Method, device, equipment, medium and product for predicting the slope of a vehicle road surface

Abstract

The present invention discloses a method, device, equipment, medium, and product for predicting the slope of a vehicle's road surface. The method comprises: determining target angular velocity data for a target vehicle at a current time node; obtaining historical road surface slope values ​​at a previous time node, and determining a predicted angular velocity slope value based on the historical road surface slope values ​​and the target angular velocity data; determining target acceleration data for the target vehicle at the current time node based on the predicted angular velocity slope value; determining a predicted acceleration slope value based on the target acceleration data; and determining the target road surface slope value for the target vehicle at the current time node based on the predicted acceleration slope value and the predicted angular velocity slope value. The above technical solution improves the accuracy of detecting the slope of a vehicle's road surface.

Description

Technical Field

[0001] The present invention relates to the field of automobile control technology, and in particular to a method, device, equipment, medium and product for predicting the slope of a vehicle driving road. Background Art

[0002] In automotive control systems, accurately acquiring information about the road surface slope is crucial for various aspects of vehicle power control, fuel economy, and comfort. For example, in vehicle starting control, starting gear selection and clutch torque control are closely related to road slope.

[0003] The current mainstream slope estimation method uses a single sensor, such as an accelerometer or gyroscope. This sensor-based detection method has certain errors, resulting in low slope detection accuracy. Summary of the Invention

[0004] The present invention provides a method, device, equipment, medium and product for predicting the slope of a vehicle-driving road surface, so as to improve the accuracy of detecting the slope of a vehicle-driving road surface.

[0005] According to one aspect of the present invention, a method for predicting the slope of a vehicle driving road is provided, the method comprising:

[0006] Determine the target angular velocity data of the target vehicle at the current time node;

[0007] Obtaining a historical road surface slope value at a previous time node, and determining an angular velocity slope prediction value based on the historical road surface slope value and the target angular velocity data;

[0008] Determining target acceleration data of the target vehicle at a current time node based on the angular velocity gradient prediction value;

[0009] Determining an acceleration gradient prediction value according to the target acceleration data;

[0010] A target road surface slope value of the target vehicle at the current time node is determined according to the acceleration slope prediction value and the angular velocity slope prediction value.

[0011] According to another aspect of the present invention, a device for predicting the slope of a road surface on which a vehicle is traveling is provided, the device comprising:

[0012] A target angular velocity determination module is used to determine the target angular velocity data of the target vehicle at the current time node;

[0013] An angular velocity gradient prediction module is configured to obtain a historical road surface gradient value at a previous time node and determine an angular velocity gradient prediction value based on the historical road surface gradient value and the target angular velocity data;

[0014] a target acceleration determination module, configured to determine target acceleration data of the target vehicle at a current time node based on the angular velocity gradient prediction value;

[0015] An acceleration gradient prediction module, configured to determine an acceleration gradient prediction value based on the target acceleration data;

[0016] The road surface gradient value prediction module is used to determine the target road surface gradient value of the target vehicle at the current time node according to the acceleration gradient prediction value and the angular velocity gradient prediction value.

[0017] According to another aspect of the present invention, an electronic device is provided, comprising:

[0018] at least one processor; and

[0019] a memory communicatively connected to the at least one processor; wherein,

[0020] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the vehicle driving road slope prediction method described in any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle driving road slope prediction method described in any embodiment of the present invention when executed.

[0022] According to another aspect of the present invention, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the computer program implements the above-mentioned method for predicting the slope of a vehicle driving road.

[0023] The technical solution of the embodiment of the present invention determines the target angular velocity data of the target vehicle at the current time node, and determines the angular velocity slope prediction value based on the historical road slope value and the target angular velocity data; determines the target acceleration data of the target vehicle at the current time node based on the angular velocity slope prediction value; determines the acceleration slope prediction value based on the target acceleration data; and determines the target road slope value of the target vehicle at the current time node based on the acceleration slope prediction value and the angular velocity slope prediction value. In the process of estimating the target road slope value of the target vehicle, the above technical solution comprehensively considers the acceleration sensor data and the angular velocity sensor data, and uses the acceleration data fusion method of the two sensors to perform slope estimation, thereby improving the accuracy of road slope estimation and avoiding the problem of low accuracy of short-term or long-term estimation results in the process of using a single sensor for slope estimation.

