点，能够基于动力学状态对车辆行驶路面坡度进行预测。
本发明提供了下述方案：
一种车辆行驶路面坡度预测方法，具体包括：
建立空间坡度曲面，设定车辆行驶区域为：坡度可感知区域、坡度不可感知区域和坡度可估计区域；
设定坡度可感知区域和坡度可估计区域的坡度角为观测值，获取车辆前方的地面坡度值和车下坡度值，用于计算当前时刻的坡度不可感知区域的实际坡度值；
基于所述观测值设定基于权重的约束条件，通过计算加权后综合测量误差，确定误差的最小值；
实时获取约束条件的最优权重值，根据最优权重值进行决策，得到坡度不可感知区域中未来时刻某一点的纵向坡度角和侧向坡度角；
预测车辆轨迹，结合坡度不可感知区域中未来时刻某一点的纵向坡度角和侧向坡度角，建立纵向坡度向量和侧向坡度向量，建立未来时刻不可感知区域的坡度平面的单位法向量；
使纵向坡度向量和侧向坡度向量围绕单位法向量进行旋转，进行矩阵计算，求得车辆在未来时刻某一点的坡度值。
进一步的，所述坡度可感知区域具体为：基于视觉传感器视场角获得的车辆前方坡度区域；
所述坡度可估计区域为车辆轮下的坡度区域；
所述坡度不可感知区域位于坡度可感知区域和坡度可估计区域之间。
进一步的，所述获取车辆前方的地面坡度值和车下坡度值，具体包括：前方路面纵向坡度夹角αc、实际前方路面坡度角αf、车身相对地面夹角αs和车下地面实际坡度角αr，其中：
所述前方路面纵向坡度夹角具体为车身平面与前方路面纵向坡度的夹角；
所述实际前方路面坡度角具体为前方路面平面在车辆纵向方向上与水平面的夹角；
所述车身相对地面夹角为车身相对于车下地面的夹角；
所述车下地面实际坡度角为车下地面与水平面的实际坡度角；
地面坡度值和车下坡度值满足如下公式：
实际前方路面坡度角＝前方路面纵向坡度夹角+车身相对地面夹角+车下地面实际坡度角；
车身相对地面夹角αs满足公式：
其中：Hfl为左前悬架高度、Hfr为右前悬架高度、Hrl为左后悬架高度、Hrr为右后悬架高度、Laxis为车辆轴距；
还包括：前方路面平面的侧向坡度角βf，侧向坡度角βf满足公式：
βf＝βc+βs+βr
其中βs角度由下式确定：
其中：βf为前方路面平面的侧向坡度角；βc车身平面与前方路面平面侧向的夹角；βr为车辆轮下侧向坡度角；Lwheelbase为车辆两侧轮距。
进一步的，不可感知区域的长度Lsg满足如下公式：


