A method for jointly estimating longitudinal and lateral road slopes

Abstract

This application relates to a method for jointly estimating the longitudinal and lateral slope of a road. The method includes: acquiring vehicle displacement and wheel geometric parameters measured by multiple sensors on the vehicle; determining multiple movement parameters of the vehicle based on the vehicle displacement and wheel geometric parameters measured by the multiple sensors; and determining the road slope based on the multiple movement parameters of the vehicle and an unknown input observer. This method can improve the accuracy of road slope estimation.

Description

Technical Field

[0001] This application relates to the field of vehicle traffic safety technology, and in particular to a method for jointly estimating the longitudinal and transverse slopes of roads. Background Technology

[0002] With the development of vehicle intelligence, people have increasingly higher requirements for driving safety, leading to the emergence of systems for measuring various road data. Among these, vehicle active safety systems have become the main tools for road measurement.

[0003] Road gradient is a key measurement indicator in vehicle active safety systems, playing a crucial role in vehicle state estimation and stability assessment. Current real-time estimation methods for road gradient primarily rely on a series of parameters related to road friction and resistance between the vehicle and the road, or directly on tire characteristic parameters.

[0004] However, current methods for estimating road slope suffer from low accuracy. Summary of the Invention

[0005] Therefore, it is necessary to provide a method for jointly estimating the longitudinal and transverse slopes of roads that can improve the accuracy of road slope estimation, in order to address the aforementioned technical problems.

[0006] Firstly, this application provides a method for jointly estimating the longitudinal and transverse slopes of a road. The method includes:

[0007] Acquire vehicle displacement and wheel geometry parameters measured by multiple sensors on the vehicle;

[0008] Based on the vehicle displacement and wheel geometry parameters measured by multiple sensors, multiple movement parameters of the vehicle are determined.

[0009] The road gradient is determined based on multiple vehicle movement parameters and unknown input observers.

[0010] In one embodiment, the method further includes:

[0011] Anomaly handling is performed on multiple movement parameters of the vehicle to obtain the processed movement parameters;

[0012] Based on multiple vehicle movement parameters and unknown input observers, the road gradient is determined, including:

[0013] The road slope is determined based on the processed movement parameters and the unknown input observer.

[0014] In one embodiment, anomaly processing is performed on multiple movement parameters of the vehicle to obtain processed movement parameters, including:

[0015] Get the vehicle's current angular velocity;

[0016] Based on the angular velocity and a preset acceleration threshold, it is determined whether multiple movement parameters of the vehicle are abnormal. If abnormal, the abnormal movement parameters are removed from the multiple movement parameters to obtain the processed movement parameters. If not abnormal, the multiple movement parameters are averaged to obtain the processed movement parameters.

[0017] In one embodiment, multiple movement parameters of the vehicle are determined based on vehicle displacement and wheel geometry parameters measured by multiple sensors, including:

[0018] Based on the vehicle displacement and wheel geometry parameters measured by each sensor, determine the position parameters of each sensor;

[0019] Based on the position parameters of each sensor, multiple movement parameters of the vehicle are determined.

[0020] In one embodiment, multiple movement parameters of the vehicle are determined based on the position parameters of each sensor, including:

[0021] Based on the position parameters of each sensor, determine the normal vector of the plane where each sensor is located;

[0022] Based on the normal vector of the plane where each sensor is located, multiple movement parameters of the vehicle are determined.

[0023] In one embodiment, the road gradient is determined based on multiple movement parameters of the vehicle and an unknown input observer, including:

[0024] Filter out the target movement parameter from multiple movement parameters of the vehicle;

[0025] Determine the road slope based on the target movement parameters and unknown input observers.

[0026] In one embodiment, determining the road slope based on target movement parameters and an unknown input observer includes:

[0027] The state model of the target movement parameters is discretized to obtain the discretized state model;

[0028] The road slope is determined based on the discretized state model and the unknown input observer.

[0029] Secondly, this application also provides a device for jointly estimating the longitudinal and transverse slopes of a road.

[0030] The device includes:

[0031] The acquisition module is used to acquire vehicle displacement and wheel geometry parameters measured by multiple sensors on the vehicle.

[0032] The first determining module is used to determine multiple movement parameters of the vehicle based on the vehicle displacement and wheel geometric parameters measured by multiple sensors.

[0033] The second determination module is used to determine the road gradient based on multiple movement parameters of the vehicle and unknown input observers.

[0034] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0035] Acquire vehicle displacement and wheel geometry parameters measured by multiple sensors on the vehicle;

[0036] Based on the vehicle displacement and wheel geometry parameters measured by multiple sensors, multiple movement parameters of the vehicle are determined.

[0037] The road gradient is determined based on multiple vehicle movement parameters and unknown input observers.

[0038] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0039] Acquire vehicle displacement and wheel geometry parameters measured by multiple sensors on the vehicle;

[0040] Based on the vehicle displacement and wheel geometry parameters measured by multiple sensors, multiple movement parameters of the vehicle are determined.

[0041] The road gradient is determined based on multiple vehicle movement parameters and unknown input observers.

[0042] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0043] Acquire vehicle displacement and wheel geometry parameters measured by multiple sensors on the vehicle;

[0044] Based on the vehicle displacement and wheel geometry parameters measured by multiple sensors, multiple movement parameters of the vehicle are determined.

[0045] The road gradient is determined based on multiple vehicle movement parameters and unknown input observers.

[0046] The aforementioned method for jointly estimating the longitudinal and lateral slope of a road first acquires vehicle displacement and wheel geometric parameters measured by multiple sensors on the vehicle. Then, based on these sensor measurements, multiple vehicle movement parameters are determined. Finally, the road slope is determined using these movement parameters and an unknown input observer. This method fully considers the influence of measured displacement data and wheel geometric parameters on the vehicle's movement parameters, thereby determining the road slope through these parameters. Compared to existing technologies, this approach eliminates the need to estimate friction and tire parameters, directly determining the road slope from measured displacement data, effectively improving the accuracy of road slope estimation. Furthermore, to determine the road slope at the current moment, it is only necessary to acquire the vehicle displacement from each sensor within the current time period and perform calculations on these displacements, thus improving the real-time performance of road slope estimation. Attached Figure Description

[0047] Figure 1 This is a diagram illustrating the application environment of the joint estimation method for longitudinal and transverse slopes of a road in one embodiment.

