A method and system for predicting the slope of a road surface on which a vehicle is traveling, and a vehicle thereof

Abstract

The present invention discloses a method, system, and vehicle for predicting the slope of a road surface on which a vehicle is traveling. The method specifically includes the following steps: establishing a spatial slope surface, setting a vehicle driving area, obtaining the ground slope value in front of the vehicle and the slope value under the vehicle, calculating the length value of the imperceptible area, setting the slope perceptible area and the slope estimable area as observation values; smoothing and weighting the length value of the imperceptible area based on the observation value, and calculating the longitudinal slope angle and the inclination slope angle of the imperceptible area; predicting the vehicle trajectory, determining the vehicle's position and heading angle at a future moment, integrating the slope perceptible area and the slope estimable area, and predicting the vehicle's slope value at a certain moment in the future. The system, vehicle, and method correspond to each other. The present invention can predict the slope of the road surface in front of the vehicle, achieve continuous prediction of the slope of the road surface in front of the vehicle, and provide a continuous and accurate slope signal for vehicle dynamic state prediction, intelligent driving, and other functions.

Description

Technical Field

[0001] The present invention relates to a road surface gradient prediction method, system and vehicle thereof, and in particular to a road surface gradient prediction method, system and vehicle thereof. Background Art

[0002] The existing technology for predicting the slope of the road ahead of the vehicle is mainly divided into two aspects:

[0003] One method is to use positioning methods such as GPS to predict slopes based on map information. However, the current positioning technology has low accuracy and relies on map accuracy. This method can only predict slopes in larger intervals on roads and cannot be used in real-time computing scenarios with high accuracy requirements. The use of high-precision maps increases costs, is currently less used, and has poor environmental adaptability.

[0004] Another method of slope prediction is based on the vehicle's own visual sensors, using devices with depth information such as binocular cameras and lidar to perceive the relative slope of the road in front of the vehicle. However, the problem is that the sensor has blind spots and cannot perceive within a relatively close range in front of the vehicle. That is, it can only identify the slope at a relatively long distance in front of the vehicle. In addition, the sensor's field of view is limited and cannot obtain slope information within a large range on the left and right sides of the vehicle. That is, when the vehicle turns at a large angle, it is impossible to know the slope information on the vehicle's forward trajectory. Summary of the Invention

[0005] The purpose of the present invention is to provide a method, system and vehicle for predicting the slope of the road surface on which a vehicle is traveling, 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 and accurate slope signals for vehicle dynamic state prediction, intelligent driving and other functions.

[0006] Another technical problem to be solved by the present invention is to achieve perception in the area closer to the front of the vehicle in response to the blind spots of the vehicle-mounted visual sensors, thus breaking through the viewing angle limitation of the visual sensors.

[0007] Another technical problem to be solved by the present invention is to combine the slope under the vehicle wheels and the perceived slope information ahead to construct a continuous spatial surface in front of the vehicle in real time, and to predict the vehicle trajectory and future slope information through vehicle dynamics parameter prediction.

[0008] Another technical problem that can be solved by the present invention is that the slope of the road on which the vehicle is traveling can be predicted based on the dynamic state according to the characteristics of different vehicles having or not having the vehicle trajectory prediction function.

[0009] The present invention provides the following solutions:

[0010] A method for predicting the slope of a road surface on which a vehicle is traveling, specifically comprising:

[0011] Establish a spatial slope surface and set the vehicle driving area as: slope-perceivable area, slope-imperceivable area and slope-estimated area;

[0012] The slope angles of the slope-perceivable area and the slope-estimated area are set as observation values, and the ground slope value in front of the vehicle and the slope value under the vehicle are obtained to calculate the actual slope value of the slope-imperceivable area at the current moment.

[0013] Setting weighted constraints based on the observed values, and determining a minimum error by calculating the weighted comprehensive measurement error;

[0014] Obtain the optimal weight value of the constraint condition in real time, make a decision based on the optimal weight value, and obtain the longitudinal slope angle and lateral slope angle of a certain point in the future time in the slope imperceptible area;

[0015] Predict the vehicle trajectory, combine the longitudinal slope angle and lateral slope angle at a certain point in the slope imperceptible area at a future time, establish the longitudinal slope vector and lateral slope vector, and establish the unit normal vector of the slope plane in the future imperceptible area;

[0016] The longitudinal slope vector and the lateral slope vector are rotated around the unit normal vector, and matrix calculation is performed to obtain the slope value of the vehicle at a certain point in the future.

[0017] Furthermore, the slope perceptible area is specifically: a slope area in front of the vehicle obtained based on the field of view angle of the visual sensor;

[0018] The slope estimable area is a slope area under the vehicle wheels;

[0019] The slope imperceptible area is located between the slope perceptible area and the slope estimable area.

[0020] Furthermore, 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 α of the road ahead c , actual front road slope angle α f 、Angle α between the vehicle body and the ground s and the actual slope angle α of the ground under the vehicle r ,in:

[0021] The longitudinal slope angle of the road ahead is specifically the angle between the vehicle body plane and the longitudinal slope of the road ahead;

[0022] The actual front road slope angle is specifically the angle between the front road plane and the horizontal plane in the longitudinal direction of the vehicle;

[0023] The angle between the vehicle body and the ground is the angle between the vehicle body and the ground below the vehicle;

[0024] 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;

[0025] The ground slope value and the vehicle slope value satisfy the following formula:

[0026] Actual front road slope angle = front road longitudinal slope angle + vehicle body relative to ground angle + actual ground slope angle under the vehicle;

[0027] Angle α between the vehicle body and the ground s Satisfies the formula:

[0028]

[0029] Among them: H fl is the left front suspension height, H fr is the right front suspension height, H rl is the height of the left rear suspension, H rr is the right rear suspension height, L axis is the vehicle wheelbase;

[0030] Also includes: the lateral slope angle β of the front road surface f , lateral slope angle β f Satisfies the formula:

[0031] β f =β c +β s +β r

[0032] where β s The angle is determined by:

[0033]

[0034] Where: f is the lateral slope angle of the road surface ahead; β c The lateral angle between the vehicle body plane and the front road surface plane; β r is the lateral slope angle under the vehicle wheel; L wheelbase The wheelbase on both sides of the vehicle.