[0024] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 This is a flow chart of a method for predicting the slope of a vehicle road surface provided in accordance with the first embodiment of the present invention;

[0027] Figure 2 This is a flow chart of a method for predicting the slope of a vehicle road surface provided in accordance with a second embodiment of the present invention;

[0028] Figure 3 This is a flow chart of a method for predicting the slope of a vehicle road surface provided in accordance with a third embodiment of the present invention;

[0029] Figure 4 2 is a schematic structural diagram of a device for predicting the slope of a road surface on which a vehicle is traveling, according to a fourth embodiment of the present invention;

[0030] Figure 5 The present invention is a schematic diagram of the structure of an electronic device for implementing the method for predicting the slope of a vehicle road surface according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] Example 1

[0034] Figure 1 This is a flow chart of a method for predicting the slope of a road surface during vehicle driving provided in the first embodiment of the present invention. This embodiment is applicable to the case of predicting the slope of a road surface during vehicle driving. The method can be executed by a vehicle road surface slope prediction device. The vehicle road surface slope prediction device can be implemented in the form of hardware and / or software. The vehicle road surface slope prediction device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0035] S110: Determine target angular velocity data of the target vehicle at the current time node.

[0036] S120: Obtain a historical road surface slope value at a previous time node, and determine an angular velocity slope prediction value based on the historical road surface slope value and the target angular velocity data.

[0037] S130 : Determine target acceleration data of the target vehicle at the current time node based on the angular velocity gradient prediction value.

[0038] S140: Determine an acceleration gradient prediction value based on the target acceleration data.

[0039] S150 : Determine a target road surface slope value of the target vehicle at the current time node based on the acceleration slope prediction value and the angular velocity slope prediction value.

[0040] The target vehicle may be a vehicle for which a real-time road slope prediction is to be performed during driving. The road slope prediction for the target vehicle during driving may be a real-time prediction or a scheduled prediction. The current time node may be the moment or time point at which the road slope prediction is currently being performed.

[0041] The target angular velocity data may be angular velocity data detected by a gyroscope sensor. To improve the accuracy of determining the angular velocity data, an error caused by zero drift of the sensor may be considered in determining the angular velocity data.

[0042] In an optional embodiment, determining target angular velocity data of the target vehicle at the current time node includes: obtaining sensor angular velocity data of the target vehicle at the current time node; and obtaining reference angular velocity data of the target vehicle within a preset time period after power-on, and determining an angular velocity zero drift estimate value based on the reference angular velocity data; and determining the target angular velocity data of the target vehicle at the current time node based on the sensor angular velocity data and the angular velocity zero drift estimate value.

[0043] The sensor angular velocity data can be acquired in real time or periodically through gyroscope sensor detection. The preset time period after the target vehicle is powered on can be pre-set by relevant technicians based on actual needs, for example, several acquisition cycles (e.g., 100 to 500) within a short period of time (e.g., 0.5 to 2 seconds) after power-on.

[0044] Specifically, the sensor angular velocity data collected by the gyroscope sensor at the current time node is obtained. The reference angular velocity data collected in several sampling periods within a short period of time after the target vehicle is powered on is obtained. The average value of the reference angular velocity data is determined as the angular velocity zero drift estimate value of the sensor angular velocity collected by the gyroscope sensor. For example, the angular velocity zero drift estimate value w bias is determined as follows:

[0045]

[0046] Among them, w i represents the i-th reference angular velocity data; n represents the number of reference angular velocity data.

[0047] The sensor angular velocity data w sensor and the angular velocity zero drift estimate w bias The difference between them is determined as the target angular velocity data w of the target vehicle at the current time node k. obj,k .

[0048] w obj,k =w sensor,k -w bias,k ;

[0049] For example, the predicted angular velocity gradient value θ at the current time node k is pre,k The determination method is as follows.

[0050] θ pre,k =θ k-1 +w obj,k Δt;

[0051] Among them, θ k-1 Represents the historical road slope value θ at the previous time node k-1 k-1 ; Δt represents the time difference between the previous time node and the current time node; w obj,k Indicates the target angular velocity data at the current time node k.

[0052] Based on the angular velocity gradient prediction value, the target acceleration data for the target vehicle at the current time point is determined. It should be noted that the error range of the angular velocity data can be determined through calibration, while the error range of the acceleration data is unknown but has a certain mapping relationship with the angular velocity data. The error range of the acceleration data can be determined based on the error range of the angular velocity data and the angular velocity gradient prediction value. The target acceleration data is the acceleration difference between the sensor acceleration data and the vehicle longitudinal acceleration data. The error range of the acceleration data can be used to set a threshold for the target acceleration data, thereby improving the accuracy of the target acceleration data determination.

[0053] According to the target acceleration data a x , determine the acceleration slope prediction value θ at the current time node k obs The way is as follows.