在上述公式中：Hg为传感器与车下地面间的垂直距离；Hb为传感器与车身地面间的距离，为一固定值；rw为车轮半径；αsd为摄像头视角下边线与车辆底板垂线间的夹角，为一固定值；αsg为摄像头视角下边线与车下地面平面间的夹角；Lsd为摄像头视角下边线感知的深度信息；Lwc为传感器在车辆前进方向上与前轮轴线的距离，近似为一固定值；Lsg为不可感知区域长度，由两段路面长度组成。
进一步的，对于不可感知区域的坡度值，分别使用前后平面纵侧向坡度角沿不可感知区域长度Lsg进行加权整合，使用坡度信号协方差确定加权曲线系数；
将前方平面坡度角、后方平面坡度角视为对中间不可感知区坡度角的两个观测值，假设中间不可感知区域为一中间平面，求得中间平面的估计值表达式：
r1、r2分别为前后平面坡度角的权重，O*(k)为加权后的中间平面坡度；计算前后坡度测量值在k时刻的测量误差：
ej(k)＝X(k|k-1)-Oj(k)j＝1,2
其中，ej(k)为测量误差向量中的一个分量，X(k|k-1)为由上一时刻预测的k时刻的坡度值；
进行加权后的综合测量误差为：
e*(k)＝[r1(k)ej(k),r2(k)e2(k)]T
其中T为转置符号；
利用最小二乘法选取最优的加权权重，可以得到误差平方和：
e*T(k)e*(k)＝(r1(k)ej(k))2+(r2(k)e2(k))2
使用r1+r2＝1作为约束条件，对上式求取最小值，使用拉格朗日极值法可得：
得到k时刻将前后坡度平面融合的最优权重，代表对前后平面的采信程度，这个值是随时间实时改变的。
进一步的，沿不可感知区域长度Lsg进行平滑处理，以车头方向为坐标起始点，将区域长度进行归一化处理并以二次函数分配权重，可得下式：
在上式中：e为随距离改变的前后平面加权权重；kr为权重系数，决定加权曲线的曲率；x为Lsg归一化后在车头方向上的距离；s为在车辆纵向方向上，坡度预测点据车辆前轴的距离。
综上，在车辆纵向方向上，车辆前轴前方距离为s的某一点的纵侧向坡度角为：
其中：αm为预测点位置的纵向坡度；βm为预测点位置的侧向坡度。
进一步的，在预测车辆轨迹时，若车辆不具备轨迹预测功能，则使用车辆当前运动学参数及车辆动力学模型，对未来时刻的纵向行驶里程和航向偏转角进行估计；
使用驱动力/纵向力预测模型以加速踏板位置、制动踏板位置作为输入信号，预测车辆在未来时刻的驱动力/制动力；
通过车辆方向盘转角的预测模块，得到未来时刻的前轮转角时间序列；
根据当前时刻车辆的纵向速度和横摆角速度，预测未来时刻车辆在当前坐标系下纵向的行程里程和航向偏转角；
求得车辆在未来时刻的空间坡度平面，结合未来时刻纵侧向坡度角及车辆位置，建立未来时刻的纵向坡度向量、横向坡度向量和空间坡度平面的单位法向量；
使纵向坡度向量、横向坡度向量进行旋转，得到旋转矩阵，得到未来时刻的纵向坡度角和横向坡度角。
一种车辆行驶路面坡度预测系统，具体包括：
空间坡度曲面建立模块，用于建立空间坡度曲面，设定车辆行驶区域为：坡度可感知区域、坡度不可感知区域和坡度可估计区域；
坡度角观测值设定模块，用于设定坡度可感知区域和坡度可估计区域的坡度角为观测值，获取车辆前方的地面坡度值和车下坡度值，用于计算当前时刻的坡度不可感知区域的实际坡度值；
加权综合测量误差计算模块，用于基于所述观测值设定基于权重的约束条件，通过计算加权后综合测量误差，确定误差的最小值；
最优权重值决策模块，用于实时获取约束条件的最优权重值，根据最优权重值进行决策，得到坡度不可感知区域中未来时刻某一点的纵向坡度角和侧向坡度角；
坡度向量及坡度平面单位法向量计算模块，预测车辆轨迹，结合坡度不可感知区域中未来时刻某一点的纵向坡度角和侧向坡度角，建立纵向坡度向量和侧向坡度向量，建立未来时刻不可感知区域的坡度平面的单位法向量；
向量旋转及矩阵计算模块，使纵向坡度向量和侧向坡度向量围绕单位法向量进行旋转，进行矩阵计算，求得车辆在未来时刻某一点的坡度值。
一种电子设备，包括：处理器、通信接口、存储器和通信总线，其中，处理器，通信接口，存储器通过通信总线完成相互间的通信；所述存储器中存储有计算机程序，当所述计算机程序被所述处理器执行时，使得所述处理器所述方法的步骤。
一种计算机可读存储介质，其存储有可由电子设备执行的计算机程序，当所述计算机程序在所述电子设备上运行时，使得所述电子设备执行所述方法的步骤。
一种车辆，具体包括：
电子设备，用于实现权利要求1至7中任一项所述的方法；
处理器，所述处理器运行程序，当所述程序运行时，对于从所述电子设备输出的数据执行权利要求1至7中任一项所述方法的步骤；
存储介质，用于存储程序，所述程序在运行时，对于从电子设备输出的数据执行所述方法的步骤。
本发明与现有技术相比具有以下的优点：
本发明能够预测车辆前方路面坡度，实现对车辆前方路面坡度的连续预测，为车辆动力学状态预测、智能驾驶等功能提供连续、准确的坡度信号。本发明进行坡度估计、预测的意义不仅仅在于对路面地形本身的感知，而是 在于对当下或未来时刻车辆轮下坡度的估计、预测，从而为精确辨识车辆未来动力学状态提供基础。
本发明针对车载视觉传感器存在的盲区，建立空间坡度曲面，设定车辆行驶区域为：坡度可感知区域、坡度不可感知区域和坡度可估计区域，通过计算不可感知区域的长度值和坡度角，结合预测车辆轨迹，融合坡度可感知区域和坡度可估计区域，最终预测车辆在未来某一时刻的坡度值，实现在车前较近区域范围的感知，突破视觉传感器的视角限制。
本发明在获取空间曲面后，需要对车辆在未来时刻的位置、航向角进行预测，预测需要基于车辆控制器根据动力学参数预测信息估算的车辆在未来状态的轨迹点，对于不具备轨迹预测功能的，也可以根据车辆当前状态进行预测，并达到了对应的技术效果，拓展了本发明在不同类型车辆中的适应性。
附图说明
为了更清楚地说明本发明具体实施方式或现有技术中的技术方案，下面将对具体实施方式或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图是本发明的一些实施方式，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本发明车辆行驶路面坡度预测方法的流程图。
图2是本发明车辆行驶路面坡度预测系统的架构图。
图3是车辆坡度可感知区域的结构示意图。
图4是传感器感知角度与实际坡度角的关系示意图。
图5是计算坡度不可感知区域各角度尺寸示意图。
图6是坡度预测所需的车辆运动参数示意图。
图7是对未来时刻车辆坡度情况进行持续预测时单个迭代周期的计算框图。
图8是电子设备的系统架构图。
具体实施方式
下面将结合附图对本发明的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
如图1所示的车辆行驶路面坡度预测方法的流程图，具体包括：
步骤S1，建立空间坡度曲面，设定车辆行驶区域为：坡度可感知区域、坡度不可感知区域和坡度可估计区域；
具体的，坡度可感知区域具体为：基于视觉传感器视场角获得的车辆前方坡度区域；
坡度可估计区域为车辆轮下的坡度区域；
坡度不可感知区域位于坡度可感知区域和坡度可估计区域之间。
具体的，获取车辆前方的地面坡度值和车下坡度值，具体包括：前方路面纵向坡度夹角αc、实际前方路面坡度角αf、车身相对地面夹角αs和车下地面实际坡度角αr，其中：
前方路面纵向坡度夹角具体为车身平面与前方路面纵向坡度的夹角；
实际前方路面坡度角具体为前方路面平面在车辆纵向方向上与水平面的夹角；
车身相对地面夹角为车身相对于车下地面的夹角；
车下地面实际坡度角为车下地面与水平面的实际坡度角；
地面坡度值和车下坡度值满足如下公式：
实际前方路面坡度角＝前方路面纵向坡度夹角+车身相对地面夹角+车下地面实际坡度角；
车身相对地面夹角αs满足公式：
其中：Hfl为左前悬架高度、Hfr为右前悬架高度、Hrl为左后悬架高度、Hrr为右后悬架高度、Laxis为车辆轴距；
还包括：前方路面平面的侧向坡度角βf，侧向坡度角βf满足公式：
βf＝βc+βs+βr
其中βs角度由下式确定：
其中：βf为前方路面平面的侧向坡度角；βc车身平面与前方路面平面侧向的夹角；βr为车辆轮下侧向坡度角；Lwheelbase为车辆两侧轮距。
具体的，不可感知区域的长度Lsg满足如下公式：