[0035] Furthermore, the length L of the imperceptible area sg Satisfies the following formula:

[0036]

[0037]

[0038]

[0039] In the above formula: H gH is the vertical distance between the sensor and the ground under the vehicle; b is the distance between the sensor and the vehicle body, which is a fixed value; r w is the wheel radius; α sd is the angle between the lower edge of the camera's viewing angle and the vertical line of the vehicle's floor, which is a fixed value; sg L is the angle between the edge of the camera's view and the ground plane under the vehicle; sd is the depth information of the edge perception under the camera’s perspective; L wc L is the distance between the sensor and the front wheel axis in the vehicle's forward direction, which is approximately a fixed value; sg The length of the imperceptible area consists of two sections of road surface.

[0040] Furthermore, for the slope value of the imperceptible area, the longitudinal and lateral slope angles of the front and rear planes along the length L of the imperceptible area are used respectively. sg Perform weighted integration and use the slope signal covariance to determine the weighted curve coefficients;

[0041] The slope angle of the front plane and the slope angle of the rear plane are regarded as two observation values ​​of the slope angle of the middle imperceptible area. Assuming that the middle imperceptible area is an intermediate plane, the estimated value expression of the intermediate plane is obtained:

[0042]

[0043] r1 and r2 are the weights of the slope angles of the front and rear planes, respectively. * (k) is the weighted mid-plane slope; calculate the measurement error of the front and back slope measurements at time k:

[0044] e j (k) = X(k|k-1)-O j (k)j=1,2

[0045] Among them, e j (k) is a component of the measurement error vector, X(k|k-1) is the slope value at time k predicted from the previous time;

[0046] The weighted comprehensive measurement error is:

[0047] e * (k)=[r1(k)e j (k), r2(k)e2(k)] T

[0048] Where T is the transposition symbol;

[0049] By using the least squares method to select the optimal weighted weight, the sum of squared errors can be obtained:

[0050] e*T (k)e * (k)=(r1(k)e j (k)) 2 +(r2(k)e2(k)) 2

[0051] Using r1+r2=1 as the constraint condition, we can find the minimum value of the above formula and use the Lagrange extreme value method to obtain:

[0052]

[0053] The optimal weight for fusing the front and rear slope planes at time k is obtained, which represents the degree of confidence in the front and rear planes. This value changes in real time with time.

[0054] Furthermore, along the length L of the imperceptible area sg Perform smoothing, take the vehicle head direction as the starting point, normalize the area length and assign weights using a quadratic function, and we get the following formula:

[0055]

[0056] In the above formula: e is the weight of the front and back planes that changes with distance; k r is the weight coefficient, which determines the curvature of the weighted curve; x is L sg The normalized distance in the direction of the vehicle's front; s is the distance between the slope prediction point and the vehicle's front axle in the longitudinal direction of the vehicle.

[0057] In summary, in the longitudinal direction of the vehicle, the longitudinal lateral slope angle of a point at a distance s in front of the front axle of the vehicle is:

[0058]

[0059] Where: α m is the longitudinal slope at the prediction point; β m is the lateral slope at the prediction point.

[0060] Furthermore, when predicting the vehicle trajectory, if the vehicle does not have a trajectory prediction function, the vehicle's current kinematic parameters and vehicle dynamics model are used to estimate the longitudinal mileage and heading deflection angle at the future moment;

[0061] Use the driving force / longitudinal force prediction model to predict the vehicle's driving force / braking force at a future time using the accelerator pedal position and brake pedal position as input signals;

[0062] Through the vehicle steering wheel angle prediction module, the time series of the front wheel angle at future moments is obtained;

[0063] Based on the current longitudinal velocity and yaw rate of the vehicle, the longitudinal distance traveled and the heading angle of the vehicle in the current coordinate system are predicted in the future.

[0064] Obtain the spatial slope plane of the vehicle at the future time, and establish the longitudinal slope vector, transverse slope vector, and unit normal vector of the spatial slope plane at the future time by combining the longitudinal and lateral slope angles and the vehicle position at the future time;

[0065] The longitudinal slope vector and the transverse slope vector are rotated to obtain a rotation matrix, and the longitudinal slope angle and the transverse slope angle at the future moment are obtained.

[0066] A vehicle road slope prediction system, specifically comprising:

[0067] A spatial slope surface establishment module is used to establish a spatial slope surface and set the vehicle driving area as: a slope perceptible area, an imperceptible slope area, and an estimable slope area;

[0068] The slope angle observation value setting module is used to set the slope angles of the slope-perceivable area and the slope-estimated area as observation values, obtain the ground slope value in front of the vehicle and the slope value under the vehicle, and use them to calculate the actual slope value of the slope-imperceivable area at the current moment;

[0069] A weighted comprehensive measurement error calculation module is used to set weighted constraint conditions based on the observation values, and determine the minimum value of the error by calculating the weighted comprehensive measurement error;

[0070] The optimal weight value decision 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 of a certain point in the slope imperceptible area at a future time;

[0071] The slope vector and slope plane unit normal vector calculation module predicts the vehicle trajectory and establishes the longitudinal slope vector and lateral slope vector based on the longitudinal slope angle and lateral slope angle at a certain point in the future in the slope imperceptible area. It also establishes the unit normal vector of the slope plane in the future imperceptible area.

[0072] The vector rotation and matrix calculation module rotates the longitudinal slope vector and the lateral slope vector around the unit normal vector, performs matrix calculation, and obtains the slope value of the vehicle at a certain point in the future.

[0073] An electronic device comprises: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method.

[0074] A computer-readable storage medium stores a computer program executable by an electronic device. When the computer program runs on the electronic device, the electronic device executes the steps of the method.

[0075] A vehicle, comprising:

[0076] An electronic device for implementing the method according to any one of claims 1 to 7;

[0077] a processor, wherein the processor runs a program, and when the program runs, the processor performs the steps of the method according to any one of claims 1 to 7 on the data output from the electronic device;

[0078] The storage medium is used to store a program, and when the program is run, the program executes the steps of the method for data output from the electronic device.

[0079] Compared with the prior art, the present invention has the following advantages:

[0080] This invention can predict the slope of the road ahead of the vehicle, achieving continuous prediction of the road slope ahead of the vehicle, providing a continuous and accurate slope signal for vehicle dynamics prediction, intelligent driving, and other functions. The significance of this invention's slope estimation and prediction lies not only in the perception of the road topography itself, but also in estimating and predicting the slope under the vehicle's wheels at the current or future moment, thus providing a basis for accurately identifying the vehicle's future dynamic state.