[0054]

[0055] Among them, a x,k Indicates the target acceleration data at the current time node k; g represents the acceleration due to gravity.

[0056] The target road surface slope value for the target vehicle at the current time point is determined based on the predicted acceleration slope value and the predicted angular velocity slope value. Specifically, the target road surface slope value can be obtained by performing a weighted summation based on pre-set weights corresponding to the predicted acceleration slope value and the predicted angular velocity slope value. The weights corresponding to the predicted acceleration slope value and the predicted angular velocity slope value can be pre-set by relevant technical personnel based on actual experience, experimental values, or actual needs.

[0057] The technical solution of the embodiment of the present invention determines the target angular velocity data of the target vehicle at the current time node, and determines the angular velocity slope prediction value based on the historical road slope value and the target angular velocity data; determines the target acceleration data of the target vehicle at the current time node based on the angular velocity slope prediction value; determines the acceleration slope prediction value based on the target acceleration data; and determines the target road slope value of the target vehicle at the current time node based on the acceleration slope prediction value and the angular velocity slope prediction value. In the process of estimating the target road slope value of the target vehicle, the above technical solution comprehensively considers the acceleration sensor data and the angular velocity sensor data, and uses the acceleration data fusion method of the two sensors to perform slope estimation, thereby improving the accuracy of road slope estimation and avoiding the problem of low accuracy of short-term or long-term estimation results in the process of using a single sensor for slope estimation.

[0058] Example 2

[0059] Figure 2 This is a flow chart of a method for predicting the slope of a vehicle road surface provided in the second embodiment of the present invention. This embodiment is optimized and improved on the basis of the above technical solutions.

[0060] Furthermore, the step of "determining the target acceleration data of the target vehicle at the current time node based on the angular velocity slope prediction value" is refined into "obtaining the sensor acceleration data and current vehicle speed data of the target vehicle at the current time node; determining the longitudinal acceleration data based on the current vehicle speed data; determining the reference acceleration data based on the sensor acceleration data and the longitudinal acceleration data; determining the acceleration error range interval based on the angular velocity slope prediction value; determining the target acceleration data based on the acceleration error range interval based on the reference acceleration data." to improve the method of determining the target acceleration data.

[0061] It should be noted that for the parts not described in detail in the embodiments of the present invention, reference can be made to the descriptions of other embodiments. Figure 2 As shown, the method includes the following specific steps:

[0062] S210: Determine target angular velocity data of the target vehicle at the current time node.

[0063] S220: Obtain the historical road surface slope value at the previous time node, and determine the angular velocity slope prediction value based on the historical road surface slope value and the target angular velocity data.

[0064] S230: Obtain sensor acceleration data and current vehicle speed data of the target vehicle at the current time node.

[0065] S240: Determine longitudinal acceleration data based on current vehicle speed data.

[0066] S250 : Determine reference acceleration data according to the sensor acceleration data and the longitudinal acceleration data.

[0067] S260: Determine an acceleration error range based on the angular velocity gradient prediction value.

[0068] S270: Determine target acceleration data based on the reference acceleration data and the acceleration error range.

[0069] S280: Determine an acceleration gradient prediction value based on the target acceleration data.

[0070] S290: Determine a target road surface slope value for the target vehicle at the current time node based on the acceleration slope prediction value and the angular velocity slope prediction value.

[0071] The target vehicle's sensor acceleration data at the current time point can be obtained through an accelerometer, and the current vehicle speed data can be obtained through a CAN (Controller Area Network) bus. Optionally, a low-pass filter can be used to filter out high-frequency noise in the sensor angular velocity data and the current vehicle speed data.

[0072] For the current vehicle speed data v can Differentiate to obtain the original longitudinal acceleration data a of the target vehicle veh , let the current time node be k, and the specific determination method is as follows.

[0073]

[0074] Among them, v can,k represents the current vehicle speed data at the current time node k; v can,k-1 represents the historical vehicle speed data at the previous time node k-1; Δt represents the time difference between the current time node k and the previous time node k-1.

[0075] According to the sensor acceleration data a at the current time node k sensor,k and longitudinal acceleration data a veh,k , determine the reference acceleration data a x,k The way is as follows.

[0076] a x,k =a sensor,k -a veh,k ;

[0077] Based on the angular velocity gradient prediction value, the acceleration error range is determined. It should be noted that the angular velocity error range is known through calibration, and the acceleration error range can be determined based on the correlation between the angular velocity data and the acceleration data.

