Road slope estimation method, electronic device, vehicle, and computer program product

Abstract

The present disclosure provides a road slope estimation method, an electronic device, a vehicle and a computer program product. The road slope estimation method comprises: obtaining driving data of a vehicle; resampling the driving data to determine target driving data with a preset driving distance as a sampling interval; constructing a target function comprising a smoothing cost, a fitting cost and a reference cost for the target driving data, the smoothing cost being used to constrain the continuity of the slope change between adjacent sampling points, the fitting cost being used to represent the degree of coincidence between the estimated road slope and the longitudinal dynamics characteristics of the vehicle, and the reference cost being used to constrain the deviation between the estimated road slope and a reference slope; and solving the target function under the constraint of the slope value range to obtain the road slope estimation value of each sampling point in the target driving data.

Description

Technical Field

[0001] This disclosure relates to a road slope estimation method, electronic equipment, vehicles, and computer program products. Background Technology

[0002] Road slope estimation typically employs either surveying or least squares estimation, but both have limitations. While surveying yields accurate results, it is costly and inefficient in updating, making it unsuitable for scenarios where road conditions are prone to change, such as mining truck transport roads or natural disaster relief efforts. Although least squares estimation is less expensive, it relies on local linear approximation and assumes that the slope remains constant within a single estimation period, leading to larger estimation errors. Summary of the Invention

[0003] This disclosure provides a road slope estimation method, electronic equipment, vehicle, and computer program product.

[0004] According to one aspect of this disclosure, a road slope estimation method is provided, comprising: acquiring vehicle driving data, wherein the driving data is time series data sampled at a preset time period; resampling the driving data with a preset driving distance as the sampling interval to determine target driving data; constructing an objective function for the target driving data, comprising a smoothing cost, a fitting cost, and a reference cost, wherein the smoothing cost is used to constrain the continuity of slope changes between adjacent sampling points, the fitting cost is used to characterize the degree of agreement between the estimated road slope and the longitudinal dynamic characteristics of the vehicle, and the reference cost is used to constrain the deviation between the estimated road slope and the reference slope; and solving the objective function under the constraint of a slope value range to obtain an estimated road slope value for each sampling point in the target driving data.

[0005] The road slope estimation method disclosed herein effectively overcomes the shortcomings of existing surveying methods, such as high cost and low efficiency, and the large estimation error caused by the assumption of local equal slope in the least squares method, by resampling driving data at equal intervals and constructing an objective function including smoothing cost, fitting cost, and reference cost for global optimization. The smoothing cost constraint ensures the continuity of slope changes between adjacent sampling points, avoiding abrupt slope changes. Simultaneously, the fitting cost combined with the vehicle's longitudinal dynamic characteristics ensures that the estimation results conform to the actual working environment of the vehicle, improving the overall accuracy and reliability of road slope estimation. This provides accurate road environment information for scenarios such as convoy formation or mining transportation, thereby enhancing the safety and adaptability of vehicle control.

[0006] According to at least one embodiment of the road slope estimation method of this disclosure, the driving data is resampled, and target driving data is determined by using a preset driving distance as the sampling interval. The method includes: obtaining the timestamps and vehicle speeds corresponding to adjacent sampling points in the driving data; calculating the preset driving distance between adjacent sampling points based on the timestamps and vehicle speeds; and interpolating the driving data based on the preset driving distance to obtain the target driving data.

[0007] According to at least one embodiment of the road slope estimation method of this disclosure, before constructing an objective function including a smoothing cost, a fitting cost, and a reference cost for the target driving data, the method includes: calculating dynamic parameters for each sampling point in the target driving data based on a vehicle longitudinal dynamics model, wherein the dynamic parameters are used to construct the fitting cost.

[0008] According to at least one embodiment of the road slope estimation method of this disclosure, the dynamic parameters of each sampling point in the target driving data are calculated based on the vehicle longitudinal dynamics model, including: approximating the vehicle longitudinal dynamics model as a linear relationship; and calculating the dynamic parameters corresponding to each sampling point based on the approximated linear relationship and the target driving data.

[0009] According to at least one embodiment of the road slope estimation method of this disclosure, the process of constructing the fitting cost includes: for each sampling point in the target driving data, constructing a residual expression of the vehicle longitudinal dynamics equation based on dynamic parameters and vehicle mass; expanding the residual expression into a quadratic function of road slope, and extracting the quadratic and linear terms related to the road slope variable from the quadratic function; and summing the extracted quadratic and linear terms for each sampling point in the target driving data to determine the fitting cost.

[0010] According to at least one embodiment of the road slope estimation method of this disclosure, the process of constructing the smoothing cost includes: calculating the second difference of the road slope at the intermediate sampling point for three consecutive adjacent sampling points in the target driving data; and summing the squares of the second differences corresponding to all three consecutive adjacent sampling points in the target driving data to determine the smoothing cost.

[0011] According to at least one embodiment of the road slope estimation method of this disclosure, the process of constructing the reference cost includes: obtaining a reference slope value for each sampling point estimated based on sensor data from an on-board inertial measurement unit; calculating the deviation between the estimated road slope value for each sampling point and the reference slope value; and summing the squared values ​​of the deviations for each sampling point to determine the reference cost.