[0081] The present invention targets blind spots in vehicle-mounted visual sensors and establishes a spatial slope surface. The vehicle driving area is divided into: a slope-perceptible area, an imperceptible area, and an estimable area. By calculating the length and slope angle of the imperceptible area, combining this with the predicted vehicle trajectory, the slope-perceptible area and the estimable area are integrated to ultimately predict the vehicle's slope value at a certain moment in the future. This allows for perception in an area close to the vehicle, breaking through the visual field limitations of the visual sensor.

[0082] After obtaining the spatial surface, the present invention needs to predict the position and heading angle of the vehicle at the future moment. The prediction needs to be based on the trajectory points of the vehicle in the future state estimated by the vehicle controller according to the dynamic parameter prediction information. For vehicles that do not have trajectory prediction function, prediction can also be made based on the current state, and the corresponding technical effect is achieved, which expands the adaptability of the present invention to different types of vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0084] Figure 1 The present invention is a flow chart of the method for predicting the slope of a vehicle driving road.

[0085] Figure 2 It is an architecture diagram of the vehicle driving road slope prediction system of the present invention.

[0086] Figure 3 It is a structural diagram of the vehicle slope perception area.

[0087] Figure 4 This is a schematic diagram of the relationship between the sensor perception angle and the actual slope angle.

[0088] Figure 5 It is a schematic diagram for calculating the dimensions of each angle in the area where the slope is imperceptible.

[0089] Figure 6 Schematic diagram of vehicle motion parameters required for slope prediction.

[0090] Figure 7 This is a calculation block diagram of a single iteration cycle when continuously predicting the vehicle slope condition at future moments.

[0091] Figure 8 It is a system architecture diagram of an electronic device. DETAILED DESCRIPTION

[0092] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0093] like Figure 1 The flowchart of the method for predicting the slope of a vehicle road surface shown in FIG. 1 specifically includes:

[0094] Step S1, establishing a spatial slope surface, setting the vehicle driving area as: a slope perceptible area, an imperceptible slope area, and a slope estimable area;

[0095] Specifically, the slope perceptible area is: the slope area in front of the vehicle obtained based on the field of view of the visual sensor;

[0096] The slope estimable area is the slope area under the vehicle wheels;

[0097] The slope imperceptible region is located between the slope perceptible region and the slope estimable region.

[0098] Specifically, the ground slope value in front of the vehicle and the slope value under the vehicle are obtained, including: the longitudinal slope angle α of the road ahead c , actual front road slope angle α f 、Angle α between the vehicle body and the ground s and the actual slope angle α of the ground under the vehicle r ,in:

[0099] The longitudinal slope angle of the road ahead is specifically the angle between the vehicle body plane and the longitudinal slope of the road ahead;

[0100] The actual front road slope angle is specifically the angle between the front road plane and the horizontal plane in the longitudinal direction of the vehicle;

[0101] The vehicle body relative to the ground angle is the angle between the vehicle body and the ground below the vehicle;

[0102] 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;

[0103] The ground slope value and the vehicle slope value satisfy the following formula:

[0104] Actual front road slope angle = front road longitudinal slope angle + vehicle body relative to ground angle + actual ground slope angle under the vehicle;

[0105] Angle α between the vehicle body and the ground s Satisfies the formula:

[0106]

[0107] Among them: H fl is the left front suspension height, H fr is the right front suspension height, H rl is the height of the left rear suspension, H rr is the right rear suspension height, L axis is the vehicle wheelbase;

[0108] Also includes: the lateral slope angle β of the front road surface f , lateral slope angle β f Satisfies the formula:

[0109] β f =β c +β s +β r

[0110] where β s The angle is determined by:

[0111]

[0112] Where: f is the lateral slope angle of the road surface ahead; β c The lateral angle between the vehicle body plane and the front road surface plane; β r is the lateral slope angle under the vehicle wheel; L wheelbase The wheelbase on both sides of the vehicle.

[0113] Specifically, the length of the imperceptible area L sg Satisfies the following formula:

[0114]

[0115]

[0116]

[0117] In the above formula: H g H is the vertical distance between the sensor and the ground under the vehicle; b is the distance between the sensor and the vehicle body, which is a fixed value; r w is the wheel radius; α sd is the angle between the lower edge of the camera's viewing angle and the vertical line of the vehicle's floor, which is a fixed value; sg L is the angle between the edge of the camera's view and the ground plane under the vehicle; sd is the depth information of the edge perception under the camera’s perspective; L wc L is the distance between the sensor and the front wheel axis in the vehicle's forward direction, which is approximately a fixed value; sg The length of the imperceptible area consists of two sections of road surface.

[0118] It is necessary to point out that: the L mentioned above wc L is the distance between the sensor and the front wheel axis in the vehicle's forward direction, which is approximately a fixed value. It is not an unclear description. Those skilled in the art can calculate L based on their common technical knowledge in this field and in combination with engineering practice, technical manuals, and textbooks. wc Determined within a certain range, because L wc The definition of L is very clear. wc It is the distance between the sensor and the front wheel axis in the direction of vehicle forward movement. Although it may vary due to different vehicle models or actual road conditions, L wc There will be certain differences, but such differences are common, predictable and calculable in engineering practice. The approximation to a fixed value is not an ambiguous description, but means that the distance can be described by an interval range.

[0119] Step S2: setting the slope angles of the slope-perceivable area and the slope-estimated area as observation values, obtaining the ground slope value in front of the vehicle and the slope value under the vehicle, and using these values ​​to calculate the actual slope value of the slope-imperceivable area at the current moment;

[0120] Specifically, for the slope value of the imperceptible area, the longitudinal and lateral slope angles of the front and rear planes along the length L of the imperceptible area are used respectively. sg Perform weighted integration and use the slope signal covariance to determine the weighted curve coefficients;

[0121] The slope angle of the front plane and the slope angle of the rear plane are regarded as two observation values ​​of the slope angle of the middle imperceptible area. Assuming that the middle imperceptible area is an intermediate plane, the estimated value expression of the intermediate plane is obtained:

[0122]

[0123] r1 and r2 are the weights of the slope angles of the front and rear planes, respectively. * (k) is the weighted mid-plane slope; calculate the measurement error of the front and back slope measurements at time k:

[0124] e j (k) = X(k|k-1)-O j (k)j=1,2

[0125] Among them, e j (k) is a component of the measurement error vector, X(k|k-1) is the slope value at time k predicted from the previous time;

[0126] The weighted comprehensive measurement error is:

[0127] e * (k)=[r1(k)e j (k), r2(k)e2(k)] T

[0128] Where T is the transposition symbol;

[0129] By using the least squares method to select the optimal weighted weight, the sum of squared errors can be obtained:

[0130] e *T (k)e * (k)=(r1(k)e j (k)) 2 +(r2(k)e2(k)) 2

[0131] Using r1+r2=1 as the constraint condition, we can find the minimum value of the above formula and use the Lagrange extreme value method to obtain:

[0132]

[0133] The optimal weight for fusing the front and rear slope planes at time k is obtained, which represents the degree of confidence in the front and rear planes. This value changes in real time with time.