[0048] Figure 2 This is a flowchart illustrating the method for jointly estimating the longitudinal and transverse slopes of a road in one embodiment;

[0049] Figure 3 This is a flowchart illustrating the joint estimation method for longitudinal and transverse slopes of a road in another embodiment;

[0050] Figure 4 for Figure 3 A flowchart illustrating step S204 in the embodiment;

[0051] Figure 5 for Figure 2 A flowchart illustrating step S202 in the embodiment;

[0052] Figure 6 This is a schematic diagram showing the sensor's installation location;

[0053] Figure 7 for Figure 5 A flowchart illustrating step S302 in the embodiment;

[0054] Figure 8 for Figure 2 A flowchart illustrating step S203 in the embodiment;

[0055] Figure 9 for Figure 8 A flowchart illustrating step S401 in the embodiment;

[0056] Figure 10 This is a schematic diagram of a vehicle tilt model;

[0057] Figure 11 This is a schematic diagram of the vehicle's pitch model;

[0058] Figure 12 A flowchart illustrating the process of road slope estimation;

[0059] Figure 13 This is a flowchart illustrating the joint estimation method for longitudinal and transverse slopes of a road in another embodiment;

[0060] Figure 14 This is a structural block diagram of a road longitudinal slope and transverse slope joint estimation device in one embodiment;

[0061] Figure 15 This is a structural block diagram of a road longitudinal slope and transverse slope joint estimation device in one embodiment;

[0062] Figure 16 This is a structural block diagram of a road longitudinal slope and transverse slope joint estimation device in one embodiment;

[0063] Figure 17 This is a structural block diagram of a road longitudinal slope and transverse slope joint estimation device in one embodiment;

[0064] Figure 18 This is a structural block diagram of a road longitudinal slope and transverse slope joint estimation device in one embodiment. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0066] With the development of vehicle intelligence, people have increasingly higher requirements for driving safety, leading to the emergence of many measurement systems for various road data. Among them, vehicle active safety systems have become the main tool for road measurement. A key measurement indicator in vehicle active safety systems is road gradient, which includes longitudinal and lateral slopes and plays a crucial role in vehicle state estimation and stability assessment.

[0067] In recent years, research on road slope estimation has mainly focused on longitudinal slope estimation, with commonly used methods including: estimation methods based on longitudinal dynamics, estimation methods based on tire dynamics models, and indirect estimation methods. These three types of methods estimate the longitudinal slope based on a series of parameters related to road friction and resistance between the vehicle and the road, or directly based on the vehicle's tire characteristic parameters. However, research on lateral slope estimation is relatively limited, typically involving tire lateral force estimation. If the lateral force estimation is inaccurate or contains errors, these errors will accumulate in the lateral slope estimation results, leading to low accuracy. This application aims to address this problem.

[0068] Having described the background technology of the road longitudinal and transverse slope joint estimation method provided in this application embodiment, the following is a brief description of the implementation environment involved in the road longitudinal and transverse slope joint estimation method provided in this application embodiment. The mechanical state determination method provided in this application embodiment can be applied to, for example... Figure 1 The computer device shown includes a processor and a memory connected via a system bus. The memory stores a computer program, and the processor executes the computer program to perform the steps of the method embodiments described below. Optionally, the computer device may also include an input / output interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used to communicate with external terminals via a network connection. Optionally, the computer device may be a server, a personal computer, a personal digital assistant, or other terminal devices, such as tablet computers, mobile phones, etc., or it may be a cloud or remote server. This application embodiment does not limit the specific form of the computer device.

[0069] Having described the application scenarios of the joint estimation method for road longitudinal slope and transverse slope provided in the embodiments of this application, the following focuses on the joint estimation method for road longitudinal slope and transverse slope described in this application.

[0070] In one embodiment, such as Figure 2 As shown, a method for jointly estimating the longitudinal and transverse slopes of a road is provided, and this method is applied to... Figure 1 Taking a computer device as an example, the explanation includes the following steps:

[0071] S201. Obtain vehicle displacement and wheel geometry parameters measured by multiple sensors on the vehicle.

[0072] The sensor can be a suspension height sensor or a displacement sensor (linear sensor), used to measure the vehicle's displacement over a certain period of time during movement, or to measure the displacement at a specific moment in real time. In this embodiment, the following structure can be constructed based on the road plane, the plane on which the vehicle frame is located, and the planes on which the four wheels are located. Figure 6 The cuboid shown has four wheels positioned at its four corners closest to the ground, and sensors positioned at its four corners away from the ground. In a real-world environment, these sensors are mounted at the connection between the vehicle's suspension and frame. It should be noted that the number of sensors can be either four or eight. Figure 6 The image shown is just an example and does not indicate that the number of sensors installed is fixed, or that the sensors measure the vehicle displacement simultaneously.

[0073] The geometric parameters of the wheels may include, but are not limited to, the distance between the two front wheels of the vehicle, the distance between the two rear wheels of the vehicle, and the distance between the four wheels of the vehicle.

[0074] In this embodiment, the sensor can measure the vehicle's displacement once at the same time interval, or it can measure the vehicle's displacement once after receiving a measurement command sent by the vehicle or the user terminal, or it can measure the vehicle's displacement once at random time intervals. When the computer device needs to evaluate the slope of the road where the vehicle is located at a certain moment, it can directly obtain the vehicle displacement measured by each sensor at the current moment, and obtain the geometric parameters of the vehicle's wheels according to the type of the vehicle.

[0075] S202. Based on the vehicle displacement and wheel geometry parameters measured by multiple sensors, determine multiple movement parameters of the vehicle.

[0076] The vehicle's movement parameters may include, but are not limited to, the vehicle's roll angle, pitch angle, rate of change of roll angle, and rate of change of pitch angle.

[0077] In this embodiment of the application, the computer device can perform mathematical operations on the vehicle displacement and wheel geometric parameters measured by multiple sensors to obtain the results of each mathematical operation, which are the multiple movement parameters of the vehicle; optionally, the computer device can also input the vehicle displacement and wheel geometric parameters measured by multiple sensors into a preset displacement model for calculation to obtain the results of each calculation, which are the multiple movement parameters of the vehicle.

[0078] S203. Determine the road gradient based on multiple vehicle movement parameters and unknown input observers.

[0079] The Unknown Input Observer (UIO) is an observer that can guarantee the state estimation error tends to zero even in the presence of unknown perturbations. The UIO is used to evaluate the road gradient of the road where the vehicle is located based on the vehicle's movement parameters. The road gradient includes the lateral slope and / or the longitudinal slope.