[0012] According to at least one embodiment of the road slope estimation method of this disclosure, for the target driving data, an objective function including a smoothing cost, a fitting cost, and a reference cost is constructed, including: setting corresponding smoothing weights, fitting weights, and reference weights for the smoothing cost, fitting cost, and reference cost, respectively, wherein the smoothing weight is used to adjust the degree of importance attached to road smoothness, the fitting weight is used to adjust the degree of importance attached to vehicle dynamics compliance, and the reference weight is used to adjust the degree of trust in sensor reference values; and constructing the objective function based on the weighted smoothing cost, fitting cost, and reference cost.

[0013] According to at least one embodiment of the road slope estimation method of this disclosure, the objective function is solved under the constraint of the slope value range to obtain the estimated road slope value for each sampling point in the target driving data, including: taking the slope value range as a constraint condition, transforming the objective function into a quadratic programming problem, solving the transformed objective function, and obtaining the estimated road slope value for each sampling point in the target driving data.

[0014] According to at least one embodiment of the road slope estimation method of this disclosure, the process of determining the slope value range includes: determining an initial slope value range based on general road engineering design specifications and vehicle driving safety standards; performing preliminary screening of the initial slope value range by combining prior map information or road grade attributes of the road segment to be estimated; using the current real-time driving state of the vehicle as a dynamic constraint, and narrowing the initially screened initial slope value range according to the current longitudinal acceleration, vehicle speed, and adhesion coefficient limits of the vehicle, to determine the final slope value range.

[0015] According to at least one embodiment of the road slope estimation method of this disclosure, the vehicle longitudinal dynamics model is as follows: Indicates driving force. Indicates braking force. Indicates the air drag coefficient. Indicates the windward area. Indicates speed, Indicates quality, Represents gravitational acceleration. Indicates the rolling resistance coefficient. Indicates slope, This represents the rotational mass conversion factor.

[0016] According to another aspect of this disclosure, an electronic device is provided, comprising: a memory storing execution instructions; and a processor executing the execution instructions stored in the memory, such that the processor performs a road slope estimation method according to any embodiment of this disclosure.

[0017] According to another aspect of this disclosure, a readable storage medium is provided, wherein executable instructions are stored therein, which, when executed by a processor, are used to implement the road slope estimation method of any embodiment of this disclosure.

[0018] According to another aspect of this disclosure, a vehicle is provided that includes the aforementioned electronic equipment.

[0019] According to another aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements a road slope estimation method according to any embodiment of this disclosure. Attached Figure Description

[0020] The accompanying drawings illustrate exemplary embodiments of the present disclosure and, together with the description thereof, serve to explain the principles of the present disclosure. These drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification.

[0021] Figure 1 This is a schematic diagram of the overall process of a road slope estimation method according to one embodiment of the present disclosure.

[0022] Figure 2 This is a schematic flowchart illustrating the process of obtaining target driving data according to a road slope estimation method based on an embodiment of the present disclosure.

[0023] Figure 3 This is a schematic diagram of the process for determining the fitting cost in a road slope estimation method according to one embodiment of the present disclosure.

[0024] Figure 4 This is a schematic diagram of the process for determining smoothing costs in a road slope estimation method according to one embodiment of the present disclosure.

[0025] Figure 5 This is a schematic diagram of the process for determining a reference cost in a road slope estimation method according to one embodiment of the present disclosure.

[0026] Figure 6 This is a flowchart illustrating the process of constructing an objective function in a road slope estimation method according to one embodiment of the present disclosure.

[0027] Figure 7 This is a schematic flowchart illustrating the process of determining the range of slope values ​​in a road slope estimation method according to one embodiment of the present disclosure.

[0028] Figure 8 This is a schematic diagram illustrating the relationship between the distance a vehicle travels and the slope in a road slope estimation method according to one embodiment of this disclosure.

[0029] Figure 9 This is a schematic diagram of a road slope estimation method with medium-distance sampling according to one embodiment of the present disclosure.

[0030] Figure 10 The dynamic parameters in the road slope estimation method according to one embodiment of this disclosure A schematic diagram of the curve showing the change with displacement s.

[0031] Figure 11 The dynamic parameters in the road slope estimation method according to one embodiment of this disclosure A schematic diagram of the curve showing the change with displacement s.

[0032] Figure 12 The dynamic parameters in the road slope estimation method according to one embodiment of this disclosure A schematic diagram of the curve showing the change with displacement s.

[0033] Figure 13 This is a schematic diagram comparing the optimized slope and the original slope in a road slope estimation method according to one embodiment of the present disclosure.

[0034] Figure 14 This is a schematic structural block diagram of a road slope estimation device according to one embodiment of the present disclosure.

[0035] Figure 15 This is a schematic structural block diagram of an electronic device according to one embodiment of the present disclosure. Detailed Implementation

[0036] The present disclosure will now be described in further detail with reference to the accompanying drawings and examples. It should be understood that the specific examples described herein are for illustrative purposes only and are not intended to limit the scope of the disclosure. Furthermore, it should be noted that, for ease of description, only the parts relevant to the present disclosure are shown in the accompanying drawings.