[0134] Specifically, along the length L of the imperceptible area sg Perform smoothing, take the vehicle head direction as the starting point, normalize the area length and assign weights using a quadratic function, and we get the following formula:

[0135]

[0136] In the above formula: e is the weight of the front and back planes that changes with distance; k r is the weight coefficient, which determines the curvature of the weighted curve; x is L sg The normalized distance in the direction of the vehicle's front; s is the distance between the slope prediction point and the vehicle's front axle in the longitudinal direction of the vehicle.

[0137] In summary, in the longitudinal direction of the vehicle, the longitudinal lateral slope angle of a point at a distance s in front of the front axle of the vehicle is:

[0138]

[0139] Where: α m is the longitudinal slope of the prediction point; β m is the lateral slope at the prediction point.

[0140] Step S3, setting weighted constraints based on the observed values, and determining the minimum value of the error by calculating the weighted comprehensive measurement error;

[0141] Step S4: obtaining the optimal weight values ​​of the constraint conditions in real time, making a decision based on the optimal weight values, and obtaining the longitudinal slope angle and lateral slope angle of a certain point in the slope imperceptible area at a future time;

[0142] Step S5: predicting the vehicle trajectory, combining the longitudinal slope angle and the lateral slope angle of a certain point in the slope imperceptible area at a future time, establishing a longitudinal slope vector and a lateral slope vector, and establishing a unit normal vector of the slope plane of the slope imperceptible area at a future time;

[0143] Specifically, when predicting the vehicle trajectory, if the vehicle does not have a trajectory prediction function, the vehicle's current kinematic parameters and vehicle dynamics model are used to estimate the longitudinal mileage and heading deflection angle at the future moment;

[0144] Use the driving force / longitudinal force prediction model to predict the vehicle's driving force / braking force at a future time using the accelerator pedal position and brake pedal position as input signals;

[0145] Through the vehicle steering wheel angle prediction module, the time series of the front wheel angle at future moments is obtained;

[0146] Based on the current longitudinal velocity and yaw rate of the vehicle, the longitudinal distance traveled and the heading angle of the vehicle in the current coordinate system are predicted in the future.

[0147] Obtain the spatial slope plane of the vehicle at the future time, and establish the longitudinal slope vector, transverse slope vector, and unit normal vector of the spatial slope plane at the future time by combining the longitudinal and lateral slope angles and the vehicle position at the future time;

[0148] The longitudinal slope vector and the transverse slope vector are rotated to obtain a rotation matrix, and the longitudinal slope angle and the transverse slope angle at the future moment are obtained.

[0149] 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.

[0150] For the method steps disclosed in the above embodiments, for the purpose of simple description, the method steps are expressed as a series of action combinations. However, those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0151] Specifically, when predicting the vehicle trajectory, if the vehicle does not have a trajectory prediction function, the vehicle's current kinematic parameters and vehicle dynamics model are used to estimate the longitudinal mileage and heading deflection angle at the future moment;

[0152] Use the driving force / longitudinal force prediction model to predict the vehicle's driving force / braking force at a future time using the accelerator pedal position and brake pedal position as input signals;

[0153] Through the vehicle steering wheel angle prediction module, the time series of the front wheel angle at future moments is obtained;

[0154] Based on the current longitudinal velocity and yaw rate of the vehicle, the longitudinal distance traveled and the heading angle of the vehicle in the current coordinate system are predicted in the future.

[0155] Obtain the spatial slope plane of the vehicle at the future time, and establish the longitudinal slope vector, transverse slope vector, and unit normal vector of the spatial slope plane at the future time by combining the longitudinal and lateral slope angles and the vehicle position at the future time;

[0156] The longitudinal slope vector and the transverse slope vector are rotated to obtain a rotation matrix, and the longitudinal slope angle and the transverse slope angle at the future moment are obtained.

[0157] For the method steps disclosed in the above embodiments, for the purpose of simple description, the method steps are expressed as a series of action combinations. However, those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0158] like Figure 2 The vehicle road slope prediction system shown in FIG. 1 specifically includes:

[0159] A spatial slope surface establishment module is used to establish a spatial slope surface and set the vehicle driving area as: a slope perceptible area, an imperceptible slope area, and an estimable slope area;

[0160] The slope angle observation value setting module is used to set the slope angles of the slope-perceivable area and the slope-estimated area as observation values, obtain the ground slope value in front of the vehicle and the slope value under the vehicle, and use them to calculate the actual slope value of the slope-imperceivable area at the current moment;

[0161] A weighted comprehensive measurement error calculation module is used to set weighted constraint conditions based on the observation values, and determine the minimum value of the error by calculating the weighted comprehensive measurement error;

[0162] The optimal weight value decision 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 of a certain point in the slope imperceptible area at a future time;

[0163] The slope vector and slope plane unit normal vector calculation module predicts the vehicle trajectory and establishes the longitudinal slope vector and lateral slope vector based on the longitudinal slope angle and lateral slope angle at a certain point in the future in the slope imperceptible area. It also establishes the unit normal vector of the slope plane in the future imperceptible area.

[0164] The vector rotation and matrix calculation module rotates the longitudinal slope vector and the lateral slope vector around the unit normal vector, performs matrix calculation, and obtains the slope value of the vehicle at a certain point in the future.