[0080] In this embodiment, the computer device can first average multiple movement parameters of the vehicle to obtain the averaged result, and then use an unknown input observer to estimate the road slope based on the averaged result to obtain the road slope. Optionally, the computer device can also input multiple movement parameters of the vehicle into the unknown input observer to estimate the road slope to obtain the road slope. Optionally, the computer device can also first sum the multiple movement parameters of the vehicle to obtain the summed result, and then input the result into the unknown input observer to estimate the road slope to obtain the road slope.

[0081] The road longitudinal and lateral slope joint estimation method provided in this application first acquires vehicle displacement and wheel geometric parameters measured by multiple sensors on the vehicle. Then, based on the vehicle displacement and wheel geometric parameters measured by multiple sensors, multiple vehicle movement parameters are determined. Finally, based on the multiple vehicle movement parameters and an unknown input observer, the road slope is determined. This method fully considers the influence of measured displacement data and wheel geometric parameters on the vehicle's movement parameters, and then determines the road slope through these movement parameters. Compared with existing technologies, this solution does not require estimation of friction parameters or tire parameters; it directly determines the road slope through measured displacement data, effectively improving the accuracy of road slope estimation. Furthermore, to determine the road slope at the current moment, it is only necessary to acquire the vehicle displacement of each sensor within the current time period and estimate based on the vehicle displacement of each sensor, thereby improving the real-time performance of road slope estimation.

[0082] In one embodiment, in Figure 2 Based on the illustrated embodiments, as Figure 3 As shown, the above method also includes:

[0083] S204. Perform anomaly processing on multiple movement parameters of the vehicle to obtain the processed movement parameters.

[0084] In this embodiment, the computer device can first preprocess multiple movement parameters of the vehicle to obtain a preprocessing result, and then compare the preprocessing result with each movement parameter of the vehicle to obtain a comparison result. If any comparison result is greater than a preset threshold, the movement parameter of the vehicle corresponding to the comparison result is deleted to obtain the processed movement parameter. For example, the multiple movement parameters of the vehicle can first be averaged to obtain an averaged result, and the averaged result can be compared with each movement parameter of the vehicle to obtain a comparison result. If any comparison result is greater than a preset threshold, the movement parameter of the vehicle corresponding to the comparison result is deleted to obtain the processed movement parameter. Optionally, the computer device can also first perform variance processing on the multiple movement parameters of the vehicle to obtain a variance processing result, and then compare the variance processing result with each movement parameter of the vehicle to obtain a comparison result. If any comparison result is greater than a preset threshold, the movement parameter of the vehicle corresponding to the comparison result is deleted to obtain the processed movement parameter.

[0085] In this embodiment, residual processing can be performed on multiple movement parameters of the vehicle to obtain processed movement parameters. A method for determining the processed movement parameters is provided below.

[0086] For example, such as Figure 4 As shown, the above-mentioned S204 "performs anomaly processing on multiple movement parameters of the vehicle to obtain processed movement parameters" includes:

[0087] S2041, Obtain the current angular velocity of the vehicle.

[0088] The vehicle's current angular velocity can be measured by an Inertial Measurement Unit (IMU) sensor. The IMU sensor can measure the vehicle's current angular velocity at equal time intervals, at unequal time intervals, or when the vehicle or user terminal sends a measurement command.

[0089] In this embodiment of the application, after determining multiple movement parameters of the vehicle, the computer device can directly obtain the current angular velocity of the vehicle measured by the IMU sensor, or it can first send a measurement command to the IMU sensor, and after the IMU sensor measures the current angular velocity of the vehicle, the computer device obtains the angular velocity of the vehicle.

[0090] S2042. Based on the angular velocity and the preset acceleration threshold, determine whether multiple movement parameters of the vehicle are abnormal. If abnormal, proceed to step S2043; if not abnormal, proceed to step S2044.

[0091] The preset acceleration threshold can be determined by the static minimum value of the threshold, the longitudinal acceleration, and the lateral acceleration. The preset acceleration threshold can be expressed by the following formula (1):

[0092] T q =T sq +T eq (|a x |+|a y |) (1);

[0093] Among them, T q For the preset acceleration threshold, T sq a is the static minimum value of the threshold. x For longitudinal acceleration, a y For lateral acceleration, T eq (|a x |+|a y |) represents the longitudinal acceleration a x and lateral acceleration a y The impact on the threshold value.

[0094] In this embodiment, the computer device can first calculate the reciprocal of the time of multiple movement parameters of the vehicle to obtain the reciprocal of the time of each vehicle's movement parameter. Then, it calculates the difference between the reciprocal of the time of each vehicle's movement parameter and the current angular velocity of the vehicle obtained above. This difference is the residual of each vehicle's movement parameter. The residual of each vehicle's movement parameter is compared with a preset acceleration threshold to obtain comparison results. These comparison results include whether the residual of the vehicle's movement parameter is greater than the preset acceleration threshold and whether the residual of the vehicle's movement parameter is less than the preset acceleration threshold. If the residual of any vehicle's movement parameter is greater than the preset acceleration threshold, it indicates that the vehicle's movement parameter is abnormal. This abnormal result indicates that a certain wheel of the vehicle is disturbed or the measurement result of the suspension height sensor is abnormal. If the residuals of all vehicle's movement parameters are less than the preset acceleration threshold, it indicates that all vehicle's movement parameters are normal. This normal result indicates that the vehicle's wheels are not disturbed or the measurement results of the suspension height sensor are normal.

[0095] For example, the residuals of the movement parameters of each vehicle can be expressed by the following formulas (2) and (3):

[0096]

[0097]

[0098] in, Let be the residual pitch angle of each vehicle. The residuals of the roll angle for each vehicle. The current angular velocity is the vehicle's pitch angle. Given the vehicle's roll angle and current angular velocity, The pitch angle of each vehicle is the reciprocal of the time. The time reciprocal of the roll angle for each vehicle.

[0099] To calculate whether any side roll angle of the vehicle is abnormal, first take the reciprocal of the roll angle in time. Then, calculate the difference between the reciprocal of the roll angle in time and the current roll angle rate of the vehicle. This difference is the residual of the roll angle. Then, compare the residual with the preset acceleration threshold given by formula (1) to obtain the comparison result. If the residual of the roll angle is less than the preset acceleration threshold, it means that the roll angle data is normal and no processing is required. If the residual of the roll angle is greater than the preset acceleration threshold, it means that the roll angle data is abnormal. Delete the roll angle along with the roll angle adjacent to the roll angle.

[0100] S2043. Remove abnormal moving parameters from multiple moving parameters to obtain processed moving parameters.