[0037] It should be noted that, where there is no conflict, the embodiments and features described in this disclosure can be combined with each other. The technical solutions of this disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0038] In convoy driving scenarios, the lead vehicle needs to provide accurate gradient references for vehicles a certain distance behind to optimize convoy energy consumption and following control. While real-time requirements are lower in this case, the gradient estimation results must reflect the overall trend of road changes rather than local instantaneous values. Furthermore, mining area transport roads suffer from continuous deterioration of road surface smoothness due to repeated compaction by heavy vehicles, or roads temporarily constructed during natural disaster relief lack prior survey data. Traditional surveying methods struggle to update quickly, and the least squares method, with its assumption of local equal gradients, cannot adapt to the actual conditions of continuously changing gradients. Consequently, the estimation results fail to support the safety and economic requirements of vehicle longitudinal control.

[0039] To address this, this disclosure proposes a road slope estimation method. This method resamples the original time-sampled driving data into an equidistant sequence and constructs a quadratic objective function that integrates smoothing cost, fitting cost, and reference cost for global optimization. While ensuring the continuity of slope changes between adjacent sampling points, it also ensures that the estimation results closely match the longitudinal dynamic characteristics of vehicles, effectively avoiding local noise interference and abrupt slope changes, thus improving the overall accuracy and reliability of road slope estimation. This provides timely and accurate road slope references for scenarios such as energy consumption optimization of convoy formations and safe scheduling of mining transport vehicles, thereby achieving a synergistic improvement in the safety, economy, and adaptability of vehicle control under dynamic and complex road conditions.

[0040] The road slope estimation method disclosed herein can be deployed independently on a cloud server, receiving driving data uploaded by multiple vehicles via vehicle-to-everything (V2X) communication, batch calculating and dynamically updating regional road slope maps, serving intelligent traffic management platforms or high-precision map service providers. Alternatively, it can be deployed independently on vehicle-side terminal devices (such as autonomous driving domain controllers or onboard ECUs), using the vehicle's sensor data to perform slope estimation in real-time or near real-time, directly supporting vehicle control functions such as adaptive cruise control, energy recovery, and hill start assist. It can also be applied to roadside edge computing units to aggregate and analyze passing vehicle data, providing slope warnings for subsequent vehicles.

[0041] Figure 1 A schematic diagram illustrating the overall flow of a road slope estimation method according to one embodiment of this disclosure is shown. Figure 1 The method M100 shown includes steps S110 to S140. This method can be executed by an onboard controller.

[0042] In step S110, the vehicle's driving data is acquired. The driving data is time-series data sampled according to a preset time period.

[0043] By periodically collecting multi-dimensional dynamic parameters generated by the vehicle during driving through onboard sensor systems or external data interfaces, a raw observation sequence that can characterize the vehicle's motion state and driving environment characteristics can be obtained.

[0044] The aforementioned driving data includes, but is not limited to, vehicle longitudinal speed, acceleration, engine torque, braking pressure, wheel speed signals, attitude angles and angular velocities output by the inertial measurement unit (IMU), and position and elevation information provided by the Global Navigation Satellite System (GNSS).

[0045] Optionally, driving data is aggregated and stored via an onboard bus (such as CAN, LIN, Ethernet) or a wireless communication module to ensure the consistency of timestamps and the stability of sampling periods.

[0046] In step S120, the driving data is resampled, with a preset driving distance as the sampling interval, to determine the target driving data.

[0047] Road gradient is essentially a spatial attribute that varies with distance. Distance domain sampling better reflects the physical laws governing gradient changes and avoids the spatial resolution inconsistencies caused by vehicle speed fluctuations in time domain sampling. The resampling process uses a preset driving distance interval (e.g., 0.25m, 0.5m, etc.) as the target sampling granularity to interpolate and transform the driving data to obtain the target driving data.

[0048] Preferably, linear interpolation, spline interpolation, or other numerical interpolation methods are used to perform interpolation transformation on the driving data.

[0049] In step S130, for the target driving data, an objective function is constructed that includes smoothing cost, fitting cost, and reference cost. The smoothing cost is used to constrain the continuity of slope changes between adjacent sampling points, the fitting cost is used to characterize the degree of agreement between the estimated road slope and the longitudinal dynamic characteristics of the vehicle, and the reference cost is used to constrain the deviation between the estimated road slope and the reference slope.

[0050] By constructing a composite objective function containing three different physical meanings of cost, multi-dimensional control of the estimated slope is achieved. Among them, the smoothing cost ensures the overall smoothness of the road slope from the perspective of geometric continuity, avoiding unreasonable jumps in the estimation results; the fitting cost ensures that the estimated slope conforms to actual physical laws from the perspective of vehicle dynamics consistency, enabling the slope estimation to explain the vehicle's motion state; and the reference cost ensures that the estimation results do not deviate too far from the sensor observations from the perspective of data reliability, maintaining the basic reliability of the road slope estimation.