[0078] In an optional embodiment, based on the angular velocity slope prediction value, the acceleration error range interval is determined, including: obtaining the angular velocity interval upper limit value and the angular velocity interval lower limit value of the angular velocity error range interval, and obtaining the slope estimation error at the previous time node; according to the angular velocity interval upper limit value and the angular velocity slope prediction value, based on the slope estimation error, determining the acceleration interval upper limit value of the angular velocity error range interval; and, according to the angular velocity interval lower limit value and the angular velocity slope prediction value, based on the slope estimation error, determining the acceleration interval lower limit value of the angular velocity error range interval.

[0079] Assume that the angular velocity error range obtained by calibration is err low ≤Δθ err ≤err high , where Δθ err Indicates angular velocity error, err low and err high They represent the upper and lower limits of the angular velocity range, respectively. x and angular velocity error Δθ err The relationship between them is as follows.

[0080] Δa x =g[sin(θ pre,k )-sinθ pre,k-1 ]+gsin(Δθ err )cos(θ pre,k )

[0081] +gsin(θ err )[cos(θ pre,k )-cosθ pre,k-1 ];

[0082] Among them, θ pre,k is the predicted value of the angular velocity slope at the current time node; θ pre,k-1 Represents the predicted value of the angular velocity slope at the previous time node; θ err θ err Indicates the slope estimation error of the previous cycle. Since the slope is not very large under actual working conditions, sin(θ err )≈0.

[0083] Δa can be determined by the above relationship x The acceleration error range of the acceleration interval upper limit a xlow and the lower limit of the acceleration range a xhigh .

[0084] a xlow =g[sin(θ pre,k )-sinθpre,k-1 ]+gsin(err low )cos(θ pre,k )

[0085] +gsin(θ err )[cos(θ pre,k )-cosθ pre,k-1 ];

[0086] a xhigh =g[sin(θ pre,k )-sinθ pre,k-1 ]+gsin(err high )cos(θ pre,k )

[0087] +gsin(θ err )[cos(θ pre,k )-cosθ pre,k-1 ];

[0088] The target acceleration data is determined based on the reference acceleration data and the acceleration error range. Specifically, the target acceleration data is compared with the acceleration error range, and the acceleration data is limited by the upper and lower limits of the acceleration error range.

[0089] In an optional embodiment, target acceleration data is determined based on the reference acceleration data and the acceleration error range interval, including: if the reference acceleration data is within the acceleration error range interval, the reference acceleration data is determined as the target acceleration data; if the reference acceleration data is not within the acceleration error range interval, a first difference between the reference acceleration data and the upper limit value of the acceleration interval of the acceleration error range interval, and a second difference between the reference acceleration data and the lower limit value of the acceleration interval of the acceleration error range interval is determined; if the first difference is greater than the second difference, the lower limit value of the acceleration interval is determined as the target acceleration data; if the first difference is not greater than the second difference, the upper limit value of the acceleration interval is determined as the target acceleration data.

[0090] For example, if the acceleration error range interval is [a, b] and the reference acceleration data is c, if the reference acceleration data c is within the acceleration error range interval [a, b], the reference acceleration data c is determined as the target acceleration data. If the reference acceleration data c is not within the acceleration error range interval [a, b], a first difference |ac| between the reference acceleration data c and the acceleration interval upper limit a of the acceleration error range interval is determined, as well as a second difference |bc| between the reference acceleration data c and the acceleration interval lower limit b of the acceleration error range interval is determined. If the first difference is greater than the second difference, it indicates that the reference acceleration data c is closer to the acceleration interval lower limit, and the acceleration interval lower limit is determined as the target acceleration data. If the first difference is not greater than the second difference, it indicates that the reference acceleration data c is closer to the acceleration interval upper limit, and the acceleration interval upper limit is determined as the target acceleration data.

[0091] This embodiment's technical solution determines longitudinal acceleration data based on current vehicle speed data, determines reference acceleration data based on sensor acceleration data and longitudinal acceleration data, determines an acceleration error range based on the predicted angular velocity gradient, and then determines target acceleration data based on the reference acceleration data and the acceleration error range. By combining the predicted angular velocity gradient with the acceleration error range and using the acceleration error range to define the target acceleration data, this technical solution handles exceptions in the target acceleration data and improves the accuracy of determining the target acceleration data.

[0092] Example 3

[0093] Figure 3 This is a flow chart of a method for predicting the slope of a vehicle road surface provided in Example 3 of the present invention. This embodiment is optimized and improved on the basis of the above technical solutions.

[0094] Furthermore, the step "determine the target road surface slope value of the target vehicle at the current time node based on the acceleration slope prediction value and the angular velocity slope prediction value" is refined into "determine the target gain of the target vehicle at the current time node; determine the target road surface slope value of the target vehicle at the current time node based on the target gain according to the angular velocity slope prediction value and the acceleration slope prediction value." to improve the method of determining the target road surface slope value.