[0101] In this embodiment of the application, if any moving parameter is abnormal, the weights of that moving parameter and the two moving parameters adjacent to it are set to 0. That is, the moving parameters that are not adjacent to the moving parameter are the processed moving parameters. For example, the process of performing abnormal processing on each moving parameter to obtain the abnormally processed moving parameters can be represented by the following formulas (4) and (5):

[0102]

[0103]

[0104] Where fl, fr, rl, rr represent left front, right front, left back, right back, and λ respectively. -ij As weight, For pitch angle parameters, This refers to the roll angle parameter. If the pitch angle of the right front wheel... If an abnormality occurs, adjust the pitch angle of the right rear wheel. pitch angle of the left front wheel And the pitch angle of the right front wheel weight λ -rr , λ -fl and λ -fr Setting the weight λ of the left rear wheel to 0 will reduce its weight. -rl Setting it to 1 means that the pitch angle after anomaly handling is the pitch angle of the left rear wheel.

[0105] S2044. Average the multiple moving parameters to obtain the processed moving parameters.

[0106] In this embodiment, if none of the movement parameters are abnormal, the weight of each movement parameter is set to 0.25, meaning the processed movement parameter is the average of multiple movement parameters. For example, if none of the roll angles are abnormal, the weight λ in formulas (4) and (5) is directly set to... -rr , λ -fl , λ -fr and λ -rl All are set to 0.25. The roll angle after abnormal treatment is obtained by substituting each roll angle into the above formulas (4) and (5).

[0107] In one embodiment, after obtaining the processed movement parameters, namely the processed roll angle and the processed pitch angle, the reciprocal of time is calculated for the processed roll angle and the processed pitch angle respectively to obtain the rate of change of the processed roll angle and the rate of change of the processed pitch angle. For example, the rate of change of the vehicle's roll angle and the rate of change of the vehicle's pitch angle can be obtained using the following formulas (6) and (7):

[0108]

[0109]

[0110] Where i, j = f, r, λ -ij As weight, Let be the rate of change of the vehicle's pitch angles. This represents the rate of change of each roll angle of the vehicle.

[0111] At this point, the processed movement parameters, including the vehicle's roll angle, pitch angle, rate of change of the roll angle, and rate of change of the pitch angle, have all been determined.

[0112] Correspondingly, when the computer device executes "determine the road slope based on multiple movement parameters of the vehicle and unknown input observers" in S203 above, the specific steps are as follows: determine the road slope based on the processed movement parameters and unknown input observers.

[0113] In this embodiment of the application, after the computer performs anomaly processing on multiple movement parameters of the vehicle and obtains the processed movement parameters, the computer device can optimize the model in the unknown input observer according to the processed movement parameters to obtain the optimized model, and obtain the road slope based on the optimized model; the computer device can also input the processed movement parameters into the unknown input observer for calculation to obtain the calculation result, which is the road slope.

[0114] This application provides a method for determining road slope. This method considers anomaly handling for multiple vehicle movement parameters and determines the road slope using the anomaly-handled movement parameters and an unknown input observer, effectively improving the accuracy of road slope estimation.

[0115] In one embodiment, in Figure 2 Based on the illustrated embodiment, the process of determining multiple movement parameters of a vehicle based on vehicle displacement and wheel geometric parameters measured by multiple sensors can be described, such as... Figure 5 As shown, the above-mentioned S202 "determining multiple movement parameters of the vehicle based on the vehicle displacement and wheel geometric parameters measured by multiple sensors" may include the following steps:

[0116] S301. Determine the position parameters of each sensor based on the vehicle displacement and wheel geometry parameters measured by each sensor.

[0117] The position parameters of each sensor can be the position of each sensor in a certain coordinate system.

[0118] In this embodiment of the application, before determining the position parameters of each sensor, a system can be established first, such as... Figure 6 The aforementioned Cartesian coordinate system has its origin set at the center of the vehicle directly above the ground. The vehicle's horizontal axis is used as the x-axis, the vehicle's vertical axis as the y-axis, and the direction perpendicular to the ground as the z-axis. After establishing this Cartesian coordinate system, the computer can represent the position parameters of each sensor within this system based on the vehicle displacement and wheel geometry parameters measured by the sensors.

[0119] For example, the position parameters of each sensor can be represented by the following formulas (8) and (9):

[0120] p il =[l i Tr f / 2 z il ] T (8);

[0121] p ir =[l i -Tr r / 2 z ir ] T (9);

[0122] Where i = f, r represents the front axle or the rear axle; l f and l r These are the longitudinal distances between the origin and the front and rear axles, respectively. The front and rear track widths are represented by Tr. f and Trr It means that z il This represents the vehicle displacement measured by sensors installed at the left front and left rear wheels, z. ir This indicates the vehicle displacement measured by sensors installed at the right front and right rear wheels.

[0123] For example, the position parameter of the sensor in the left front position can be represented as p fl =[l f Tr f / 2 z fl The position parameter of the sensor at the left rear position can be represented as p. rl =[l r Tr r / 2 z rl ].

[0124] S302. Determine multiple movement parameters of the vehicle based on the position parameters of each sensor.

[0125] In this embodiment, the computer device can determine multiple movement parameters of the vehicle based on the positional relationships between the position parameters of each sensor. Optionally, the computer device can first calculate the angle between the position parameters of any two sensors, and then determine the multiple movement parameters of the vehicle based on each angle. Alternatively, the computer device can first determine the normal vector of the plane where each sensor is located based on the position parameters of each sensor, and then determine the multiple movement parameters of the vehicle based on the normal vector of the plane where each sensor is located.

[0126] In this embodiment, multiple movement parameters of the vehicle can be determined based on the positional relationship between the position parameters of each sensor. A method for determining multiple movement parameters of a vehicle is provided below.

[0127] For example, such as Figure 7 As shown, the above-mentioned S302 "determines multiple movement parameters of the vehicle based on the position parameters of each sensor" includes:

[0128] S3021. Determine the normal vector of the plane where each sensor is located based on the position parameters of each sensor.

[0129] In this embodiment, the relative position vector between any two adjacent sensors can be calculated first based on the position parameters of each sensor, and then the normal vector of the plane where each sensor is located can be calculated based on the relative position vector between any two sensors.

[0130] For example, the relative position vector between any two adjacent sensors can be represented by the following formula (10):

[0131] ρ ij，mn =p mn -p ij(10)

[0132] Where, ρ ij,mn ρ represents the relative position vector between any two adjacent sensors. ij and ρ mn Each of these represents the position parameter of any one of the sensors. For example, the relative position vector ρ between the left front sensor and the left rear sensor. fl，rl =ρ fl -ρ rl The relative position vector ρ between the left front sensor and the right front sensor fl，fr =ρ fl -ρ fr .