[0051] Preferably, before constructing the objective function containing smoothing cost, fitting cost, and reference cost for the target driving data, the following steps are taken: based on the vehicle's longitudinal dynamics model, the dynamic parameters of each sampling point in the target driving data are calculated, and the dynamic parameters are used to construct the fitting cost. This ensures that the fitting cost can accurately characterize the degree of agreement between the estimated slope and the actual motion state of the vehicle, decoupling the dynamic data from the optimization variables, reducing the computational complexity of the optimization solution, and improving the algorithm efficiency.

[0052] In one specific embodiment, based on the vehicle's longitudinal dynamics model, the dynamic parameters of each sampling point in the target driving data are calculated, including: approximating the vehicle's longitudinal dynamics model as a linear relationship; and calculating the dynamic parameters corresponding to each sampling point based on the approximated linear relationship and the target driving data. By approximating the vehicle's longitudinal dynamics model as a linear relationship, the linearization characteristics of trigonometric functions in a small slope scenario are utilized, simplifying complex nonlinear physical constraints. Pre-calculating the dynamic parameters of each sampling point based on the approximated linear relationship achieves decoupling between time-varying driving data and slope optimization variables.

[0053] In step S140, the objective function is solved under the constraint of the slope value range to obtain the estimated road slope value for each sampling point in the target driving data.

[0054] The road slope estimation problem is transformed into a quadratic programming problem with boundary constraints. Using the road slope value at each sampling point as the optimization variable and preset upper and lower slope limits as constraints, a numerical optimization solver is invoked to minimize the objective function. During the solution process, the relationship between smoothness, dynamic fit, and reference value deviation must be balanced. The slope sequence that minimizes the total cost is iteratively calculated, and finally, the specific slope value corresponding to each equidistant sampling point, i.e., the estimated road slope value, is output, forming a complete road slope distribution curve.

[0055] Preferably, the range of slope values ​​is used as a constraint, and the objective function is transformed into a quadratic programming problem. The transformed objective function is then solved to obtain the estimated road slope value for each sampling point in the target driving data. By introducing the slope range constraint, it is ensured that the estimation result conforms to the physical limits of the actual road geometry, avoiding the calculation of abnormally steep slope values.

[0056] Therefore, the road slope estimation method disclosed in this paper effectively overcomes the shortcomings of traditional surveying methods, such as high cost and low efficiency, and the large estimation error caused by the assumption of local equal slope in the least squares method, by resampling the time-domain driving data at equal intervals and constructing a quadratic objective function that integrates smoothing cost, fitting cost, and reference cost for global optimization. The smoothing cost constraint ensures the continuity of slope changes between adjacent sampling points, avoiding abrupt slope changes. Simultaneously, the fitting cost combined with the longitudinal dynamic characteristics of the vehicle ensures that the estimation results conform to the actual working environment of the vehicle, improving the accuracy and reliability of the overall road slope estimation. Furthermore, the quadratic modeling makes the solution efficient, particularly suitable for scenarios where road conditions are easily dynamically changed, such as convoy formations and mining transportation, requiring estimation before application. It can provide accurate, smooth, and physically consistent road slope references for vehicle longitudinal control, thereby achieving a synergistic improvement in the safety, economy, and adaptability of vehicle control under dynamic and complex road conditions.

[0057] Regarding step S120, the driving data is resampled, with a preset driving distance as the sampling interval, to determine the target driving data. In some embodiments of this disclosure, this may include, for example... Figure 2 Steps S1201 to S1203 are shown.

[0058] In step S1201, the timestamps and vehicle speeds corresponding to adjacent sampling points in the driving data are obtained.

[0059] By obtaining timestamps, the stability of sampling intervals and data synchronization can be determined. Combined with vehicle speed, the cumulative travel distance between adjacent sampling points can be accurately calculated, thereby enabling interpolation and resampling of the original data at preset travel distance intervals.

[0060] In step S1202, the preset travel distance between adjacent sampling points is calculated based on the timestamp and vehicle speed.

[0061] The sampling time interval is determined based on the timestamp difference between adjacent sampling points. Combined with the longitudinal driving speed of the vehicle at the corresponding moment, the actual driving distance between adjacent sampling points is obtained by cumulative calculation.

[0062] In step S1203, the driving data is interpolated based on the preset driving distance to obtain the target driving data.

[0063] Using driving distance (e.g., 0.25m, 0.5m) as the interval benchmark, interpolation calculations are performed between adjacent sampling points of the driving data to obtain a target driving data sequence arranged at equal intervals.

[0064] Therefore, by obtaining the timestamps of adjacent sampling points and the vehicle speed and calculating the cumulative driving distance, and then interpolating and resampling the driving data based on the preset driving distance interval, the data conversion from the time domain to the distance domain is realized, which effectively avoids the problem of uneven sampling density caused by fluctuations in vehicle speed and ensures the equidistant distribution and resolution consistency of the target driving data in the spatial domain.

[0065] Regarding step S130, for the target driving data, an objective function is constructed that includes smoothing cost, fitting cost, and reference cost. In some embodiments of this disclosure, the process of constructing the fitting cost may include, for example... Figure 3 Steps S310 to S330 are shown.