[0095] It should be noted that for the parts not described in detail in the embodiments of the present invention, reference can be made to the descriptions of other embodiments. Figure 3 As shown, the method includes the following specific steps:

[0096] S310: Determine target angular velocity data of the target vehicle at the current time node.

[0097] S320: Obtain the historical road surface slope value at the previous time node, and determine the angular velocity slope prediction value based on the historical road surface slope value and the target angular velocity data.

[0098] S330: Determine target acceleration data of the target vehicle at the current time node based on the angular velocity gradient prediction value.

[0099] S340: Determine the acceleration gradient prediction value according to the target acceleration data.

[0100] S350: Determine the target gain of the target vehicle at the current time node.

[0101] S360: Determine a target road surface slope value for the target vehicle at the current time node based on the angular velocity slope prediction value and the acceleration slope prediction value and the target gain.

[0102] Among them, the specific value of the target gain can be pre-set by relevant technical personnel according to actual needs. In order to achieve accurate estimation of the road surface slope, it can also be iteratively updated in real time when calculating the target road surface slope value at different time nodes.

[0103] In an optional embodiment, determining the target gain of the target vehicle at the current time node includes: determining vehicle acceleration data and vehicle angular velocity data of the target vehicle within a preset time window; constructing an observation noise covariance and a process noise covariance based on the vehicle angular velocity data and the vehicle angular velocity data; obtaining the historical error covariance at the previous time node, and determining the target gain of the target vehicle at the current time node based on the observation noise covariance, the process noise covariance, and the historical error covariance.

[0104] Specifically, a time window is created. The time window can be pre-created according to actual needs or created based on the current time node. For example, the time window can be the time period between the previous time node and the current time node.

[0105] Vehicle acceleration data and angular velocity data within the time window are collected. Based on the acceleration and angular velocity data within the time window, the observation noise covariance R and process noise covariance Q are estimated. Using the Kalman filter algorithm, the Kalman filter gain K is calculated based on the observation noise covariance R and process noise covariance Q values, and the Kalman filter gain K is determined as the target gain.

[0106] In order to further improve the calculation accuracy of the target gain, the vehicle acceleration data and vehicle angular velocity data can be comprehensively considered in the process of constructing the membership function of the observation noise variance R and the process noise covariance Q.

[0107] In an optional embodiment, the observation noise covariance and the process noise covariance are constructed based on the vehicle angular velocity data and the vehicle angular velocity data, including: determining the number of acceleration data in the vehicle acceleration data that is less than a preset acceleration threshold; and determining the angular velocity integral value of the vehicle angular velocity data in a preset time window; and constructing the observation noise covariance and the process noise covariance based on the number of acceleration data and the angular velocity integral value.

[0108] Specifically, the acceleration threshold is pre-calibrated, and the number of acceleration data that is less than the preset angular velocity threshold is determined, which is recorded as n. equal This index is used to characterize the degree to which a vehicle travels at a constant speed. equal The larger the value is, the higher the vehicle's uniform speed is; n equal The smaller the value, the lower the degree of uniform speed of the vehicle. Determine the angular velocity integral value of the vehicle angular velocity data in the preset time window, recorded as I w , this index is used to characterize the degree of change in road slope. w The higher it is, the greater the degree of change in road slope; w The lower it is, the smaller the road slope change is.

[0109] Using the trapezoidal membership function, we establish these two variables n equal and I w The fuzzy subsets of the observation noise variance R and process noise covariance Q are established using the triangular membership function. Based on actual test experience, the fuzzy logic rules of the two subsets are given to adapt different R and Q values ​​for different working conditions. For example, when the vehicle is traveling at a constant speed (n equal High), trust the acceleration observation value and reduce the observation noise variance R; when the road surface is undulating violently (I w When the value is high, the gyroscope prediction value is trusted, and the observation noise variance R is increased; the same is true for Q.

[0110] According to the angular velocity slope prediction value θ pre,k and the predicted acceleration slope θ obs,k , based on the target gain K, determine the target road slope value θ of the target vehicle at the current time node k k , the specific implementation method is as follows.

[0111] θ k =(1-K)θ pre,k +Kθ obs,k ;

[0112] This embodiment of the technical solution determines the target gain for the target vehicle at the current time point. Based on the predicted angular velocity gradient and acceleration gradient, the target road slope value for the target vehicle at the current time point is determined based on the target gain. By incorporating the target gain, or Kalman filter gain, into the road slope estimation process, the accuracy of the target road slope estimation is improved.