[0133] After obtaining the relative position vectors between any two adjacent sensors, the relative position vectors between any two adjacent sensors are multiplied to obtain the normal vector of the plane containing the relative position vectors between any two adjacent sensors, which is the normal vector of the plane containing any three adjacent sensors. The normal vector of the plane containing any three adjacent sensors can be expressed by the following formula (11):

[0134] N = ρ ij，mn ×ρ ij，pq (11)

[0135] Where N is the normal vector of the plane containing any three adjacent sensors, ρ ij,mn and ρ ij,pq All represent the relative position vectors between any two adjacent sensors, ij,mn,pq∈{fl,fr,rl,rr}.

[0136] For example, the normal vectors of the planes containing the three sensors—left front, left rear, and right front—can be represented by the following formula (12):

[0137] N = ρ fl，rl ×ρ fl，fr (12)

[0138] The normal vectors of the planes containing the three sensors (left front, left rear, and right rear) can be expressed by the following formula (13):

[0139] N = ρ fl，rl ×ρ rr，rl (13)

[0140] S3022. Determine multiple movement parameters of the vehicle based on the normal vector of the plane where each sensor is located.

[0141] In this embodiment, after determining the normal vectors of the planes where each sensor is located, the computer device can determine multiple movement parameters of the vehicle based on the positional relationship between the normal vectors of the planes where the sensors are located. For example, any three of the normal vectors of the four planes can be combined first to obtain four... Where i,j=f,r, Let N represent the normal vectors of the plane containing any three sensors. -ij Given the combination of normal vectors of the planes containing any three sensors, the four pitch angle movement parameters and four roll angle movement parameters of the vehicle are determined based on the combination of normal vectors of the planes containing the three sensors, according to the following formulas (14) and (15):

[0142]

[0143]

[0144] Where i,j=f,r, Four estimated values ​​representing the vehicle's roll angle. Four estimated values ​​representing the vehicle's pitch angle. This represents an array consisting of any three roll or pitch angles. and All of these can be any roll or pitch angle.

[0145] For example, This represents the estimated right rear pitch angle of the vehicle. This represents the estimated right rear pitch angle of the vehicle.

[0146] The method for determining multiple movement parameters of a vehicle provided in this application determines the vehicle's movement parameters based on the measured displacement data and the positional relationship between the geometric parameters of the wheels, and then calculates the road slope, which can effectively improve the accuracy of road slope estimation. In addition, if it is necessary to determine the road slope of the vehicle at the current moment, it is only necessary to obtain the vehicle displacement of each sensor in the current time period and perform calculations on the vehicle displacement of each sensor, thereby improving the real-time performance of road slope estimation.

[0147] In one embodiment, in Figure 2 Based on the illustrated embodiment, the process of determining the road gradient according to multiple vehicle movement parameters and unknown input observers can be described, such as... Figure 8 As shown, the above-mentioned S203 "determining the road slope based on multiple movement parameters of the vehicle and unknown input observers" may include the following steps:

[0148] S401. Select the target movement parameter from multiple movement parameters of the vehicle.

[0149] The target movement parameter can be either the largest or the smallest of the vehicle's multiple movement parameters.

[0150] In this embodiment, after determining multiple movement parameters of a vehicle, the computer device can process the data of these parameters to select a target movement parameter. Optionally, the computer device can average the multiple movement parameters to obtain an average result, and then use this average result as the target movement parameter. Optionally, the computer device can also calculate the variance of the multiple movement parameters to obtain a variance result, and then compare each vehicle's movement parameter with the variance result to obtain a comparison result. The movement parameter of the vehicle with the smallest difference in the comparison result is then determined as the target movement parameter. Optionally, the computer device can also input the multiple movement parameters of the vehicle into a preset optimization model for selection, select the optimal movement parameter, and determine the optimal movement parameter as the target movement parameter.

[0151] S402. Determine the road slope based on the target movement parameters and the unknown input observer.

[0152] In this embodiment of the application, the computer device can first optimize the model in the unknown input observer according to the target movement parameters to obtain the optimized model, and obtain the road slope based on the optimized model; optionally, the computer device can also input the target movement parameters into the unknown input observer for calculation to obtain the calculation result, which is the road slope.

[0153] In this embodiment, the road slope can be determined based on the target movement parameters and the unknown input observer. A method for determining the road slope is provided below.

[0154] For example, such as Figure 9 As shown, the above-mentioned S401 "determine the road slope based on the target movement parameters and the unknown input observer" includes:

[0155] S4011. Discretize the state model of the target movement parameters to obtain the discretized state model.

[0156] The state model of the target movement parameter can be a model representing the relationship between the state variables, the target movement parameter, and the road slope. The state model of the target movement parameter can be expressed by the following formulas (16) and (17):

[0157]

[0158]

[0159] in, The first derivative of the pitch angle state quantity is represented by... The first derivative of the roll angle state quantity is represented by... γ v The vehicle's pitch angle. Let θ be the rate of change of the vehicle's pitch angle. v The vehicle's roll angle. A is the rate of change of the vehicle's roll angle. γ and A θ B can be expressed by the following formulas (18) and (19). γ and B θ It can be expressed by the following formulas (20) and (21):

[0160]

[0161]

[0162]

[0163]

[0164] Among them, Figure 10 , 11 In the model of the target movement parameters shown, K γ and K θ C represents pitch stiffness and roll stiffness, respectively. γ and C θ These represent pitch damping and roll damping, respectively. x and I y Let m represent the pitch moment of inertia and the roll moment of inertia, respectively. s The mass of the vehicle body, h RC and h PC These represent the distances between the pitch axis and the vehicle's center of gravity, and the roll axis and the vehicle's center of gravity, respectively.

[0165] In addition, μ γ and μ θ It can be expressed by the following formulas (22) and (23):

[0166]

[0167]

[0168] in, and V represents the first derivative of the lateral velocity and the first derivative of the longitudinal velocity, r represents the yaw rate of the vehicle, and Vx and V y The longitudinal and lateral velocities of the vehicle are represented by g and γ, respectively. v and θ v These represent the vehicle's pitch angle and roll angle, respectively. and θ r These represent the lateral slope and longitudinal slope of the road, respectively.

[0169] Based on the above formulas (16)-(23), the state model of the above target movement parameters is expressed by the state equations shown in the following formulas (24) and (25):

[0170]

[0171] y q =C q x q +D q μ q (25)

[0172] Where q∈(θ,γ), The first derivative of the state variable, y q For output, A q B q C q and D q Let x be the system state matrix. q For state variables, u q The input quantity is unknown.