[0066] In step S310, for each sampling point in the target driving data, a residual expression for the vehicle's longitudinal dynamics equation is constructed based on the dynamic parameters and vehicle mass.

[0067] In step S320, the residual expression is expanded into a quadratic function of road slope, and the quadratic and linear terms related to the road slope variable are extracted from the quadratic function.

[0068] In step S330, the sum of the quadratic and linear terms extracted from each sampling point in the target driving data is accumulated to determine the fitting cost.

[0069] Therefore, by constructing the residual expression of the vehicle's longitudinal dynamics equation and expanding it into a quadratic function of the road slope, and extracting the quadratic and linear terms related to the slope variable and summing them to determine the fitting cost, the decoupling of time-varying driving data and slope optimization variables, as well as the effective fusion of vehicle dynamics characteristics and slope optimization variables, are achieved. This ensures that the estimated road slope conforms to the actual vehicle motion state, improves the physical reliability and accuracy of the estimation results, and transforms the nonlinear dynamic constraints into a standard quadratic form, facilitating the construction of a quadratic programming problem together with the smoothing cost and reference cost.

[0070] Regarding step S130, for the target driving data, an objective function is constructed that includes a smoothing cost, a fitting cost, and a reference cost. In some embodiments of this disclosure, the process of constructing the smoothing cost may include, for example... Figure 4 Steps S410 to S420 are shown.

[0071] In step S410, for three consecutive adjacent sampling points in the target driving data, the second difference of the road slope at the intermediate sampling point is calculated.

[0072] In step S420, the squares of the second-order differences corresponding to all three consecutive adjacent sampling points in the target driving data are summed to determine the smoothing cost.

[0073] Therefore, by calculating the second-order difference of the road slope at three consecutive adjacent sampling points and summing their squared values ​​to determine the smoothing cost, the continuity of slope changes between adjacent sampling points is effectively constrained, avoiding abrupt changes in estimated slope caused by sensor noise or data fluctuations. This not only conforms to the physical law that road geometry usually changes smoothly, improving the overall smoothness and reliability of slope estimation, but also transforms the smoothing constraint into a quadratic form, making it easier to construct a quadratic programming problem together with the fitting cost and reference cost.

[0074] Regarding step S130, for the target driving data, an objective function is constructed that includes smoothing cost, fitting cost, and reference cost. In some embodiments of this disclosure, the process of constructing the reference cost may include, for example... Figure 5 Steps S510 to S530 are shown.

[0075] In step S510, the reference slope value of each sampling point is obtained based on the sensor data of the vehicle-mounted inertial measurement unit.

[0076] In step S520, the deviation between the estimated road slope value and the reference slope value for each sampling point is calculated.

[0077] In step S530, the squared values ​​of the deviations at each sampling point are summed to determine the reference cost.

[0078] Therefore, by acquiring the reference slope value estimated based on sensor data from the vehicle-mounted inertial measurement unit, and calculating the sum of the squared deviations between the estimated slope and the reference slope to determine the reference cost, the actual sensor measurement information is effectively introduced as an optimization constraint. This prevents the estimation result from deviating too far from the actual observation value due to excessive smoothing or dynamic fitting bias during the global optimization process. It suppresses the cumulative error caused by drift or noise interference from a single data source, and also transforms the reference constraint into a quadratic form, making it easy to construct a standard quadratic programming problem together with the smoothing cost and fitting cost.

[0079] Regarding step S130, for the target driving data, an objective function is constructed that includes smoothing cost, fitting cost, and reference cost. In some embodiments of this disclosure, it may include, for example... Figure 6 Steps S1301 to S1302 are shown.

[0080] In step S1301, corresponding smoothing weights, fitting weights, and reference weights are set for smoothing cost, fitting cost, and reference cost, respectively. The smoothing weight is used to adjust the degree of importance attached to road smoothness, the fitting weight is used to adjust the degree of importance attached to vehicle dynamics compliance, and the reference weight is used to adjust the degree of trust in sensor reference values.

[0081] The aforementioned smoothing weights, fitting weights, and reference weights can be dynamically configured based on sensor noise levels or road types.

[0082] In step S1302, an objective function is constructed based on the weighted smoothing cost, fitting cost, and reference cost.

[0083] Therefore, by setting smoothing weights, fitting weights, and reference weights separately, and constructing an objective function based on the weighted cost, flexible adjustment and multi-source information fusion of road geometric smoothness, vehicle dynamics consistency, and sensor observation reliability are achieved. This allows for dynamic optimization of the estimation strategy based on different application scenarios (such as sensor noise levels and differences in road surface smoothness), avoiding estimation bias caused by a single constraint.

[0084] Regarding step S140, the objective function is solved under the constraint of the slope value range to obtain the estimated road slope value for each sampling point in the target driving data. In some embodiments of this disclosure, the process of determining the slope value range may include, for example... Figure 7 Steps S710 to S730 are shown.

[0085] In step S710, the range of initial slope values ​​is determined based on the general design specifications for road engineering and vehicle driving safety standards.