[0113] Example 4

[0114] Figure 4 This is a schematic diagram of the structure of a vehicle road surface slope prediction device provided by the fourth embodiment of the present invention. The vehicle road surface slope prediction device provided by the embodiment of the present invention is applicable to the situation where the road surface slope is predicted during the driving process of the vehicle. The vehicle road surface slope prediction device can be implemented in the form of hardware and / or software, such as Figure 4 As shown, the device specifically includes: a target angular velocity determination module 401, an angular velocity gradient prediction module 402, a target acceleration determination module 403, an acceleration gradient prediction module 404 and a road surface gradient value prediction module 405.

[0115] in,

[0116] The target angular velocity determination module 401 is used to determine the target angular velocity data of the target vehicle at the current time node;

[0117] An angular velocity gradient prediction module 402 is configured to obtain a historical road gradient value at a previous time node and determine an angular velocity gradient prediction value based on the historical road gradient value and the target angular velocity data;

[0118] A target acceleration determination module 403 is configured to determine target acceleration data of the target vehicle at a current time node based on the angular velocity gradient prediction value;

[0119] An acceleration gradient prediction module 404 is configured to determine an acceleration gradient prediction value based on the target acceleration data;

[0120] The road surface gradient value prediction module 405 is configured to determine a target road surface gradient value of the target vehicle at the current time node based on the acceleration gradient prediction value and the angular velocity gradient prediction value.

[0121] The technical solution of the embodiment of the present invention determines the target angular velocity data of the target vehicle at the current time node, and determines the angular velocity slope prediction value based on the historical road slope value and the target angular velocity data; determines the target acceleration data of the target vehicle at the current time node based on the angular velocity slope prediction value; determines the acceleration slope prediction value based on the target acceleration data; and determines the target road slope value of the target vehicle at the current time node based on the acceleration slope prediction value and the angular velocity slope prediction value. In the process of estimating the target road slope value of the target vehicle, the above technical solution comprehensively considers the acceleration sensor data and the angular velocity sensor data, and uses the acceleration data fusion method of the two sensors to perform slope estimation, thereby improving the accuracy of road slope estimation and avoiding the problem of low accuracy of short-term or long-term estimation results in the process of using a single sensor for slope estimation.

[0122] Optionally, the target angular velocity determination module 401 is specifically configured to:

[0123] Obtaining sensor angular velocity data of the target vehicle at the current time node; and

[0124] Acquiring reference angular velocity data of the target vehicle within a preset time period after power-on, and determining an angular velocity zero drift estimation value based on the reference angular velocity data;

[0125] The target angular velocity data of the target vehicle at the current time node is determined according to the sensor angular velocity data and the angular velocity zero drift estimation value.

[0126] Optionally, the target acceleration determination module 403 includes:

[0127] A speed data acquisition unit, configured to acquire sensor acceleration data and current vehicle speed data of the target vehicle at a current time node;

[0128] a longitudinal acceleration determination unit, configured to determine longitudinal acceleration data based on the current vehicle speed data;

[0129] a reference acceleration determining unit, configured to determine reference acceleration data based on the sensor acceleration data and the longitudinal acceleration data;

[0130] an acceleration error interval determining unit, configured to determine an acceleration error range interval based on the angular velocity gradient prediction value;

[0131] The target acceleration determination unit is configured to determine target acceleration data according to the reference acceleration data and based on the acceleration error range.

[0132] Optionally, the acceleration error interval determination unit is specifically configured to:

[0133] Obtaining an angular velocity interval upper limit value and an angular velocity interval lower limit value of an angular velocity error range, and obtaining a slope estimation error at the previous time node;

[0134] Determining an acceleration interval upper limit value of an angular velocity error range interval based on the angular velocity interval upper limit value and the angular velocity gradient prediction value and the gradient estimation error; and

[0135] According to the angular velocity interval lower limit value and the angular velocity gradient prediction value, and based on the gradient estimation error, the acceleration interval lower limit value of the angular velocity error range is determined.

[0136] Optionally, the target acceleration determination unit is specifically configured to:

[0137] If the reference acceleration data is within the acceleration error range, determining the reference acceleration data as the target acceleration data;

[0138] If the reference acceleration data is not within the acceleration error range, determining a first difference between the reference acceleration data and an upper limit of the acceleration range, and a second difference between the reference acceleration data and a lower limit of the acceleration range;

[0139] If the first difference is greater than the second difference, determining the lower limit of the acceleration range as the target acceleration data;

[0140] If the first difference is not greater than the second difference, the upper limit of the acceleration interval is determined as the target acceleration data.