[0173] In this embodiment of the application, after representing the state model of the target movement parameter as a state equation, in order to ensure the accuracy and response characteristics of the system, the state equation of the target movement parameter can be discretized to obtain the discretized state model. For example, after discretizing formulas (24) and (25), formulas (26) and (27) are obtained:

[0174]

[0175]

[0176] in, T s For discrete time, A q B q C q and D q Let be the system state matrix, q∈(θ,γ), k is a positive integer, t represents time, and τ represents the integral time coefficient.

[0177] Formulas (26) and (27) can be rewritten by iterating for L+1 time steps as shown in the expression (28) below:

[0178]

[0179] The above formula (28) can be simplified to formula (29):

[0180] y q [0:L]=O Lq x q [0]+J Lq v q [0:L] (29);

[0181] in,

[0182]

[0183]

[0184] Where L is the number of iterations, and its value is a positive integer, x q [0] represents the initial state.

[0185] The above formula (29) is the discretized state model.

[0186] S4012. Determine the road slope based on the discretized state model and the unknown input observer.

[0187] In this embodiment of the application, after obtaining the discretized state model as described above, the computer device can first construct an unknown input observer, and then input the discretized state model into the unknown input observer to obtain the road slope.

[0188] For any positive integer L, the unknown input observer can be constructed as shown in formulas (32), (33) and (34):

[0189]

[0190]

[0191] y q [k:k+1]=O Lq x q [q]+J Lq μ q [k:k+1] (34);

[0192] in, E represents the state observation at step k+1. q and F q All are observer gain matrices. Let y represent the state observation at step k. q [k:k+1] represents y q [k] and y q A column matrix consisting of [k+1] columns, y q [k] represents the output at step k, y q [k+1] represents the output at the (k+1)th step. This represents the input at step k. It should be noted that the above-mentioned unknown state observer can provide the unknown state observation. and input observations

[0193] By inputting formulas (22), (23) and (4), (5) into the above unknown input observers (32), (33) and (34), the lateral slope and longitudinal slope of the road can be obtained. The lateral slope and longitudinal slope of the road can be expressed by the following formulas (35) and (36):

[0194]

[0195]

[0196] in, and These represent the lateral and longitudinal slopes of the road at step k, respectively. and These are the estimated lateral slope and longitudinal slope of the road at step k, respectively. and Let r[k] be the first derivative of the lateral velocity and the first derivative of the longitudinal velocity of the vehicle at step k, respectively, and let r[k] be the yaw rate at step k. x [k] and V y [k] represents the vehicle's longitudinal and lateral velocities, respectively. and These are the vehicle's pitch angle and roll angle, respectively.

[0197] The method for determining road slope provided in this application fully considers the influence of measured displacement data and wheel geometry parameters on vehicle movement parameters. It then determines the road slope using movement parameters and an unknown input observer. Compared to existing technologies, this solution eliminates the need to estimate friction parameters and tire parameters, directly determining the road slope through measured displacement data, effectively improving the accuracy of road slope estimation. Furthermore, to determine the road slope at the current moment, it is only necessary to obtain the vehicle displacement from each sensor within the current time period and perform calculations on the vehicle displacement from each sensor, thereby improving the real-time performance of road slope estimation.

[0198] In summary, all the above embodiments, such as Figure 12 As shown, the vehicle displacement and wheel geometry parameters (e.g., longitudinal distance l between the origin and the front and rear axles) are first obtained from multiple sensors on the vehicle. f and l r The distance Tr between the front and rear wheels f and Tr r Then, based on the vehicle displacement measured by multiple sensors on the vehicle and the positional relationship between the wheel geometric parameters, the vehicle's roll angle is obtained. and pitch angle and the rate of change of the roll angle and the rate of change of pitch angle Then adjust the vehicle's roll angle and pitch angle and the rate of change of the roll angle and the rate of change of pitch angle The vehicle's yaw rate r, longitudinal and lateral velocities V measured by IMU sensors x and V y The distance h between the pitch axis and the vehicle's center of gravity and the roll axis and the vehicle's center of gravity. RC and h PC The vehicle's body mass m s Pitch stiffness and roll stiffness K γ and K θ The input is fed into an unknown input observer to obtain the lateral and longitudinal slopes of the road. and like Figure 13 As shown, a complete method for joint estimation of road longitudinal and transverse slopes is provided, which includes:

[0199] S10. Acquire vehicle displacement and wheel geometry parameters measured by multiple sensors on the vehicle;

[0200] S11. Determine the position parameters of each sensor based on the vehicle displacement and wheel geometric parameters measured by each sensor;

[0201] S12. Determine the normal vector of the plane where each sensor is located based on the position parameters of each sensor;

[0202] S13. Determine multiple movement parameters of the vehicle based on the normal vector of the plane where each sensor is located;

[0203] S14. Select the target movement parameter from multiple movement parameters of the vehicle;

[0204] S15. Discretize the state model of the target movement parameters to obtain the discretized state model;

[0205] S16. Determine the road slope based on the discretized state model and the unknown input observer.

[0206] The road longitudinal and lateral slope joint estimation method provided in this application first acquires vehicle displacement and wheel geometric parameters measured by multiple sensors on the vehicle. Then, based on the vehicle displacement and wheel geometric parameters measured by multiple sensors, multiple vehicle movement parameters are determined. Finally, based on the multiple vehicle movement parameters and an unknown input observer, the road slope is determined. This method fully considers the influence of measured displacement data and wheel geometric parameters on the vehicle's movement parameters, and then determines the road slope through these movement parameters. Compared with existing technologies, this solution does not require estimation of friction parameters or tire parameters; it directly determines the road slope through measured displacement data, effectively improving the accuracy of road slope estimation. Furthermore, to determine the road slope at the current moment, it is only necessary to acquire the vehicle displacement of each sensor within the current time period and estimate based on the vehicle displacement of each sensor, thereby improving the real-time performance of road slope estimation.

[0207] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0208] Based on the same inventive concept, this application also provides a device for jointly estimating the longitudinal and transverse slopes of a road to implement the aforementioned method. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the device for jointly estimating the longitudinal and transverse slopes of a road provided below can be found in the limitations of the method for jointly estimating the longitudinal and transverse slopes of a road described above, and will not be repeated here.

[0209] In one embodiment, such as Figure 14 As shown, a device for jointly estimating the longitudinal and transverse slopes of a road is provided, comprising: an acquisition module 10, a first determination module 20, and a second determination module 30, wherein:

[0210] The acquisition module 10 is used to acquire vehicle displacement and wheel geometry parameters measured by multiple sensors on the vehicle.