[0086] Based on the limitations on maximum longitudinal slope for different road grades stipulated in the General Design Specifications for Road Engineering, and combined with the performance limits on climbing ability and braking safety in vehicle driving safety standards, the range of initial slope values ​​applicable to the current application scenario is analyzed and determined. Based on the analysis results, upper and lower limits of the slope (e.g., ±5.7 degrees) are set to ensure that the initial slope range covers most actual road conditions and conforms to physical limits.

[0087] In step S720, the range of initial slope values ​​is initially screened by combining the prior map information or road grade attributes of the road segment to be estimated.

[0088] Prior map information and road grade attributes of the road segment to be estimated are obtained through vehicle-to-everything (V2X) communication or high-precision map databases. The design maximum longitudinal slope standard corresponding to different road grades is then analyzed. The slope limit value of the specific road segment obtained by analysis is compared with the initial slope value range determined based on general design specifications. The more stringent or better matching limit between the two is selected as the upper and lower limits of the optimization constraint.

[0089] In step S730, the vehicle's current real-time driving state is used as a dynamic constraint. Based on the vehicle's current longitudinal acceleration, speed, and adhesion coefficient limits, the initial slope value range after preliminary screening is narrowed to determine the final slope value range.

[0090] The system collects real-time data on the vehicle's longitudinal acceleration, speed, and road surface adhesion coefficient limits. Based on the vehicle's longitudinal dynamics model, it calculates the maximum climbing and descending gradients the vehicle can withstand under the current power output and braking capabilities. The calculated dynamic gradient limits are then intersected with the initially selected gradient range to eliminate gradient ranges exceeding the vehicle's current driving capacity and safety thresholds, thus determining the final gradient range.

[0091] Therefore, by combining general specifications, prior maps, and real-time vehicle status through a progressive constraint selection process, a final slope value range that balances engineering compliance, scenario specificity, and dynamic safety was determined. This effectively avoids abnormal slope values ​​that violate physical principles or exceed vehicle driving capabilities during the optimization solution, ensuring the physical rationality and driving safety of the estimation results. Furthermore, by narrowing the optimization search space, the accuracy and robustness of the solution are improved, ensuring that the final output road slope curve conforms to both road geometry and the actual operating conditions of the vehicle.

[0092] In one specific embodiment, the vehicle's longitudinal dynamics model is as follows: Indicates driving force. Indicates braking force. Indicates the air drag coefficient. Indicates the windward area. Indicates speed, Indicates quality, Represents gravitational acceleration. Indicates the rolling resistance coefficient. Indicates slope, This represents the rotational mass conversion factor.

[0093] The technical solution of this disclosure will be further explained below with specific implementation and application examples.

[0094] In general, the raw driving data collected is sampled at fixed intervals. However, the road slope estimation method disclosed in this paper requires data at equal intervals, so the raw driving data needs to be resampled. Linear interpolation is generally used to obtain the required target driving data, but other interpolation methods can also be used. In addition, in order to simplify the solution process of the optimization problem (i.e., the objective function), it is necessary to calculate the dynamic parameters involved in the solution process in advance. , and The quality of the collected vehicles also needs to be determined. Finally, the solver is called to solve the optimization problem.

[0095] The longitudinal dynamics formula for a vehicle while it is in motion is: Indicates driving force. Indicates braking force. Indicates the air drag coefficient. Indicates the windward area. Indicates speed, Indicates quality, Represents gravitational acceleration. Indicates the rolling resistance coefficient. Indicates slope, This represents the rotational mass conversion factor.

[0096] Since road gradients are generally less than 10%, corresponding to 5.7 degrees, the vehicle longitudinal dynamics formula can be approximated as: Preferably, the smoothing cost is to smooth the road as a whole, avoiding abrupt changes in the calculated road slope due to data collection. The distance traveled by a vehicle is related to the slope, such as... Figure 8 The relationship is shown below. The slope relationship between the i-th sampling point and its adjacent sampling points is illustrated in the diagram. Figure 9 As shown, Figure 9 This is a schematic diagram of equidistant sampling. (For example...) Figure 9 As shown, the definition Indicates the first The longitudinal position of each sampling point Indicates the first The longitudinal position of each sampling point Indicates the first +1 sampling point longitudinal position, Indicates the first The slope of each sampling point Indicates the first The slope of each sampling point Indicates the first The slope of each sampling point.

[0097] Because it is based on equal distances traveled. Since the data sampling points selected for interpolation are equal, the x-coordinates of two adjacent data sampling points are interpolated. Therefore, the above three equations can be simplified to: choose The square of the modulus is used as the smoothing cost of the path. ,Right now Indicates the length of the fixed-distance sampling. This indicates the number of sampling points.

[0098] Preferably, the fitting cost is to make the slope conform to vehicle dynamics. Assuming n data sampling points are collected, let the dynamic parameters of the i-th sampling point be: The vehicle longitudinal dynamics formula then becomes: Therefore, the fit is good. It can be calculated using the following formula, and recorded as follows: Expanding the above equation, Extract the part related to road slope as the fitting cost. ,Right now Among them, the mass m does not change during the data collection process, so it is not merged into Ai, Bi, Ci.