在上述公式中：Hg为传感器与车下地面间的垂直距离；Hb为传感器与车身地面间的距离，为一固定值；rw为车轮半径；αsd为摄像头视角下边线与 车辆底板垂线间的夹角，为一固定值；αsg为摄像头视角下边线与车下地面平面间的夹角；Lsd为摄像头视角下边线感知的深度信息；Lwc为传感器在车辆前进方向上与前轮轴线的距离，近似为一固定值；Lsg为不可感知区域长度，由两段路面长度组成。
有必要指出的是：上文提及的Lwc为传感器在车辆前进方向上与前轮轴线的距离，近似为一固定值，并非是不清楚的描述，本领域技术人员能够根据其掌握的本领域普通技术知识并结合工程实践、技术手册、教科书，将Lwc确定在一个确定的范围内，因为Lwc的定义很明确，Lwc是传感器在车辆前进方向上与前轮轴线的距离，虽然由于车辆型号的不同或实际路况的不同Lwc会有一定的差异，但这种差异是工程实践中常见的、可预期的并可计算的，所述近似为一固定值并非含糊的描述，而是指该距离可以用一个区间范围进行描述。
步骤S2，设定坡度可感知区域和坡度可估计区域的坡度角为观测值，获取车辆前方的地面坡度值和车下坡度值，用于计算当前时刻的坡度不可感知区域的实际坡度值；
具体的，对于不可感知区域的坡度值，分别使用前后平面纵侧向坡度角沿不可感知区域长度Lsg进行加权整合，使用坡度信号协方差确定加权曲线系数；
将前方平面坡度角、后方平面坡度角视为对中间不可感知区坡度角的两个观测值，假设中间不可感知区域为一中间平面，求得中间平面的估计值表达式：
r1、r2分别为前后平面坡度角的权重，O*(k)为加权后的中间平面坡度；计算前后坡度测量值在k时刻的测量误差：
ej(k)＝X(k|k-1)-Oj(k)j＝1,2
其中，ej(k)为测量误差向量中的一个分量，X(k|k-1)为由上一时刻预测的k时刻的坡度值；
进行加权后的综合测量误差为：
e*(k)＝[r1(k)ej(k),r2(k)e2(k)]T
其中T为转置符号；
利用最小二乘法选取最优的加权权重，可以得到误差平方和：
e*T(k)e*(k)＝(r1(k)ej(k))2+(r2(k)e2(k))2
使用r1+r2＝1作为约束条件，对上式求取最小值，使用拉格朗日极值法可得：
得到k时刻将前后坡度平面融合的最优权重，代表对前后平面的采信程度，这个值是随时间实时改变的。
具体的，沿不可感知区域长度Lsg进行平滑处理，以车头方向为坐标起始点，将区域长度进行归一化处理并以二次函数分配权重，可得下式：
在上式中：e为随距离改变的前后平面加权权重；kr为权重系数，决定加权曲线的曲率；x为Lsg归一化后在车头方向上的距离；s为在车辆纵向方向上，坡度预测点据车辆前轴的距离。
综上，在车辆纵向方向上，车辆前轴前方距离为s的某一点的纵侧向坡度角为：
其中：αm为预测点位置的纵向坡度；βm为预测点位置的侧向坡度。
步骤S3，基于观测值设定基于权重的约束条件，通过计算加权后综合测量误差，确定误差的最小值；
步骤S4，实时获取约束条件的最优权重值，根据最优权重值进行决策，得到坡度不可感知区域中未来时刻某一点的纵向坡度角和侧向坡度角；
步骤S5，预测车辆轨迹，结合坡度不可感知区域中未来时刻某一点的纵向坡度角和侧向坡度角，建立纵向坡度向量和侧向坡度向量，建立未来时刻不可感知区域的坡度平面的单位法向量；
具体的，在预测车辆轨迹时，若车辆不具备轨迹预测功能，则使用车辆当前运动学参数及车辆动力学模型，对未来时刻的纵向行驶里程和航向偏转角进行估计；
使用驱动力/纵向力预测模型以加速踏板位置、制动踏板位置作为输入信号，预测车辆在未来时刻的驱动力/制动力；
通过车辆方向盘转角的预测模块，得到未来时刻的前轮转角时间序列；
根据当前时刻车辆的纵向速度和横摆角速度，预测未来时刻车辆在当前坐标系下纵向的行程里程和航向偏转角；
求得车辆在未来时刻的空间坡度平面，结合未来时刻纵侧向坡度角及车辆位置，建立未来时刻的纵向坡度向量、横向坡度向量和空间坡度平面的单位法向量；
使纵向坡度向量、横向坡度向量进行旋转，得到旋转矩阵，得到未来时刻的纵向坡度角和横向坡度角。
步骤S6，使纵向坡度向量和侧向坡度向量围绕单位法向量进行旋转，进行矩阵计算，求得车辆在未来时刻某一点的坡度值。
对于上述实施例公开的方法步骤，出于简单描述的目的将方法步骤表述为一系列的动作组合，但是本领域技术人员应该知悉，本发明实施例并不受所描述的动作顺序的限制，因为依据本发明实施例，某些步骤可以采用其他顺序或者同时进行。其次，本领域技术人员也应该知悉，说明书中所描述的实施例均属于优选实施例，所涉及的动作并不一定是本发明实施例所必须的。
具体的，在预测车辆轨迹时，若车辆不具备轨迹预测功能，则使用车辆当前运动学参数及车辆动力学模型，对未来时刻的纵向行驶里程和航向偏转角进行估计；
使用驱动力/纵向力预测模型以加速踏板位置、制动踏板位置作为输入信号，预测车辆在未来时刻的驱动力/制动力；
通过车辆方向盘转角的预测模块，得到未来时刻的前轮转角时间序列；