[0165] It is worth noting that although only the basic functional modules of the vehicle road slope prediction system are disclosed in this embodiment, it does not mean that the composition of this system is limited to the above basic functional modules. On the contrary, what this embodiment wants to express is that on the basis of the above basic functional modules, those skilled in the art can arbitrarily add one or more functional modules in combination with the existing technology to form an infinite number of embodiments or technical solutions. In other words, this system is open rather than closed. Just because this embodiment only discloses individual basic functional modules, it cannot be considered that the scope of protection of the claims of the present invention is limited to the disclosed basic functional modules. At the same time, for the convenience of description, the above devices are described in terms of functions, which are divided into various units and modules. Of course, when implementing the present invention, the functions of each unit and module can be implemented in the same or one or more software and / or hardware.

[0166] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0167] like Figure 3 The diagram shows the relationship between the sensor perception angle and the actual slope angle. The visual sensor can obtain the relative angle between the road surface 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's field of view (FOV) and the sensor's blind spot, and the area where the slope ahead can be perceived is limited, so it is impossible to obtain a continuous slope value ahead of the vehicle. The method disclosed in the embodiment of the present invention is divided into two steps: the establishment of a spatial slope surface and the kinematic-based method. First, the vehicle's forward-perceived slope signal and the wheel-under-estimated slope signal are used to construct the spatial planes ahead of the vehicle and under the wheels respectively. Then, based on the slope signal covariance, the weighted curve coefficient is obtained, and then a continuous weighted curve is obtained to achieve the fusion of the front and rear spatial planes in the slope-imperceptible area to obtain a continuous spatial surface. Then, based on the calculated spatial slope surface and the expected driving trajectory, the slope value of the vehicle at a certain moment in the future is predicted.

[0168] According to common sense in driving, life, and traffic, the vehicle's driving area includes the area in front of the vehicle and under the vehicle 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, for the same road, the slope is positive when the vehicle faces an uphill slope and negative when facing a downhill slope.

[0169] like Figure 4The relationship between the sensor perception angle and the actual slope angle is shown in the figure. c is the angle between the vehicle body plane and the longitudinal slope of the road ahead, i.e. the longitudinal slope signal ahead output by the visual sensor; α f is the angle between the front road surface plane and the horizontal plane in the longitudinal direction of the vehicle, that is, the actual slope angle of the front road surface; α s is the angle between the vehicle body and the ground below; α r is the actual slope angle of the ground under the vehicle, which is calculated and output by the vehicle controller. It can be obtained by converting the visual sensor value α c The formula for converting to the actual front road slope angle is:

[0170] α f =α c +α s +α r

[0171] where α s The angle is determined by:

[0172]

[0173] Among them, H fl is the left front suspension height, H fr is the right front suspension height, H rl is the height of the left rear suspension, H rr is the right rear suspension height, L axis is the vehicle wheelbase.

[0174] Similarly, the lateral slope angle β of the front road surface can also be calculated f :

[0175] β f =β c +β s +β r

[0176] Where: s The angle is determined by:

[0177]

[0178] Where: β f is the lateral slope angle of the road surface ahead; β c The lateral angle between the vehicle body plane and the front road surface plane; β r is the lateral slope angle under the vehicle wheel; L wheelbase The wheelbase on both sides of the vehicle.

[0179] like Figure 5The diagram below shows the dimensions of the imperceptible area at each angle for calculating the slope. To obtain the slope information within the imperceptible area, the length of the imperceptible area must also be determined. The visual sensor installation position is fixed at a fixed height relative to the vehicle body, and the sensor's field of view angle is also fixed relative to the vehicle body. Under typical terrain, the length of the imperceptible area is L. sg It can be determined by the following formula:

[0180]

[0181]

[0182]

[0183] Where: H g H is the vertical distance between the sensor and the ground under the vehicle; b is the distance between the sensor and the vehicle body, which is a fixed value; r w is the wheel radius; α sd is the angle between the lower edge of the camera's viewing angle and the vertical line of the vehicle's floor, which is a fixed value; sg L is the angle between the edge of the camera's view and the ground plane under the vehicle; sd is the depth information of the edge perception under the camera’s perspective; L wc L is the distance between the sensor and the front wheel axis in the vehicle's forward direction, which is approximately a fixed value; sg The length of the imperceptible area consists of two sections of road surface.

[0184] The above formula calculates the length of the imperceptible area at the intersection of two typical slopes. While actual road slopes vary widely, this method has a small error under different operating conditions and can be used for subsequent calculations.

[0185] After the above calculation, it can be known that the longitudinal slope angle of the slope plane closest to the vehicle in the front sensing area is α f0 , the lateral slope angle is β f0 , the longitudinal slope angle of the plane under the vehicle wheel is α r , the lateral slope angle of the plane under the vehicle wheel is β r For the slope value of the middle slope imperceptible area, the longitudinal and lateral slope angles of the front and rear planes are used along the length L of the imperceptible area. sg Perform weighted fusion and use the slope signal covariance to determine the weighted curve coefficient. The slope angle of the front plane and the slope angle of the rear plane can be regarded as two observation values ​​of the slope angle of the middle imperceptible area. Assuming that the middle imperceptible area is a plane, we have:

[0186] O j (k) = X(k) + n j (k)j=1,2

[0187] Among them: j (k) is the slope observation value at time k; X(k) is the actual slope value of the middle plane; n j (k) Observation noise.

[0188] Since the previous and next observations are independent of each other, the estimated value of the middle plane can be expressed as:

[0189]

[0190] Among them: r1, r2 are the weights of the slope angles of the front and rear planes respectively; * (k) is the weighted mid-plane slope angle.

[0191] The measurement error of the front and rear slope measurements at time k is:

[0192] e j (k) = X(k|k-1)-O j (k)j=1,2

[0193] Among them, e j (k) is a component in the measurement error vector, and X(k|k-1) is the slope value at time k predicted from the previous time. The weighted comprehensive measurement error is:

[0194] e * (k)=[r1(k)e j (k), r2(k)e2(k)] T

[0195] Where T is the transpose symbol. Because vertical matrices are difficult to view and format, a transpose symbol is added to horizontal matrices to facilitate viewing and formatting. Matrix transposition is common knowledge in the field. Replacing the rows (or columns) of matrix A with columns (or rows) of the same ordinal number creates a new matrix, called the transposed matrix of matrix A.