[0099] Preferably, the reference cost is used to ensure that the solved slope fluctuates around a reference slope, without deviating too much. for: This represents the reference slope at the i-th sampling point, obtained through sensor data acquisition. Slope can typically be estimated using an IMU sensor.

[0100] Preferably, the overall cost of this disclosed design as follows Indicates smoothing weights, Represents the fitting weights. Indicates the reference weight.

[0101] Additionally, the optimization variable is the slope of n sampling points, and the optimization constraint is the upper and lower limits of the slope.

[0102] In one specific embodiment, the vehicle mass is 3500 kg, and the sampling interval is 0.25 m. Parameters such as Figure 10 As shown, Parameters such as Figure 11 As shown, Parameters such as Figure 12As shown. Using the road slope estimation method disclosed herein, with a smoothing weight of 100, a fitting weight of 30, a reference weight of 10, and upper and lower limits of slope of +0.1 and -0.1, the comparison curves of the optimized slope and the original slope are shown below. Figure 13 As shown in the figure, the horizontal axis s represents the driving distance, and the vertical axis alpha represents the road slope. The red dashed line represents the original slope data, which contains obvious noise and fluctuations; the black solid line represents the slope estimation result optimized by the method of this disclosure, and the curve is smoother and more continuous.

[0103] Based on any of the above embodiments, this disclosure also provides a road slope estimation device.

[0104] Figure 14 This is a schematic block diagram of the structure of a road slope estimation device according to one embodiment of the present disclosure.

[0105] like Figure 14 As shown, the road slope estimation device includes: The data acquisition module 1402 acquires the vehicle's driving data, which is time-series data sampled according to a preset time period. The resampling module 1404 resamples the driving data, using a preset driving distance as the sampling interval, to determine the target driving data. The function construction module 1406 constructs an objective function for the target driving data, which includes smoothing cost, fitting cost and reference cost. The smoothing cost is used to constrain the continuity of slope change between adjacent sampling points, the fitting cost is used to characterize the degree of agreement between the estimated road slope and the longitudinal dynamic characteristics of the vehicle, and the reference cost is used to constrain the deviation between the estimated road slope and the reference slope. The slope estimation module 1408 solves the objective function under the constraint of the slope value range, and obtains the estimated road slope value for each sampling point in the target driving data.

[0106] The aforementioned road slope estimation device can be in the form of computer software, and each module of the aforementioned road slope estimation device can be implemented through computer software modules.

[0107] The specific implementation process of the functions and roles of each module in the above-mentioned road slope estimation device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0108] This disclosure also provides an electronic device. Figure 15 A schematic diagram of the hardware implementation using the processing system is shown.

[0109] The hardware structure of electronic device 1000 can be implemented using a bus architecture. The bus architecture can include any number of interconnect buses and bridges, depending on the specific application and overall design constraints of the hardware. Bus 1100 connects various circuits including one or more processors 1200, memory 1300, and / or hardware modules. Bus 1100 can also connect various other circuits 1400 such as peripherals, voltage regulators, power management circuits, external antennas, etc. Bus 1100 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Component (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, only one connection line is used in this figure, but this does not indicate that there is only one bus or one type of bus.

[0110] For ease of explanation, certain steps of the above method are described in relation to modules. It should be understood that the corresponding module performing one or more steps of the above method may be one or more hardware modules specifically configured to perform the corresponding step, or implemented by a processor configured to perform the corresponding step, or stored in a computer-readable medium for implementation by a processor, or implemented by some combination thereof.

[0111] According to an embodiment of this application, a vehicle is also provided. The vehicle includes the aforementioned electronic equipment.

[0112] This disclosure also provides a readable storage medium storing a computer program that, when executed by a processor, is used to implement the methods described above. A "readable storage medium" can be any means capable of containing, storing, communicating, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples of a readable storage medium include: an electrical connection with one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable read-only memory (CDROM), etc.

[0113] This disclosure also provides a computer program product, the methods of which can be implemented wholly or partially through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented wholly or partially as a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed, all or part of the processes or functions of this disclosure are performed.

[0114] Computer programs or instructions can be stored in a readable storage medium or transferred from one readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The readable storage medium can be any available medium capable of access, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; an optical medium, such as a digital video optical disc; or a semiconductor medium, such as a solid-state drive. The computer-readable storage medium can be a volatile or non-volatile storage medium, or it can include both volatile and non-volatile types of storage media.

[0115] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0116] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0117] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0118] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0119] In the description of this specification, the references to terms such as "one embodiment / mode," "some embodiments / modes," "example," "specific example," or "some examples," etc., refer to specific features, structures, or characteristics described in connection with that embodiment / mode or example, which are included in at least one embodiment / mode or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment / mode or example. Moreover, the specific features, structures, or characteristics described may be combined in any suitable manner in one or more embodiments / modes or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments / modes or examples described in this specification, as well as the features of different embodiments / modes or examples.

[0120] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0121] Those skilled in the art should understand that the above embodiments are merely for illustrating the present disclosure and are not intended to limit the scope of the disclosure. Those skilled in the art can make other changes or modifications based on the above disclosure, and these changes or modifications still fall within the scope of the present disclosure.