根据当前时刻车辆的纵向速度和横摆角速度，预测未来时刻车辆在当前坐标系下纵向的行程里程和航向偏转角；
求得车辆在未来时刻的空间坡度平面，结合未来时刻纵侧向坡度角及车辆位置，建立未来时刻的纵向坡度向量、横向坡度向量和空间坡度平面的单位法向量；
使纵向坡度向量、横向坡度向量进行旋转，得到旋转矩阵，得到未来时刻的纵向坡度角和横向坡度角。
对于上述实施例公开的方法步骤，出于简单描述的目的将方法步骤表述为一系列的动作组合，但是本领域技术人员应该知悉，本发明实施例并不受所描述的动作顺序的限制，因为依据本发明实施例，某些步骤可以采用其他顺序或者同时进行。其次，本领域技术人员也应该知悉，说明书中所描述的实施例均属于优选实施例，所涉及的动作并不一定是本发明实施例所必须的。
如图2所示的车辆行驶路面坡度预测系统，具体包括：
空间坡度曲面建立模块，用于建立空间坡度曲面，设定车辆行驶区域为：坡度可感知区域、坡度不可感知区域和坡度可估计区域；
坡度角观测值设定模块，用于设定坡度可感知区域和坡度可估计区域的坡度角为观测值，获取车辆前方的地面坡度值和车下坡度值，用于计算当前时刻的坡度不可感知区域的实际坡度值；
加权综合测量误差计算模块，用于基于所述观测值设定基于权重的约束条件，通过计算加权后综合测量误差，确定误差的最小值；
最优权重值决策模块，用于实时获取约束条件的最优权重值，根据最优权重值进行决策，得到坡度不可感知区域中未来时刻某一点的纵向坡度角和侧向坡度角；
坡度向量及坡度平面单位法向量计算模块，预测车辆轨迹，结合坡度不可感知区域中未来时刻某一点的纵向坡度角和侧向坡度角，建立纵向坡度向量和侧向坡度向量，建立未来时刻不可感知区域的坡度平面的单位法向量；
向量旋转及矩阵计算模块，使纵向坡度向量和侧向坡度向量围绕单位法向量进行旋转，进行矩阵计算，求得车辆在未来时刻某一点的坡度值。
值得注意的是，虽然在本实施例中只披露了车辆行驶路面坡度预测系统的基本功能模块，但并不意味着本系统的组成仅仅局限于上述基本功能模块，相反，本实施例所要表达的意思是：在上述基本功能模块的基础之上本领域技术人员可以结合现有技术任意添加一个或多个功能模块，形成无穷多个实施例或技术方案，也就是说本系统是开放式而非封闭式的，不能因为本实施例仅仅披露了个别基本功能模块，就认为本发明权利要求的保护范围局限于所公开的基本功能模块。同时，为了描述的方便，描述以上装置时以功能分为各种单元、模块分别描述。当然在实施本发明时可以把各单元、模块的功能在同一个或多个软件和/或硬件中实现。
以上所描述的装置实施方式仅仅是示意性的，其中所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施方式方案的目的。本领域普通技术人员在不付出创造性劳动的情况下，即可以理解并实施。
如图3所示的传感器感知角度与实际坡度角之间的关系示意图，视觉传感器能够获得前方路面与车身平面的相对角，具体图像处理、计算方式已经属于成熟的现有技术。车辆受制于视觉传感器视场角FOV及传感器盲区等影响，前方坡度可感知区域受限，故无法得到车辆前方连续的坡度值。本发明实施例公开的方法分为两个步骤，空间坡度曲面的建立和基于运动学的首先使用车辆前方感知坡度信号和轮下估计坡度信号分别构建车辆前方及轮下空间平面，后基于坡度信号协方差获取加权曲线系数，进而获得连续的加权曲线以实现在坡度不可感知区域内对前后空间平面进行融合，得到连续的空间曲面。然后基于计算出的空间坡度曲面及预计行驶轨迹，预测车辆在未来某一时刻的坡度值。
根据驾驶常识和生活常识、交通常识可知：车辆行驶区域包括车辆前方、车身下方，路面坡度值不取决于路面，需要根据车辆坐标系进行转化，例如同样一段路，车辆面对上坡时坡度为正，面对下坡时坡度为负。
如图4所示的传感器感知角度与实际坡度角的关系图，图中αc为车身平面与前方路面纵向坡度的夹角，即视觉传感器输出的前方纵向坡度信号；αf为前方路面平面在车辆纵向方向上与水平面的夹角，即前方路面的实际坡度角；αs为车身相对于车下地面的夹角；αr为车下地面的实际坡度角，由车辆控制器计算输出，可得出将视觉传感器数值αc转化为实际前方路面坡度角的公式为：
αf＝αc+αs+αr
其中αs角度由下式确定：
其中，Hfl为左前悬架高度、Hfr为右前悬架高度、Hrl为左后悬架高度、Hrr为右后悬架高度、Laxis为车辆轴距。
同样的，也可求得前方路面平面的侧向坡度角βf：
βf＝βc+βs+βr
其中：βs角度由下式确定：
其中：βf为前方路面平面的侧向坡度角；βc车身平面与前方路面平面侧向的夹角；βr为车辆轮下侧向坡度角；Lwheelbase为车辆两侧轮距。
如图5所示的计算坡度不可感知区域各角度的尺寸示意图，若想获得不可感知区域内的坡度信息，还需确定不可感知区域的长度。视觉传感器安装位置与车身底板的高度是固定的，传感器视场角位置相对于车身也是固定的，在典型地形下不可感知区域长度Lsg可由下列式确定：