[0211] The first determining module 20 is used to determine multiple movement parameters of the vehicle based on the vehicle displacement and wheel geometry parameters measured by multiple sensors.

[0212] The second determining module 30 is used to determine the road slope based on multiple movement parameters of the vehicle and unknown input observers.

[0213] In one embodiment, such as Figure 15 As shown, the above-mentioned device also includes:

[0214] The processing module 40 is used to perform anomaly processing on multiple movement parameters of the vehicle to obtain the processed movement parameters.

[0215] The second determining module 30 is also used to determine the road slope based on the processed movement parameters and the unknown input observer.

[0216] In one embodiment, such as Figure 16 As shown, the processing module 40 includes: an acquisition unit 400 and a first determination unit 401, wherein:

[0217] Acquisition unit 400 is specifically used to acquire the current angular velocity of the vehicle;

[0218] The first determining unit 401 is specifically used to determine whether multiple movement parameters of the vehicle are abnormal based on the angular velocity and a preset acceleration threshold. If abnormal, the abnormal movement parameters are removed from the multiple movement parameters to obtain the processed movement parameters. If not abnormal, the average value of the multiple movement parameters is applied to obtain the processed movement parameters.

[0219] In one embodiment, such as Figure 17 As shown, the first determining module 20 includes: a second determining unit 200 and a third determining unit 201, wherein:

[0220] The second determining unit 200 is specifically used to determine the position parameters of each sensor based on the vehicle displacement and wheel geometric parameters measured by each sensor.

[0221] The third determining unit 201 is specifically used to determine multiple movement parameters of the vehicle based on the position parameters of each sensor.

[0222] In one embodiment, the third determining unit 201 is specifically used to determine the normal vector of the plane where each sensor is located based on the position parameters of each sensor; and to determine multiple movement parameters of the vehicle based on the normal vector of the plane where each sensor is located.

[0223] In one embodiment, such as Figure 18As shown, the second determining module 30 includes: a filtering unit 300 and a fourth determining unit 301, wherein:

[0224] The filtering unit 300 is specifically used to filter out the target movement parameter from multiple movement parameters of the vehicle.

[0225] The fourth determining unit 301 is specifically used to determine the road slope based on the target movement parameters and the unknown input observer.

[0226] In one embodiment, the fourth determining unit 301 is specifically used to discretize the state model of the target movement parameters to obtain a discretized state model; and to determine the road slope based on the discretized state model and the unknown input observer.

[0227] The modules in the aforementioned joint estimation device for longitudinal and transverse slope of roads can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0228] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 1 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores motion parameter data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When executed by the processor, the computer program implements a method for jointly estimating the longitudinal and lateral slopes of a road.

[0229] Those skilled in the art will understand that Figure 1 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0230] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0231] Acquire vehicle displacement and wheel geometry parameters measured by multiple sensors on the vehicle;

[0232] Based on the vehicle displacement and wheel geometry parameters measured by multiple sensors, multiple movement parameters of the vehicle are determined.

[0233] The road gradient is determined based on multiple vehicle movement parameters and unknown input observers.

[0234] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0235] Anomaly processing is performed on multiple movement parameters of the vehicle to obtain processed movement parameters;

[0236] Based on multiple vehicle movement parameters and unknown input observers, the road gradient is determined, including:

[0237] The road slope is determined based on the processed movement parameters and the unknown input observer.

[0238] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0239] Get the vehicle's current angular velocity;

[0240] Based on the angular velocity and a preset acceleration threshold, it is determined whether multiple movement parameters of the vehicle are abnormal. If abnormal, the abnormal movement parameters are removed from the multiple movement parameters to obtain the processed movement parameters. If not abnormal, the multiple movement parameters are averaged to obtain the processed movement parameters.

[0241] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0242] Based on the vehicle displacement and wheel geometry parameters measured by each sensor, determine the position parameters of each sensor;

[0243] Based on the position parameters of each sensor, multiple movement parameters of the vehicle are determined.

[0244] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0245] Based on the position parameters of each sensor, determine the normal vector of the plane where each sensor is located;

[0246] Based on the normal vector of the plane where each sensor is located, multiple movement parameters of the vehicle are determined.

[0247] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0248] Filter out the target movement parameter from multiple movement parameters of the vehicle;

[0249] Determine the road slope based on the target movement parameters and unknown input observers.

[0250] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0251] The state model of the target movement parameters is discretized to obtain the discretized state model;

[0252] The road slope is determined based on the discretized state model and the unknown input observer.

[0253] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0254] Acquire vehicle displacement and wheel geometry parameters measured by multiple sensors on the vehicle;

[0255] Based on the vehicle displacement and wheel geometry parameters measured by multiple sensors, multiple movement parameters of the vehicle are determined.

[0256] The road gradient is determined based on multiple vehicle movement parameters and unknown input observers.

[0257] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0258] Anomaly handling is performed on multiple movement parameters of the vehicle to obtain the processed movement parameters;

[0259] Based on multiple vehicle movement parameters and unknown input observers, the road gradient is determined, including:

[0260] The road slope is determined based on the processed movement parameters and the unknown input observer.

[0261] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0262] Get the vehicle's current angular velocity;

[0263] Based on the angular velocity and a preset acceleration threshold, it is determined whether multiple movement parameters of the vehicle are abnormal. If abnormal, the abnormal movement parameters are removed from the multiple movement parameters to obtain the processed movement parameters. If not abnormal, the multiple movement parameters are averaged to obtain the processed movement parameters.

[0264] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0265] Based on the vehicle displacement and wheel geometry parameters measured by each sensor, determine the position parameters of each sensor;

[0266] Based on the position parameters of each sensor, multiple movement parameters of the vehicle are determined.

[0267] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0268] Based on the position parameters of each sensor, determine the normal vector of the plane where each sensor is located;

[0269] Based on the normal vector of the plane where each sensor is located, multiple movement parameters of the vehicle are determined.

[0270] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0271] Filter out the target movement parameter from multiple movement parameters of the vehicle;

[0272] Determine the road slope based on the target movement parameters and unknown input observers.

[0273] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0274] The state model of the target movement parameters is discretized to obtain the discretized state model;

[0275] The road slope is determined based on the discretized state model and the unknown input observer.

[0276] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0277] Acquire vehicle displacement and wheel geometry parameters measured by multiple sensors on the vehicle;

[0278] Based on the vehicle displacement and wheel geometry parameters measured by multiple sensors, multiple movement parameters of the vehicle are determined.

[0279] The road gradient is determined based on multiple vehicle movement parameters and unknown input observers.