Claims

1. A method for estimating road slope, characterized in that, include: Acquire vehicle driving data, wherein the driving data is time-series data sampled according to a preset time period; The driving data is resampled, with a preset driving distance as the sampling interval, to determine the target driving data; For the target driving data, an objective function is constructed that includes smoothing cost, fitting cost, and reference cost. The smoothing cost is used to constrain the continuity of slope changes between adjacent sampling points. The fitting cost is used to characterize the degree of agreement between the estimated road slope and the longitudinal dynamic characteristics of the vehicle. The reference cost is used to constrain the deviation between the estimated road slope and the reference slope. as well as The objective function is solved under the constraint of the slope value range to obtain the estimated road slope value for each sampling point in the target driving data.

2. The road slope estimation method as described in claim 1, characterized in that, The driving data is resampled at a preset driving distance as the sampling interval to determine the target driving data, including: Obtain the timestamps and vehicle speeds corresponding to adjacent sampling points in the driving data; The preset travel distance between adjacent sampling points is calculated based on the timestamp and vehicle speed. Based on the preset driving distance, the driving data is interpolated to obtain the target driving data.

3. The road slope estimation method as described in claim 1, characterized in that, Before constructing an objective function that includes a smoothing cost, a fitting cost, and a reference cost for the target driving data, the following steps are taken: based on the vehicle longitudinal dynamics model, the dynamic parameters of each sampling point in the target driving data are calculated, and the dynamic parameters are used to construct the fitting cost.

4. The road slope estimation method as described in claim 3, characterized in that, Based on the vehicle's longitudinal dynamics model, the dynamic parameters of each sampling point in the target driving data are calculated, including: The longitudinal dynamics model of the vehicle is approximated as a linear relationship; Based on the approximate linear relationship and the target driving data, the dynamic parameters corresponding to each sampling point are calculated.

5. The road slope estimation method as described in claim 3, characterized in that, The process of constructing the fitting cost includes: For each sampling point in the target driving data, a residual expression for the vehicle's longitudinal dynamics equation is constructed based on the dynamic parameters and vehicle mass. Expand the residual expression into a quadratic function of road slope, and extract the quadratic and linear terms related to the road slope variable from the quadratic function; The sum of the quadratic and linear terms extracted from each sampling point in the target driving data is accumulated to determine the fitting cost.

6. The road slope estimation method as described in claim 1, characterized in that, The process of constructing the smoothing cost includes: For three consecutive adjacent sampling points in the target driving data, calculate the second difference of the road slope at the intermediate sampling point; The smoothing cost is determined by summing the squares of the second-order differences corresponding to all three consecutive adjacent sampling points in the target driving data. Optionally, the process of constructing the reference cost includes: Obtain the reference slope value for each sampling point estimated based on sensor data from the vehicle-mounted inertial measurement unit; Calculate the deviation between the estimated road slope value and the reference slope value at each sampling point; The reference cost is determined by summing the squared values ​​of the deviations at each sampling point. Optionally, for the target driving data, an objective function is constructed that includes smoothing cost, fitting cost, and reference cost, including: For smoothing cost, fitting cost, and reference cost, corresponding smoothing weight, fitting weight, and reference weight are set respectively. The smoothing weight is used to adjust the importance of road smoothness, the fitting weight is used to adjust the importance of vehicle dynamics compliance, and the reference weight is used to adjust the trust level of sensor reference values. Based on the weighted smoothing cost, fitting cost, and reference cost, an objective function is constructed. Optionally, the objective function is solved under the constraint of the slope range to obtain the estimated road slope value for each sampling point in the target driving data, including: taking the slope range as a constraint, transforming the objective function into a quadratic programming problem, solving the transformed objective function, and obtaining the estimated road slope value for each sampling point in the target driving data. Optionally, the process of determining the range of slope values ​​includes: Based on general design specifications for road engineering and vehicle driving safety standards, the range of initial slope values ​​is determined. By combining prior map information or road grade attributes of the road segment to be estimated, the range of initial slope values ​​is initially screened. Using the vehicle's current real-time driving state as a dynamic constraint, the initial slope value range after preliminary screening is narrowed based on the vehicle's current longitudinal acceleration, speed, and adhesion coefficient limits, thus determining the final slope value range.

7. The road slope estimation method as described in claim 3, characterized in that, The longitudinal dynamics model of the vehicle is as follows: Indicates driving force. Indicates braking force. Indicates the air drag coefficient. Indicates the windward area. Indicates speed, Indicates quality, Represents gravitational acceleration. Indicates the rolling resistance coefficient. Indicates slope, This represents the rotational mass conversion factor.

8. An electronic device, characterized in that, include: The memory stores execution instructions; as well as A processor that executes the execution instructions stored in the memory, causing the processor to perform the road slope estimation method according to any one of claims 1 to 7.

9. A vehicle, characterized in that, The vehicle includes the electronic equipment as described in claim 8.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the road slope estimation method according to any one of claims 1 to 7.

Images