[0196] By using the least squares method to select the optimal weighted weight, the sum of squared errors can be obtained:

[0197] e *T (k)e * (k)=(r1(k)e j (k)) 2 +(r2(k)e2(k)) 2

[0198] Using r1+r2=1 as the constraint condition, we can find the minimum value of the above formula and use the Lagrange extreme value method to obtain:

[0199]

[0200] The optimal weight for fusing the front and back slope planes at time k is obtained, which represents the degree of confidence in the front and back planes. This value changes in real time with time. However, in fact, the imperceptible area in the middle is not a plane, but a continuous surface. It is also necessary to follow the length L of the imperceptible area. sg Smoothing is performed. Taking the vehicle head direction as the starting point, normalizing the region length and assigning weights using a quadratic function, we can obtain the following formula:

[0201]

[0202] Where: e is the weight of the front and back planes that changes with distance; k r is the weight coefficient, which determines the curvature of the weighted curve; x is L sg The normalized distance in the direction of the vehicle's front; s is the distance between the slope prediction point and the vehicle's front axle in the longitudinal direction of the vehicle.

[0203] In summary, in the longitudinal direction of the vehicle, the longitudinal lateral slope angle of a point at a distance s in front of the front axle of the vehicle should be as follows:

[0204]

[0205] Where: α m is the longitudinal slope at the prediction point; β m is the lateral slope at the prediction point.

[0206] Due to the low lateral velocity of the vehicle and the limited lateral sensing range of the sensor, the above method only considers the distribution of weights in the longitudinal direction. By varying the position of s, a continuous surface can be obtained within the imperceptible region. By comparing the observed noise of the front and rear surfaces, the surface shape is dynamically adjusted. Specifically, if the slope noise perceived by the front sensor is low, the surface is more reliable for the front surface; if the slope noise estimated by the rear vehicle is low, the surface is more reliable for the rear surface.

[0207] like Figure 6 The schematic diagram of vehicle motion parameters required for slope prediction is shown. In this embodiment, the slope can be predicted based on the dynamic state:

[0208] After obtaining the spatial surface, the vehicle's position and heading angle at a future moment need to be predicted. This calculation relies on the vehicle's trajectory points estimated by the vehicle controller based on the predicted dynamic parameters. If this information is not available, a prediction can be made based on the vehicle's current state.

[0209] If the vehicle does not have trajectory prediction function, the vehicle's current kinematic parameters and vehicle dynamics model can be used to predict the longitudinal mileage s at the future moment. k+n , heading deflection angle θ k+nThe specific method for estimation is as follows:

[0210] Since the driver's operation frequency is low, it is assumed that the operation instructions given by the driver from time k to time k+n remain unchanged, that is, the accelerator pedal position, brake pedal position, and steering wheel angle are constant. At time k, the driving force / longitudinal force prediction model uses the accelerator pedal position and brake pedal position as input signals to predict the time series of the vehicle's driving force / braking force at time [k, k+1, k+2...k+n]. Similarly, the vehicle steering wheel angle is used as input to the front wheel angle prediction model to obtain the time series of the vehicle's front wheel angle from time k to time k+n. At time k, the vehicle uses the current longitudinal velocity Current yaw rate Predict the longitudinal mileage of the vehicle in the current coordinate system at time k+1 and heading deflection angle θ k+1 , as follows:

[0211]

[0212] Where: t0 is the unit time of each interval from time k to time k+n.

[0213] use Combining the above formulas, we can obtain the spatial slope plane of the vehicle at time k+1:

[0214]

[0215] Combine the longitudinal and lateral slope angles and vehicle position at time k+1 to establish the longitudinal and lateral slope vectors Establish the unit normal vector of the spatial slope plane at time k+1

[0216]

[0217] make Around the unit normal vector With θ k+1 Angle to rotate:

[0218]

[0219]

[0220] Finally based on The longitudinal slope angle of the vehicle at time k+1 can be successfully calculated Side slope angle

[0221] like Figure 7 The calculation block diagram of a single iteration cycle for continuous prediction of vehicle slope conditions at future moments is shown, using the prediction model from The slope information at time k+1 just calculated is input into the dynamic model to complete the longitudinal velocity at time k+1. Yaw angular velocity The above calculation steps are an iterative calculation step. Repeating the above steps can achieve continuous prediction of the vehicle slope conditions at time k+2, k+3...k+n.

[0222] Assuming that the current time is time k, if we want to predict the vehicle's slope at time k+n, we need the vehicle's position at time k+n in the current vehicle coordinate system. If the vehicle has the function of trajectory prediction, this information is considered known. We have the longitudinal mileage s at time k+n in the vehicle's current coordinate system. k+n , heading deflection angle θ k+n .

[0223] The embodiment of the present invention realizes the continuous estimation of the slope spatial information in the imperceptible area ahead at any time, and then realizes the prediction of the vehicle position and heading angle based on the predicted calculation of the vehicle dynamics parameters, and realizes the prediction of the vehicle slope condition at future moments through iterative update calculation. The driving force / longitudinal force prediction model, the front wheel turning angle prediction model, and the vehicle dynamics model are existing technologies. Those skilled in the art can realize the specific application of the driving force / longitudinal force prediction model, the front wheel turning angle prediction model, and the vehicle dynamics model in this embodiment by relying on the existing technologies they have mastered.

[0224] like Figure 8 As shown, the present invention discloses a method and system for predicting the slope of a vehicle road surface, and also discloses corresponding electronic equipment and storage media:

[0225] An electronic device includes: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of a method for predicting the slope of a vehicle driving road.

[0226] A computer-readable storage medium stores a computer program executable by an electronic device. When the computer program runs on the electronic device, the electronic device executes the steps of a method for predicting the slope of a vehicle driving road.

[0227] The communication bus mentioned in the electronic device mentioned above may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. This communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is used in the figure, but this does not mean that there is only one bus or only one type of bus.

[0228] The electronic device includes a hardware layer, an operating system layer running on the hardware layer, and an application layer running on the operating system. The hardware layer includes hardware such as a central processing unit (CPU), a memory management unit (MMU), and memory. The operating system can be any one or more computer operating systems that control electronic devices through processes, such as the Linux operating system, the Unix operating system, the Android operating system, the iOS operating system, or the Windows operating system. In the embodiments of the present invention, the electronic device can be a handheld device such as a smartphone or a tablet computer, or an electronic device such as a desktop computer or a portable computer, which is not particularly limited in the embodiments of the present invention.

[0229] The execution subject of the electronic device control in the embodiment of the present invention can 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 in detail in the embodiment of the present invention.

[0230] The electronic device can also obtain a 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.

[0231] In this case, 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 which the corresponding firmware is written, thereby resetting 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 in the existing technology and will not be described in detail in the embodiments of the present invention.