[0141] Optionally, the road slope value prediction module 405 includes:

[0142] A target gain determination unit, configured to determine a target gain of the target vehicle at a current time node;

[0143] The slope value prediction unit is used to determine the target road surface slope value of the target vehicle at the current time node based on the target gain according to the angular velocity slope prediction value and the acceleration slope prediction value.

[0144] Optionally, the target gain determination unit includes:

[0145] a relevant data determination subunit, configured to determine vehicle acceleration data and vehicle angular velocity data of the target vehicle within a preset time window;

[0146] a construction subunit, configured to construct an observation noise covariance and a process noise covariance based on the vehicle angular velocity data and the vehicle angular velocity data;

[0147] The target gain determination subunit is used to obtain the historical error covariance at the previous time node, and determine the target gain of the target vehicle at the current time node based on the observation noise covariance, the process noise covariance and the historical error covariance.

[0148] Optionally, construct subunits, specifically for:

[0149] Determining the number of acceleration data in the vehicle acceleration data that is less than a preset acceleration threshold; and

[0150] Determining an angular velocity integral value of the vehicle angular velocity data within the preset time window;

[0151] An observation noise covariance and a process noise covariance are constructed according to the acceleration data quantity and the angular velocity integral value.

[0152] The vehicle road surface slope prediction device provided in the embodiment of the present invention can execute the vehicle road surface slope prediction method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0153] Example 5

[0154] Figure 5 A schematic diagram of the structure of an electronic device 50 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0155] like Figure 5As shown, the electronic device 50 includes at least one processor 51 and a memory, such as a read-only memory (ROM) 52, a random access memory (RAM) 53, etc., which is communicatively connected to the at least one processor 51. The memory stores a computer program that can be executed by the at least one processor. The processor 51 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 52 or the computer program loaded from the storage unit 58 into the random access memory (RAM) 53. Various programs and data required for the operation of the electronic device 50 can also be stored in the RAM 53. The processor 51, ROM 52, and RAM 53 are connected to each other via a bus 54. An input / output (I / O) interface 55 is also connected to the bus 54.

[0156] Multiple components in the electronic device 50 are connected to the I / O interface 55, including an input unit 56, such as a keyboard, a mouse, etc.; an output unit 57, such as various types of displays, speakers, etc.; a storage unit 58, such as a magnetic disk, an optical disk, etc.; and a communication unit 59, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 59 allows the electronic device 50 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0157] The processor 51 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 51 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any other suitable processor, controller, microcontroller, etc. The processor 51 executes the various methods and processes described above, such as the method for predicting the slope of a vehicle's road surface.

[0158] In some embodiments, the vehicle driving road surface slope prediction method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 58. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 50 via the ROM 52 and / or the communication unit 59. When the computer program is loaded into the RAM 53 and executed by the processor 51, one or more steps of the vehicle driving road surface slope prediction method described above can be performed. Alternatively, in other embodiments, the processor 51 can be configured to execute the vehicle driving road surface slope prediction method in any other appropriate manner (for example, by means of firmware).

[0159] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0160] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0161] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0162] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0163] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0164] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0165] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0166] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for predicting the slope of a vehicle road surface, characterized in that: include: Determine the target angular velocity data of the target vehicle at the current time node; Obtaining a historical road surface slope value at a previous time node, and determining an angular velocity slope prediction value based on the historical road surface slope value and the target angular velocity data; Obtaining sensor acceleration data and current vehicle speed data of the target vehicle at the current time node; determining longitudinal acceleration data according to the current vehicle speed data; determining reference acceleration data according to the sensor acceleration data and the longitudinal acceleration data; Obtaining an angular velocity interval upper limit value and an angular velocity interval lower limit value of an angular velocity error range, and obtaining a slope estimation error at the previous time node; Determining an acceleration interval upper limit value of an angular velocity error range interval according to the angular velocity interval upper limit value and the angular velocity gradient prediction value and based on the gradient estimation error; as well as, Determining an acceleration interval lower limit value of an angular velocity error range interval based on the angular velocity interval lower limit value and the angular velocity gradient prediction value and the gradient estimation error; If the reference acceleration data is within the acceleration error range, determining the reference acceleration data as the target acceleration data; If the reference acceleration data is not within the acceleration error range, determining a first difference between the reference acceleration data and an upper limit of the acceleration range, and a second difference between the reference acceleration data and a lower limit of the acceleration range; If the first difference is greater than the second difference, determining the lower limit of the acceleration range as the target acceleration data; If the first difference is not greater than the second difference, determining the upper limit of the acceleration interval as the target acceleration data; Determining an acceleration gradient prediction value according to the target acceleration data; A target road surface slope value of the target vehicle at the current time node is determined according to the acceleration slope prediction value and the angular velocity slope prediction value.