[0280] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0281] Anomaly handling is performed on multiple movement parameters of the vehicle to obtain the processed movement parameters;

[0282] Based on multiple vehicle movement parameters and unknown input observers, the road gradient is determined, including:

[0283] The road slope is determined based on the processed movement parameters and the unknown input observer.

[0284] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0285] Get the vehicle's current angular velocity;

[0286] Based on the angular velocity and a preset acceleration threshold, it is determined whether multiple movement parameters of the vehicle are abnormal. If abnormal, the abnormal movement parameters are removed from the multiple movement parameters to obtain the processed movement parameters. If not abnormal, the multiple movement parameters are averaged to obtain the processed movement parameters.

[0287] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0288] Based on the vehicle displacement and wheel geometry parameters measured by each sensor, determine the position parameters of each sensor;

[0289] Based on the position parameters of each sensor, multiple movement parameters of the vehicle are determined.

[0290] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0291] Based on the position parameters of each sensor, determine the normal vector of the plane where each sensor is located;

[0292] Based on the normal vector of the plane where each sensor is located, multiple movement parameters of the vehicle are determined.

[0293] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0294] Filter out the target movement parameter from multiple movement parameters of the vehicle;

[0295] Determine the road slope based on the target movement parameters and unknown input observers.

[0296] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0297] The state model of the target movement parameters is discretized to obtain the discretized state model;

[0298] The road slope is determined based on the discretized state model and the unknown input observer.

[0299] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0300] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0301] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for jointly estimating the longitudinal and transverse slopes of a road, characterized in that, The method includes: Acquire vehicle displacement and wheel geometry parameters measured by multiple sensors on the vehicle; The position parameters of each sensor are determined based on the vehicle displacement measured by each sensor and the wheel geometric parameters. Based on the position parameters of each sensor, determine the relative position vector between any two adjacent sensors, and based on the relative position between any two adjacent sensors, determine the normal vector of the plane in which each sensor is located; Based on the normal vector of the plane where each sensor is located, multiple movement parameters of the vehicle are determined; The road gradient is determined based on multiple movement parameters of the vehicle and unknown input observers; The position parameters of each sensor are expressed by the following formulas (1) and (2): in, and These are the longitudinal distances between the origin and the front and rear axles, respectively. The front and rear track widths are respectively represented by... and express, This indicates the vehicle displacement measured by sensors installed at the left front and left rear wheels. This indicates the vehicle displacement measured by sensors installed at the right front and right rear wheels; the origin is the center position of the vehicle directly above the ground; the front axle is the horizontal axis at the front of the vehicle, and the rear axle is the horizontal axis at the rear of the vehicle. The movement parameters include the vehicle roll angle and the vehicle pitch angle. The process for determining the vehicle roll angle and the vehicle pitch angle is shown in the following formulas (3)-(4): in, Indicates the vehicle's roll angle. Indicates the vehicle's pitch angle. , Let represent the normal vectors of the plane containing any three sensors. It is the quantity of the combination of normal vectors of the plane containing any three sensors.

2. The method according to claim 1, characterized in that, The method further includes: Anomaly processing is performed on multiple movement parameters of the vehicle to obtain processed movement parameters; The step of determining the road gradient based on multiple movement parameters of the vehicle and an unknown input observer includes: The road slope is determined based on the processed movement parameters and the unknown input observer.

3. The method according to claim 2, characterized in that, The process of performing anomaly processing on multiple movement parameters of the vehicle to obtain processed movement parameters includes: Obtain the current angular velocity of the vehicle; Based on the angular velocity and the preset acceleration threshold, it is determined whether multiple movement parameters of the vehicle are abnormal. If abnormal, the abnormal movement parameters are removed from the multiple movement parameters to obtain the processed movement parameters. If not abnormal, the multiple movement parameters are averaged to obtain the processed movement parameters.

4. The method according to claim 1, characterized in that, The step of determining the road gradient based on multiple movement parameters of the vehicle and an unknown input observer includes: Target movement parameters are selected from multiple movement parameters of the vehicle; The road slope is determined based on the target movement parameters and the unknown input observer.

5. The method according to claim 4, characterized in that, Determining the road slope based on the target movement parameters and the unknown input observer includes: The state model of the target movement parameters is discretized to obtain the discretized state model; The road slope is determined based on the discretized state model and the unknown input observer.

6. A device for jointly estimating the longitudinal and transverse slopes of a road, characterized in that, The device includes: The acquisition module is used to acquire vehicle displacement and wheel geometry parameters measured by multiple sensors on the vehicle. The first determining module is used to determine the position parameters of each sensor based on the vehicle displacement measured by each sensor and the wheel geometric parameters; determine the relative position vector between any two adjacent sensors based on the position parameters of each sensor; determine the normal vector of the plane where each sensor is located based on the relative position between any two adjacent sensors; and determine multiple movement parameters of the vehicle based on the normal vector of the plane where each sensor is located. The second determining module is used to determine the road slope based on multiple movement parameters of the vehicle and an unknown input observer; The position parameters of each sensor are expressed by the following formulas (1) and (2): in, and These are the longitudinal distances between the origin and the front and rear axles, respectively. The front and rear track widths are respectively represented by... and express, This indicates the vehicle displacement measured by sensors installed at the left front and left rear wheels. This indicates the vehicle displacement measured by sensors installed at the right front and right rear wheels; the origin is the center position of the vehicle directly above the ground; the front axle is the horizontal axis at the front of the vehicle, and the rear axle is the horizontal axis at the rear of the vehicle. The movement parameters include the vehicle roll angle and the vehicle pitch angle. The process for determining the vehicle roll angle and the vehicle pitch angle is shown in the following formulas (3)-(4): in, Indicates the vehicle's roll angle. Indicates the vehicle's pitch angle. , Let represent the normal vectors of the plane containing any three sensors. It is the quantity of the combination of normal vectors of the plane containing any three sensors.

7. The apparatus according to claim 6, characterized in that, The device further includes: The processing module is used to perform anomaly processing on multiple movement parameters of the vehicle to obtain the processed movement parameters; The second determining module is further configured to determine the road slope based on the processed movement parameters and the unknown input observer.

8. The apparatus according to claim 7, characterized in that, The exception handling module includes: The acquisition unit is specifically used to acquire the current angular velocity of the vehicle; The determining unit is specifically used to determine whether multiple movement parameters of the vehicle are abnormal based on the angular velocity and a preset acceleration threshold. If abnormal, the abnormal movement parameters are removed from the multiple movement parameters to obtain the processed movement parameters. If not abnormal, the multiple movement parameters are averaged to obtain the processed movement parameters.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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