[0232] The present invention also discloses a vehicle with a slope prediction function, specifically comprising:

[0233] An electronic device for implementing a method for predicting the slope of a road surface on which a vehicle is traveling;

[0234] a processor, the processor running a program, and when the program is running, executing the steps of the vehicle driving road slope prediction method for the data output from the electronic device;

[0235] The storage medium is used to store a program, which, when running, executes the steps of the vehicle driving road slope prediction method for data output from the electronic device.

[0236] The vehicle with slope prediction function disclosed in the present invention can combine the forward-looking perceived slope and its own slope to realize slope prediction in imperceptible areas; use covariance weighting to realize the fusion of front and rear slope information; use dynamic information to predict position angle, use position angle information to determine the slope value, use the slope prediction value to update the dynamic information, and realize continuous prediction of the slope at any time in the future through multiple iterative calculation methods.

[0237] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

[0238] Those skilled in the art will understand that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by those skilled in the art in the art to which the present invention pertains. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with those in the context of the prior art and, unless specifically defined, will not be interpreted in an idealized or overly formal sense.

[0239] It should be noted that certain words are used in this specification and claims to refer to specific components. Those skilled in the art should understand that vehicle manufacturers may use different nouns to refer to the same component. This specification and claims do not use the difference in nouns as a way to distinguish components, but use the functional differences of the components as the criterion for distinction. As mentioned throughout the specification and claims, "including" or "comprising" is an open term, so it should be understood as "including but not limited to". The preferred embodiment of the present invention will be described later, but the description is based on the general principles of the specification and is not used to limit the scope of the invention. The scope of protection of the present invention shall be based on the definition of the claims attached thereto.

[0240] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0241] In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in this field can implement it. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0242] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of the devices, methods, and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, which contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, as well as the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0243] In addition, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0244] Those skilled in the art will appreciate that the modules in the devices in the embodiments can be adaptively changed and set in one or more devices different from the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and in addition they can be divided into multiple submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including corresponding claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner can be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including corresponding claims, abstracts and drawings) can be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0245] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of the present invention and to form different embodiments. For example, in the claims below, any of the claimed embodiments may be used in any combination.

[0246] The various component embodiments of the present invention can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components in the device for distributing messages according to an embodiment of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program for implementing the present invention can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can 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 slope of a vehicle road surface, characterized in that: Specifically include: Establish a spatial slope surface and set the vehicle driving area as: slope-perceivable area, slope-imperceivable area and slope-estimated area; The slope angles of the slope-perceivable area and the slope-estimated area are set as observation values, and the ground slope value in front of the vehicle and the slope value under the vehicle are obtained to calculate the actual slope value of the slope-imperceivable area at the current moment. Setting weighted constraints based on the observed values, and determining a minimum error by calculating the weighted comprehensive measurement error; Obtain the optimal weight value of the constraint condition in real time, make a decision based on the optimal weight value, and obtain the longitudinal slope angle and lateral slope angle of a certain point in the future time in the slope imperceptible area; Predict the vehicle trajectory, combine the longitudinal slope angle and lateral slope angle at a certain point in the slope imperceptible area at a future time, establish the longitudinal slope vector and lateral slope vector, and establish the unit normal vector of the slope plane in the future imperceptible area; The longitudinal slope vector and the lateral slope vector are rotated around the unit normal vector, and matrix calculation is performed to obtain the slope value of the vehicle at a certain point in the future. For the slope value of the imperceptible area, use the longitudinal and lateral slope angles of the front and rear planes along the length L of the imperceptible area. sg Perform weighted integration and use the slope signal covariance to determine the weighted curve coefficients; The slope angle of the front plane and the slope angle of the rear plane are regarded as two observation values ​​of the slope angle of the middle imperceptible area. Assuming that the middle imperceptible area is an intermediate plane, the estimated value expression of the intermediate plane is obtained: r1 and r2 are the weights of the slope angles of the front and rear planes, respectively. * (k) is the weighted mid-plane slope; calculate the measurement error of the front and back slope measurements at time k: e j (k)=X(k|k-1)-O j (k)j=1,2 Among them, e j (k) is a component of the measurement error vector, X(k|k-1) is the slope value at time k predicted from the previous time; The weighted comprehensive measurement error is: and * (k)=[r1(k)e j (k),r2(k)e2(k)] T Where T is the transposition symbol; The least squares method is used to select the optimal weighted weights and obtain the sum of squared errors: and *T (cold * (k)=(r1(k)e j (k)) 2 +(r2(k)e2(k)) 2 Using r1+r2=1 as the constraint condition, we can find the minimum value of the above formula and use the Lagrange extreme value method to obtain: Obtain the optimal weight for fusing the front and rear slope planes at time k, which represents the degree of confidence in the front and rear planes. This value changes in real time over time. Along the length L of the imperceptible area sg Perform smoothing, take the vehicle head direction as the starting point, normalize the area length and assign weights using a quadratic function, and we get the following formula: In the above formula: e is the weight of the front and back planes that changes with distance; k r is the weight coefficient, which determines the curvature of the weighted curve; x is L sg The normalized distance in the vehicle head direction; s is the distance between the slope prediction point and the front axle of the vehicle in the longitudinal direction of the vehicle; In summary, in the longitudinal direction of the vehicle, the longitudinal lateral slope angle of a point at a distance s in front of the front axle of the vehicle is: Where: α m is the longitudinal slope of the prediction point; β m is the lateral slope at the prediction point; β f is the lateral slope angle of the road surface ahead, β r is the lateral slope angle under the vehicle wheel, and the actual slope angle of the ground under the vehicle is α r , actual front road slope angle α f .

2. The method for predicting the slope of a vehicle road surface according to claim 1, wherein: The slope perceptible 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 a slope area under the vehicle wheels; The slope imperceptible area is located between the slope perceptible area and the slope estimable area.