2. The method according to claim 1, characterized in that Determining target angular velocity data of the target vehicle at the current time node includes: Obtaining sensor angular velocity data of the target vehicle at the current time node; and Acquiring reference angular velocity data of the target vehicle within a preset time period after power-on, and determining an angular velocity zero drift estimation value based on the reference angular velocity data; The target angular velocity data of the target vehicle at the current time node is determined according to the sensor angular velocity data and the angular velocity zero drift estimation value.

3. The method according to claim 1, characterized in that Determining a target road surface slope value of the target vehicle at the current time node based on the acceleration slope prediction value and the angular velocity slope prediction value includes: Determining a target gain of the target vehicle at a current time node; According to the angular velocity gradient prediction value and the acceleration gradient prediction value, and based on the target gain, a target road surface gradient value of the target vehicle at the current time node is determined.

4. The method according to claim 3, characterized in that Determining the target gain of the target vehicle at the current time node includes: Determining vehicle acceleration data and vehicle angular velocity data of the target vehicle within a preset time window; constructing an observation noise covariance and a process noise covariance based on the vehicle acceleration data and the vehicle angular velocity data; The historical error covariance at the previous time node is obtained, and the target gain of the target vehicle at the current time node is determined according to the observation noise covariance, the process noise covariance, and the historical error covariance.

5. The method according to claim 4, characterized in that The constructing of the observation noise covariance and the process noise covariance according to the vehicle acceleration data and the vehicle angular velocity data includes: Determining the number of acceleration data in the vehicle acceleration data that is less than a preset acceleration threshold; and Determining an angular velocity integral value of the vehicle angular velocity data within the preset time window; An observation noise covariance and a process noise covariance are constructed according to the acceleration data quantity and the angular velocity integral value.

6. A device for predicting the slope of a road surface on which a vehicle is traveling, characterized in that: include: A target angular velocity determination module is used to determine the target angular velocity data of the target vehicle at the current time node; An angular velocity gradient prediction module is configured to obtain a historical road surface gradient value at a previous time node and determine an angular velocity gradient prediction value based on the historical road surface gradient value and the target angular velocity data; a target acceleration determination module, configured to determine target acceleration data of the target vehicle at a current time node based on the angular velocity gradient prediction value; An acceleration gradient prediction module, configured to determine an acceleration gradient prediction value based on the target acceleration data; a road surface gradient value prediction module, configured to determine a target road surface gradient value of the target vehicle at the current time node based on the acceleration gradient prediction value and the angular velocity gradient prediction value; Wherein, the target acceleration determination module includes: A speed data acquisition unit, configured to acquire sensor acceleration data and current vehicle speed data of the target vehicle at a current time node; a longitudinal acceleration determination unit, configured to determine longitudinal acceleration data based on the current vehicle speed data; a reference acceleration determining unit, configured to determine reference acceleration data based on the sensor acceleration data and the longitudinal acceleration data; an acceleration error interval determining unit, configured to determine an acceleration error range interval based on the angular velocity gradient prediction value; a target acceleration determination unit, configured to determine target acceleration data according to the reference acceleration data and based on the acceleration error range; The acceleration error interval determination unit is specifically used to: Obtaining an angular velocity interval upper limit value and an angular velocity interval lower limit value of an angular velocity error range, and obtaining a slope estimation error at the previous time node; Determining an acceleration interval upper limit value of an angular velocity error range interval based on the angular velocity interval upper limit value and the angular velocity gradient prediction value and the gradient estimation error; and Determining an acceleration interval lower limit value of an angular velocity error range interval based on the angular velocity interval lower limit value and the angular velocity gradient prediction value and the gradient estimation error; The target acceleration determination unit is specifically used to: If the reference acceleration data is within the acceleration error range, determining the reference acceleration data as the target acceleration data; If the reference acceleration data is not within the acceleration error range, determining a first difference between the reference acceleration data and an upper limit of the acceleration range, and a second difference between the reference acceleration data and a lower limit of the acceleration range; If the first difference is greater than the second difference, determining the lower limit of the acceleration range as the target acceleration data; If the first difference is not greater than the second difference, the upper limit of the acceleration interval is determined as the target acceleration data.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the vehicle driving road slope prediction method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle driving road surface slope prediction method according to any one of claims 1 to 5 when executed.

9. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, implements the method for predicting the slope of a vehicle driving road according to any one of claims 1 to 5.

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