式中：Hg为传感器与车下地面间的垂直距离；Hb为传感器与车身地面间的距离，为一固定值；rw为车轮半径；αsd为摄像头视角下边线与车辆底板垂线间的夹角，为一固定值；αsg为摄像头视角下边线与车下地面平面间的夹角；Lsd为摄像头视角下边线感知的深度信息；Lwc为传感器在车辆前进方向上与前轮轴线的距离，近似为一固定值；Lsg为不可感知区域长度，由两段路面长度组成。
上式为车辆行驶在典型两段坡路交接处的不可感知区域长度计算方法，实际路面坡度变化情况较多，但此方法在不同工况下误差都较小，可用于后续计算。
经上述计算后，可知前方感知区域内距离车辆最近的坡度平面纵向坡度角为αf0、侧向坡度角为βf0，车辆轮下平面纵向坡度角为αr、车辆轮下平面侧向坡度角为βr。对于中间坡度不可感知区域的坡度值，分别使用前后平面纵侧向坡度角沿不可感知区域长度Lsg进行加权融合，使用坡度信号协方差确定加权曲线系数。可以将前方平面坡度角、后方平面坡度角视为对中间不可感知区坡度角的两个观测值，假设中间不可感知区为一平面，则有：
Oj(k)＝X(k)+nj(k)j＝1,2
其中：Oj(k)为k时刻下的坡度观测值；X(k)中间平面实际坡度值；nj(k)观测噪声。
由于前后观测值相互独立，则中间平面的估计值可表示为：
其中：r1、r2分别为前后平面坡度角的权重；O*(k)为加权后的中间平面坡度角。
前后坡度测量值在k时刻的测量误差为：
ej(k)＝X(k|k-1)-Oj(k)j＝1,2
其中，ej(k)为测量误差向量中的一个分量，X(k|k-1)为由上一时刻预测的k时刻的坡度值。进行加权后的综合测量误差为：
e*(k)＝[r1(k)ej(k),r2(k)e2(k)]T
其中T为转置符号，由于竖着写的矩阵不方便查看和排版，故在横着写的矩阵上加上转置符号，使其方便查看与排版。矩阵的转置是本领域的公知常识，将矩阵A的行(或列)换成同序数的列(或行)得到一个新的矩阵，叫做矩阵A的转置矩阵。
利用最小二乘法选取最优的加权权重，可以得到误差平方和：
e*T(k)e*(k)＝(r1(k)ej(k))2+(r2(k)e2(k))2
使用r1+r2＝1作为约束条件，对上式求取最小值，使用拉格朗日极值法可得：
得到k时刻将前后坡度平面融合的最优权重，代表对前后平面的采信程度，这个值是随时间实时改变的。但实际上中间的不可感知区并不是一个平面，而是连续曲面，还需沿不可感知区域长度Lsg进行平滑处理。以车头方向为坐标起始点，将区域长度进行归一化处理并以二次函数分配权重，可得下式：
式中：e为随距离改变的前后平面加权权重；kr为权重系数，决定加权曲线的曲率；x为Lsg归一化后在车头方向上的距离；s为在车辆纵向方向上，坡度预测点据车辆前轴的距离。
综上，在车辆纵向方向上，车辆前轴前方距离为s的某一点的纵侧向坡度角应为下式：
其中：αm为预测点位置的纵向坡度；βm为预测点位置的侧向坡度。
由于车辆侧向速度较小、传感器侧向感知范围较小的因素，以上方法只考虑了权重在纵向方向上的分布情况。通过改变s的位置，可以在不可感知区内得到连续的曲面。通过对比前后平面的观测噪声，动态的调节曲面形状，即若前方传感器感知坡度噪声较低则曲面对前平面更加采信；若后面车辆估计平面噪声较低则曲面对后方平面更为采信。
如图6所示的坡度预测所需的车辆运动参数示意图，在本实施例中可基于动力学状态对坡度进行预测：
获取空间曲面后，需要对车辆在未来时刻的位置、航向角进行预测。本部分计算需要基于车辆控制器根据动力学参数预测信息估算的车辆在未来状态的轨迹点，若不具备此信息，也可以根据车辆当前状态进行预测。
若车辆不具备轨迹预测功能，则可使用车辆当前运动学参数及车辆动力学模型对未来时刻的纵向行驶里程sk+n、航向偏转角θk+n进行估计，具体方法如下：
由于驾驶员操作频率较低，假设k时刻至k+n时刻驾驶员给出的操作指令不变，即加速踏板位置、制动踏板位置即方向盘转角大小数值恒定。在k时刻时，使用驱动力/纵向力预测模型以加速踏板位置、制动踏板位置作为输入信号，预测车辆在[k、k+1、k+2……k+n]时刻的驱动力/制动力的时间序列同理，使用车辆方向盘转角输入给前轮转角预测模型，得到车辆在k至k+n时刻的前轮转角时间序列 在k时刻车辆使用当前时刻纵向速度当前横摆角速度 预测k+1时刻车辆在当前坐标系下纵向的行驶里程及航向偏转角θk+1，如下式：
其中：t0为k至k+n时刻每段间隔的单位时间。
使用结合前文公式可求得车辆在k+1时刻时的空间坡度平面：
结合k+1时刻纵侧向坡度角及车辆位置，建立纵侧向坡度向量 建立k+1时刻空间坡度平面的单位法向量
使绕单位法向量以θk+1角度进行旋转：