3. The method for predicting the slope of a vehicle road surface according to claim 1, wherein: The method of obtaining the ground slope value in front of the vehicle and the slope value under the vehicle specifically includes: the longitudinal slope angle α of the road ahead c , actual front road slope angle α f 、Angle α between the vehicle body and the ground s and the actual slope angle α of the ground under the vehicle r ,in: The longitudinal slope angle of the road ahead is specifically the angle between the vehicle body plane and the longitudinal slope of the road ahead; The actual front road slope angle is specifically the angle between the front road plane 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 below 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; The ground slope value and the vehicle slope value satisfy the following formula: Actual front road slope angle = front road longitudinal slope angle + vehicle body relative to ground angle + actual ground slope angle under the vehicle; Angle α between the vehicle body and the ground s Satisfies the formula: Among them: H fl is the left front suspension height, H fr is the right front suspension height, H rl is the height of the left rear suspension, H rr is the right rear suspension height, L axis is the vehicle wheelbase; Also includes: the lateral slope angle β of the front road surface f , lateral slope angle β f Satisfies the formula: β f ＝β c +β s +β r where β s The angle is determined by: Where: c The lateral angle between the vehicle body plane and the front road surface; L wheelbase The wheelbase on both sides of the vehicle.

4. The method for predicting the slope of a vehicle road surface according to claim 3, wherein: The length of the imperceptible area L sg Satisfies the following formula: In the above formula: H g H is the vertical distance between the sensor and the ground under the vehicle; b is the distance between the sensor and the vehicle body, which is a fixed value; r w is the wheel radius; α sd is the angle between the lower edge of the camera's viewing angle and the vertical line of the vehicle's floor, which is a fixed value; sg L is the angle between the edge of the camera's view and the ground plane under the vehicle; sd is the depth information of the edge perception under the camera’s perspective; L wc L is the distance between the sensor and the front wheel axis in the vehicle's forward direction, which is a fixed value; sg The length of the imperceptible area consists of two sections of road surface.

5. The method for predicting the slope of a vehicle road surface according to claim 1, wherein: When predicting the vehicle trajectory, if the vehicle does not have trajectory prediction function, the vehicle's current kinematic parameters and vehicle dynamics model are used to estimate the longitudinal mileage and heading deflection angle at the future moment; Use the driving force / longitudinal force prediction model to predict the vehicle's driving force / braking force at a future time using the accelerator pedal position and brake pedal position as input signals; Through the vehicle steering wheel angle prediction module, the time series of the front wheel angle at future moments is obtained; Based on the current longitudinal velocity and yaw rate of the vehicle, the longitudinal distance traveled and the heading angle of the vehicle in the current coordinate system are predicted in the future. Obtain the spatial slope plane of the vehicle at the future time, and establish the longitudinal slope vector, transverse slope vector, and unit normal vector of the spatial slope plane at the future time by combining the longitudinal and lateral slope angles and the vehicle position at the future time; The longitudinal slope vector and the transverse slope vector are rotated to obtain a rotation matrix, and the longitudinal slope angle and the transverse slope angle at the future moment are obtained.

6. A vehicle road slope prediction system, characterized in that: Specifically include: A spatial slope surface establishment module is used to establish a spatial slope surface and set the vehicle driving area as: a slope perceptible area, an imperceptible slope area, and an estimable slope area; The slope angle observation value setting module is used to set the slope angles of the slope-perceivable area and the slope-estimated area as observation values, obtain the ground slope value in front of the vehicle and the slope value under the vehicle, and use them to calculate the actual slope value of the slope-imperceivable area at the current moment; A weighted comprehensive measurement error calculation module is used to set weighted constraint conditions 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 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 of a certain point in the slope imperceptible area at a future time; The slope vector and slope plane unit normal vector calculation module predicts the vehicle trajectory and establishes the longitudinal slope vector and lateral slope vector based on the longitudinal slope angle and lateral slope angle at a certain point in the future in the slope imperceptible area. It also establishes the unit normal vector of the slope plane in the future imperceptible area. 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. For the slope value of the imperceptible area, use the longitudinal and lateral slope angles of the front and rear planes along the length L of the imperceptible area. sg Perform weighted integration and use the slope signal covariance to determine the weighted curve coefficients; The slope angle of the front plane and the slope angle of the rear plane are regarded as two observation values ​​of the slope angle of the middle imperceptible area. Assuming that the middle imperceptible area is an intermediate plane, the estimated value expression of the intermediate plane is obtained: r1 and r2 are the weights of the slope angles of the front and rear planes, respectively. * (k) is the weighted mid-plane slope; calculate the measurement error of the front and back slope measurements at time k: e j (k)=X(k|k-1)-O j (k)j=1,2 Among them, e j (k) is a component of the measurement error vector, X(k|k-1) is the slope value at time k predicted from the previous time; The weighted comprehensive measurement error is: and * (k)=[r1(k)e j (k),r2(k)e2(k)] T Where T is the transposition symbol; By using the least squares method to select the optimal weighted weight, the sum of squared errors can be obtained: and *T (cold * (k)=(r1(k)e j (k)) 2 +(r2(k)e2(k)) 2 Using r1+r2=1 as the constraint condition, we can find the minimum value of the above formula and use the Lagrange extreme value method to obtain: Obtain the optimal weight for fusing the front and rear slope planes at time k, which represents the degree of confidence in the front and rear planes. This value changes in real time over time. Along the length L of the imperceptible area sg Perform smoothing, take the vehicle head direction as the starting point, normalize the area length and assign weights using a quadratic function, and we get the following formula: In the above formula: e is the weight of the front and back planes that changes with distance; k r is the weight coefficient, which determines the curvature of the weighted curve; x is L sg The normalized distance in the vehicle head direction; s is the distance between the slope prediction point and the front axle of the vehicle in the longitudinal direction of the vehicle; In summary, in the longitudinal direction of the vehicle, the longitudinal lateral slope angle of a point at a distance s in front of the front axle of the vehicle is: Where: α m is the longitudinal slope of the prediction point; β m is the lateral slope at the prediction point; β f is the lateral slope angle of the road surface ahead, β r is the lateral slope angle under the vehicle wheel, and the actual slope angle of the ground under the vehicle is α r , actual front road slope angle α f .

7. An electronic device, characterized in that: include: A processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that It stores a computer program that can be executed by an electronic device. When the computer program runs on the electronic device, the electronic device executes the steps of the method according to any one of claims 1 to 5.

9. A vehicle, characterized in that: Specifically include: An electronic device for implementing the method according to any one of claims 1 to 5; a processor, the processor running a program, wherein when the program is running, the processor performs the steps of the method according to any one of claims 1 to 5 on the data output from the electronic device; A storage medium for storing a program, wherein when the program is run, the program executes the steps of the method according to any one of claims 1 to 5 for data output from an electronic device.

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