最后基于可顺利求出车辆在k+1时刻的纵向坡度角侧向坡度角
如图7所示的对未来时刻车辆坡度情况进行持续预测时单个迭代周期的计算框图，使用来自于预测模型的及刚计算完的k+1时刻坡度信息输入动力学模型，完成对k+1时刻纵向速度横摆角速度的更新。以上计算步骤为一个迭代计算步骤，重复以上步骤可实现对k+2、k+3……k+n时刻车辆坡度情况的持续预测。
假设当前时刻为k时刻，若想预测k+n时刻时车辆的坡度情况，则需要在当前车辆坐标系下k+n时刻的车辆位置，若车辆具备轨迹预测的功能，则 此信息视为已知的，有车辆当前坐标系下，k+n时刻的纵向行驶里程sk+n、航向偏转角θk+n。
本发明实施例实现了在任意时刻对前方不可感知区域内坡度空间上信息的连续估计，而后基于对车辆动力学参数的预测计算，实现对车辆位置及航向角的预测，通过迭代更新计算实现对未来时刻车辆坡度情况的预测，驱动力/纵向力预测模型、前轮转角预测模型、车辆动力学模型为现有技术，本领域技术人员能够凭借其掌握的现有技术实现对驱动力/纵向力预测模型、前轮转角预测模型、车辆动力学模型在本实施例中的具体应用。
如图8所示，本发明在公开了车辆行驶路面坡度预测方法、系统的基础之上，还公开了与之对应的电子设备和存储介质：
一种电子设备，包括：处理器、通信接口、存储器和通信总线，其中，处理器，通信接口，存储器通过通信总线完成相互间的通信；所述存储器中存储有计算机程序，当所述计算机程序被所述处理器执行时，使得所述处理器执行车辆行驶路面坡度预测方法的步骤。
一种计算机可读存储介质，其存储有可由电子设备执行的计算机程序，当所述计算机程序在所述电子设备上运行时，使得所述电子设备执行车辆行驶路面坡度预测方法的步骤。
上述电子设备提到的通信总线可以是外设部件互连标准(Peripheral Component Interconnect，PCI)总线或扩展工业标准结构(Extended Industry Standard Architecture，EISA)总线等。该通信总线可以分为地址总线、数据总线、控制总线等。为便于表示，图中仅用一条粗线表示，但并不表示仅有一根总线或一种类型的总线。
电子设备包括硬件层，运行在硬件层之上的操作系统层，以及运行在操作系统上的应用层。该硬件层包括中央处理器(CPU，Central Processing Unit)、内存管理单元(MMU，Memory Management Unit)和内存等硬件。该操 作系统可以是任意一种或多种通过进程(Process)实现电子设备控制的计算机操作系统，例如，Linux操作系统、Unix操作系统、Android操作系统、iOS操作系统或windows操作系统等。并且在本发明实施例中该电子设备可以是智能手机、平板电脑等手持设备，也可以是桌面计算机、便携式计算机等电子设备，本发明实施例中并未特别限定。
本发明实施例中的电子设备控制的执行主体可以是电子设备，或者是电子设备中能够调用程序并执行程序的功能模块。电子设备可以获取到存储介质对应的固件，存储介质对应的固件由供应商提供，不同存储介质对应的固件可以相同可以不同，在此不做限定。电子设备获取到存储介质对应的固件后，可以将该存储介质对应的固件写入存储介质中，具体地是往该存储介质中烧入该存储介质对应固件。将固件烧入存储介质的过程可以采用现有技术实现，在本发明实施例中不做赘述。
电子设备还可以获取到存储介质对应的重置命令，存储介质对应的重置命令由供应商提供，不同存储介质对应的重置命令可以相同可以不同，在此不做限定。
此时电子设备的存储介质为写入了对应的固件的存储介质，电子设备可以在写入了对应的固件的存储介质中响应该存储介质对应的重置命令，从而电子设备根据存储介质对应的重置命令，对该写入对应的固件的存储介质进行重置。根据重置命令对存储介质进行重置的过程可以现有技术实现，在本发明实施例中不做赘述。
本发明还公开了一种具有坡度预测功能的车辆，具体包括：
电子设备，用于实现车辆行驶路面坡度预测方法；
处理器，所述处理器运行程序，当所述程序运行时，对于从所述电子设备输出的数据执行车辆行驶路面坡度预测方法的步骤；
存储介质，用于存储程序，所述程序在运行时，对于从电子设备输出的数据执行车辆行驶路面坡度预测方法的步骤。
本发明公开的具有坡度预测功能的车辆，能够结合前视感知坡度及自身坡度，实现了对不可感知区域的坡度预测；使用协方差加权的方式实现对前后坡度信息的融合；运用动力学信息预测位置转角，运用位置转角信息确定坡度值，利用坡度预测值更新动力学信息，通过多次迭代计算方法实现对未来任意时刻的坡度连续预测。
最后应说明的是：以上各实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述各实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分或者全部技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的范围。
本技术领域技术人员可以理解，除非另外定义，这里使用的所有术语(包括技术术语和科学术语)，具有与本发明所属领域中的普通技术人员的一般理解相同的意义。还应该理解的是，诸如通用字典中定义的那些术语，应该被理解为具有与现有技术的上下文中的意义一致的意义，并且除非被特定定义，否则不会用理想化或过于正式的含义来解释。
需要说明的是，本说明书与权利要求中使用了某些词汇来指称特定元件。本领域技术人员应可以理解，车辆制造商可能会用不同名词来称呼同一个元件。本说明书与权利要求并不以名词的差异来作为区分元件的方式，而是以元件在功能上的差异作为区分的准则。如通篇说明书及权利要求当中所提及的“包含”或“包括”为一开放式用语，故其应被理解成“包括但不限定于”。后续将对实施本发明的较佳实施方式进行描述说明，但是所述说明是以说明书的一般原则为目的，并非用于限定本发明的范围。本发明的保护范围当根据其所附的权利要求所界定者为准。
在本说明书的描述中，参考术语“一个实施例”、“示例”、“具体示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本发明的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不一定指的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任何的一个或多个实施例或示例中以合适的方式结合。
另外，本发明各个实施例之间的技术方案可以相互结合，但是必须是以本领域普通技术人员能够实现为基础，当技术方案的结合出现相互矛盾或无法实现时应当认为这种技术方案的结合不存在，也不在本发明要求的保护范围之内。
在本发明所提供的几个实施例中，应该理解到，所揭示的装置和方法，也可以通过其它的方式实现。以上所描述的装置实施例仅仅是示意性的，例如，附图中的流程图和框图显示了根据本发明的多个实施例的装置、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段或代码的一部分，所述模块、程序段或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现方式中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个连续的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，由所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或动作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
另外，在本发明各个实施例中的各功能模块可以集成在一起形成一个独立的部分，也可以是各个模块单独存在，也可以两个或两个以上模块集成形成一个独立的部分。
本领域技术人员可以理解，可以对实施例中的设备中的模块进行自适应性地改变并且把它们设置在与该实施例不同的一个或多个设备中。可以把实施例中的模块或单元或组件组合成一个模块或单元或组件，以及此外可以把它们分成多个子模块或子单元或子组件。除了这样的特征和/或过程或者单元中的至少一些是相互排斥之外，可以采用任何组合对本说明书(包括相应的权利要求、摘要和附图)中公开的所有特征以及如此公开的任何方法或者设备的所有过程或单元进行组合。除非另外明确陈述，本说明书(包括相应的权利要求、摘要和附图)中公开的每个特征可以由提供相同、等同或相似目的的替代特征来代替。
此外，本领域的技术人员能够理解，尽管在此所述的一些实施例包括其它实施例中所包括的某些特征而不是其它特征，但是不同实施例的特征的组合意味着处于本发明的范围之内并且形成不同的实施例。例如，在下面的权利要求书中，所要求保护的实施例的任意之一都可以以任意的组合方式来使用。
本发明的各个部件实施例可以以硬件实现，或者以在一个或者多个处理器上运行的软件模块实现，或者以它们的组合实现。本领域的技术人员应当理解，可以在实践中使用微处理器或者数字信号处理器(DSP)来实现根据本发明实施例的分发消息的设备中的一些或者全部部件的一些或者全部功能。本发明还可以实现为用于执行这里所描述的方法的一部分或者全部的设备或者装置程序(例如，计算机程序和计算机程序产品)。这样的实现本发明的程序可以存储在计算机可读介质上，或者可以具有一个或者多个信号的形式。这样的信号可以从因特网网站上下载得到，或者在载体信号上提供，或者以任何其他形式提供。

Claims (11)

  1. 一种车辆行驶路面坡度预测方法，其特征在于，具体包括：
    建立空间坡度曲面，设定车辆行驶区域为：坡度可感知区域、坡度不可感知区域和坡度可估计区域；
    设定坡度可感知区域和坡度可估计区域的坡度角为观测值，获取车辆前方的地面坡度值和车下坡度值，用于计算当前时刻的坡度不可感知区域的实际坡度值；
    基于所述观测值设定基于权重的约束条件，通过计算加权后综合测量误差，确定误差的最小值；
    实时获取约束条件的最优权重值，根据最优权重值进行决策，得到坡度不可感知区域中未来时刻某一点的纵向坡度角和侧向坡度角；
    预测车辆轨迹，结合坡度不可感知区域中未来时刻某一点的纵向坡度角和侧向坡度角，建立纵向坡度向量和侧向坡度向量，建立未来时刻不可感知区域的坡度平面的单位法向量；
    使纵向坡度向量和侧向坡度向量围绕单位法向量进行旋转，进行矩阵计算，求得车辆在未来时刻某一点的坡度值。
  2. 根据权利要求1所述的车辆行驶路面坡度预测方法，其特征在于，所述坡度可感知区域具体为：基于视觉传感器视场角获得的车辆前方坡度区域；
    所述坡度可估计区域为车辆轮下的坡度区域；
    所述坡度不可感知区域位于坡度可感知区域和坡度可估计区域之间。
  3. 根据权利要求1所述的车辆行驶路面坡度预测方法，其特征在于，
    所述获取车辆前方的地面坡度值和车下坡度值，具体包括：前方路面纵向坡度夹角αc、实际前方路面坡度角αf、车身相对地面夹角αs和车下地面实际坡度角αr，其中：
    所述前方路面纵向坡度夹角具体为车身平面与前方路面纵向坡度的夹角；
    所述实际前方路面坡度角具体为前方路面平面在车辆纵向方向上与水平面的夹角；
    所述车身相对地面夹角为车身相对于车下地面的夹角；
    所述车下地面实际坡度角为车下地面与水平面的实际坡度角；
    地面坡度值和车下坡度值满足如下公式：
    实际前方路面坡度角＝前方路面纵向坡度夹角+车身相对地面夹角+车下地面实际坡度角；
    车身相对地面夹角αs满足公式：
    其中：Hfl为左前悬架高度、Hfr为右前悬架高度、Hrl为左后悬架高度、Hrr为右后悬架高度、Laxis为车辆轴距；
    还包括：前方路面平面的侧向坡度角βf，侧向坡度角βf满足公式：
    βf＝βc+βs+βr
    其中βs角度由下式确定：
    其中：βf为前方路面平面的侧向坡度角；βc车身平面与前方路面平面侧向的夹角；βr为车辆轮下侧向坡度角；Lwheelbase为车辆两侧轮距。
  4. 根据权利要求1所述的车辆行驶路面坡度预测方法，其特征在于，
    不可感知区域的长度Lsg满足如下公式：


    在上述公式中：Hg为传感器与车下地面间的垂直距离；Hb为传感器与车身地面间的距离，为一固定值；rw为车轮半径；αsd为摄像头视角下边线与车辆底板垂线间的夹角，为一固定值；αsg为摄像头视角下边线与车下地面平面间的夹角；Lsd为摄像头视角下边线感知的深度信息；Lwc为传感器在车辆前进方向上与前轮轴线的距离，近似为一固定值；Lsg为不可感知区域长度，由两段路面长度组成。
  5. 根据权利要求1所述的车辆行驶路面坡度预测方法，其特征在于，
    对于不可感知区域的坡度值，分别使用前后平面纵侧向坡度角沿不可感知区域长度Lsg进行加权整合，使用坡度信号协方差确定加权曲线系数；
    将前方平面坡度角、后方平面坡度角视为对中间不可感知区坡度角的两个观测值，假设中间不可感知区域为一中间平面，求得中间平面的估计值表达式：
    r1、r2分别为前后平面坡度角的权重，O*(k)为加权后的中间平面坡度；计算前后坡度测量值在k时刻的测量误差：
    ej(k)＝X(k|k-1)-Oj(k)j＝1,2
    其中，ej(k)为测量误差向量中的一个分量，X(k|k-1)为由上一时刻预测的k时刻的坡度值；
    进行加权后的综合测量误差为：
    e*(k)＝[r1(k)ej(k),r2(k)e2(k)]T
    其中T为转置符号；
    利用最小二乘法选取最优的加权权重，可以得到误差平方和：
    e*T(k)e*(k)＝(r1(k)ej(k))2+(r2(k)e2(k))2
    使用r1+r2＝1作为约束条件，对上式求取最小值，使用拉格朗日极值法可得：
    得到k时刻将前后坡度平面融合的最优权重，代表对前后平面的采信程度，这个值是随时间实时改变的。
  6. 根据权利要求5所述的车辆行驶路面坡度预测方法，其特征在于，
    沿不可感知区域长度Lsg进行平滑处理，以车头方向为坐标起始点，将区域长度进行归一化处理并以二次函数分配权重，可得下式：
    在上式中：e为随距离改变的前后平面加权权重；kr为权重系数，决定加权曲线的曲率；x为Lsg归一化后在车头方向上的距离；s为在车辆纵向方向上，坡度预测点据车辆前轴的距离；
    综上，在车辆纵向方向上，车辆前轴前方距离为s的某一点的纵侧向坡度角为：
    其中：αm为预测点位置的纵向坡度；βm为预测点位置的侧向坡度。
  7. 根据权利要求1所述的车辆行驶路面坡度预测方法，其特征在于，在预测车辆轨迹时，若车辆不具备轨迹预测功能，则使用车辆当前运动学参数及车辆动力学模型，对未来时刻的纵向行驶里程和航向偏转角进行估计；
    使用驱动力/纵向力预测模型以加速踏板位置、制动踏板位置作为输入信号，预测车辆在未来时刻的驱动力/制动力；
    通过车辆方向盘转角的预测模块，得到未来时刻的前轮转角时间序列；
    根据当前时刻车辆的纵向速度和横摆角速度，预测未来时刻车辆在当前坐标系下纵向的行程里程和航向偏转角；
    求得车辆在未来时刻的空间坡度平面，结合未来时刻纵侧向坡度角及车辆位置，建立未来时刻的纵向坡度向量、横向坡度向量和空间坡度平面的单位法向量；
    使纵向坡度向量、横向坡度向量进行旋转，得到旋转矩阵，得到未来时刻的纵向坡度角和横向坡度角。
  8. 一种车辆行驶路面坡度预测系统，其特征在于，具体包括：
    空间坡度曲面建立模块，用于建立空间坡度曲面，设定车辆行驶区域为：坡度可感知区域、坡度不可感知区域和坡度可估计区域；
    坡度角观测值设定模块，用于设定坡度可感知区域和坡度可估计区域的坡度角为观测值，获取车辆前方的地面坡度值和车下坡度值，用于计算当前时刻的坡度不可感知区域的实际坡度值；
    加权综合测量误差计算模块，用于基于所述观测值设定基于权重的约束条件，通过计算加权后综合测量误差，确定误差的最小值；
    最优权重值决策模块，用于实时获取约束条件的最优权重值，根据最优权重值进行决策，得到坡度不可感知区域中未来时刻某一点的纵向坡度角和侧向坡度角；
    坡度向量及坡度平面单位法向量计算模块，预测车辆轨迹，结合坡度不可感知区域中未来时刻某一点的纵向坡度角和侧向坡度角，建立纵向坡度向量和侧向坡度向量，建立未来时刻不可感知区域的坡度平面的单位法向量；
    向量旋转及矩阵计算模块，使纵向坡度向量和侧向坡度向量围绕单位法向量进行旋转，进行矩阵计算，求得车辆在未来时刻某一点的坡度值。
  9. 一种电子设备，其特征在于，包括：处理器、通信接口、存储器和通信总线，其中，处理器，通信接口，存储器通过通信总线完成相互间的通信；所述存储器中存储有计算机程序，当所述计算机程序被所述处理器执行时，使得所述处理器执行权利要求1至7中任一项所述方法的步骤。
  10. 一种计算机可读存储介质，其特征在于，其存储有可由电子设备执行的计算机程序，当所述计算机程序在所述电子设备上运行时，使得所述电子设备执行权利要求1至7中任一项所述方法的步骤。
  11. 一种车辆，其特征在于，具体包括：
    电子设备，用于实现权利要求1至7中任一项所述的方法；
    处理器，所述处理器运行程序，当所述程序运行时，对于从所述电子设备输出的数据执行权利要求1至7中任一项所述方法的步骤；
    存储介质，用于存储程序，所述程序在运行时，对于从电子设备输出的数据执行权利要求1至7中任一项所述方法的步骤。
PCT/CN2023/092417 2022-09-08 2023-05-06 一种车辆行驶路面坡度预测方法、系统及其车辆 WO2024051188A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202211094710.6 2022-09-08
CN202211094710.6A CN115416669A (zh) 2022-09-08 2022-09-08 一种车辆行驶路面坡度预测方法、系统及其车辆

Publications (1)

Publication Number Publication Date
WO2024051188A1 true WO2024051188A1 (zh) 2024-03-14

Family

ID=84203107

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2023/092417 WO2024051188A1 (zh) 2022-09-08 2023-05-06 一种车辆行驶路面坡度预测方法、系统及其车辆

Country Status (2)

Country Link
CN (1) CN115416669A (zh)
WO (1) WO2024051188A1 (zh)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115416669A (zh) * 2022-09-08 2022-12-02 中国第一汽车股份有限公司 一种车辆行驶路面坡度预测方法、系统及其车辆

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102700551A (zh) * 2012-05-31 2012-10-03 清华大学 一种车辆行驶过程中路面坡度的实时估计方法
JP2014019256A (ja) * 2012-07-17 2014-02-03 Mitsubishi Fuso Truck & Bus Corp トレーラ車両の路面勾配推定装置
CN108297872A (zh) * 2018-03-08 2018-07-20 中国第汽车股份有限公司 全工况车载路面坡度估算装置和方法
KR20190072733A (ko) * 2017-12-18 2019-06-26 전자부품연구원 스테레오 카메라 기반 도로 구배 예측방법 및 시스템
CN114620052A (zh) * 2022-04-12 2022-06-14 中国第一汽车股份有限公司 一种车辆信号处理方法、装置和驾驶设备
CN115416669A (zh) * 2022-09-08 2022-12-02 中国第一汽车股份有限公司 一种车辆行驶路面坡度预测方法、系统及其车辆

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102700551A (zh) * 2012-05-31 2012-10-03 清华大学 一种车辆行驶过程中路面坡度的实时估计方法
JP2014019256A (ja) * 2012-07-17 2014-02-03 Mitsubishi Fuso Truck & Bus Corp トレーラ車両の路面勾配推定装置
KR20190072733A (ko) * 2017-12-18 2019-06-26 전자부품연구원 스테레오 카메라 기반 도로 구배 예측방법 및 시스템
CN108297872A (zh) * 2018-03-08 2018-07-20 中国第汽车股份有限公司 全工况车载路面坡度估算装置和方法
CN114620052A (zh) * 2022-04-12 2022-06-14 中国第一汽车股份有限公司 一种车辆信号处理方法、装置和驾驶设备
CN115416669A (zh) * 2022-09-08 2022-12-02 中国第一汽车股份有限公司 一种车辆行驶路面坡度预测方法、系统及其车辆

Also Published As

Publication number Publication date
CN115416669A (zh) 2022-12-02

Similar Documents

Publication Publication Date Title
US11023751B2 (en) Systems and methods for safe route determination
JP6644742B2 (ja) 頑健で効率的な車両測位用のアルゴリズム及びインフラ
EP3714290B1 (en) Lidar localization using 3d cnn network for solution inference in autonomous driving vehicles
JP7256758B2 (ja) 自動運転車両においてｒｎｎとｌｓｔｍを用いて時間平滑化を行うｌｉｄａｒ測位
US20210365712A1 (en) Deep learning-based feature extraction for lidar localization of autonomous driving vehicles
US9077958B2 (en) Road departure warning system
EP3779922A1 (en) Method for estimating driving road and driving road estimation system
WO2020112649A1 (en) Probabilistic risk assessment for trajectory evaluation
WO2019178548A1 (en) Determining drivable free-space for autonomous vehicles
JP2017535873A (ja) 路上シーン認識のための連続オクルージョンモデル
EP4242998A1 (en) Traffic stream information determination method and apparatus, electronic device and storage medium
JP2022022287A (ja) 地図作成装置、制御方法、プログラム及び記憶媒体
WO2022247203A1 (zh) 自动驾驶车辆的控制方法、装置、设备以及存储介质
WO2024051188A1 (zh) 一种车辆行驶路面坡度预测方法、系统及其车辆
CN117440908A (zh) 用于自动驾驶系统中基于图神经网络的行人动作预测的方法和系统
CN113195322A (zh) 用于确定预定区域内的运动轨迹的方法和装置
CN115705780A (zh) 关联被感知和映射的车道边缘以进行定位
WO2023114037A1 (en) Lidar point cloud alignment validator in hd mapping
CN111967094A (zh) 一种基于Mobileye提供车道线方程的后向车道线推算方法
US20230168100A1 (en) Automatic annotation of drivable road segments
CN113160550B (zh) 涉及交叉路口的自动驾驶场景的传感器覆盖范围分析
CN114817765A (zh) 基于地图的目标航向消歧
US20230195830A1 (en) Calibration metrics for measuring trajectory prediction
US20240010213A1 (en) Autonomous vehicle chassis frame estimation by minimizing distortion from cad model
Backman Motion prediction of ego vehicle in complex scenarios

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 23861893

Country of ref document: EP

Kind code of ref document: A1