Vehicle side slip angle calculation method and device, electronic equipment and storage medium

Abstract

The invention discloses a vehicle side slip angle calculation method and device, electronic equipment and a storage medium, and the method comprises the steps: selecting a first feature set according to the importance evaluation score of a first operation parameter of a vehicle, and carrying out the training of the first feature set to obtain a side slip angle calculation model; the vehicle lateral acceleration, the steering wheel rotation angle and the left front wheel rotation angle speed of the to-be-measured vehicle are input into the side slip angle calculation model, the side slip angle of the to-be-measured vehicle is obtained through direct calculation, and the problem that in the prior art, the side slip angle is obtained through direct integration of a vehicle-mounted sensor signal, and consequently errors are large can be solved; and the problem that in the prior art, the calculation precision of the side slip angle is not high due to the fact that a kinetic model does not conform to the actual situation can also be solved, and therefore the calculation precision of the side slip angle is better guaranteed.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent vehicle status detection, and in particular to a method and device for calculating the sideslip angle of the center of mass of a vehicle, an electronic device and a storage medium. Background Art

[0002] The center of mass slip angle is an important parameter for measuring the lateral stability of a vehicle. Accurate and real-time acquisition of this key vehicle state parameter is crucial for vehicle active safety control systems and advanced driver assistance systems. However, direct measurement of the center of mass slip angle using the global positioning system (GPS) and inertial navigation system (INS) or non-contact optical sensors faces problems such as high cost and susceptibility to environmental influences.

[0003] The current methods for calculating the vehicle's center of mass sideslip angle can be divided into two main categories: one is a method based on a kinematic model, which analyzes the vehicle's kinematic model and directly integrates the on-board sensor signal to obtain the center of mass sideslip angle. This type of method relies heavily on sensor accuracy, and long-term integration will cause errors to accumulate continuously; the other is a method based on a dynamic model. This type of method usually designs observers, such as the Romberg observer, the sliding film observer, the robust observer, etc., and performs methods such as Kalman filtering, extended Kalman filtering, and unscented Kalman filtering to obtain the center of mass sideslip angle. This type of method needs to be based on a more accurate vehicle dynamics model, and when using Kalman filtering and its variants, it is also necessary to assume that the sensor noise is Gaussian noise, which is inconsistent with the actual situation.

[0004] In summary, the existing method for calculating the sideslip angle of the vehicle center of mass has the problem of low accuracy. Summary of the invention

[0005] The main purpose of the embodiments of the present application is to propose a method and device for calculating the sideslip angle of the center of mass of a vehicle, an electronic device and a storage medium, so as to improve the calculation accuracy of the sideslip angle of the center of mass of the vehicle.

[0006] To achieve the above object, one aspect of an embodiment of the present application provides a method for calculating a vehicle center of mass sideslip angle, the method comprising:

[0007] Obtaining a first operating parameter of the vehicle;

[0008] Calculate an importance assessment score according to the first operating parameter of the vehicle;

[0009] According to the importance evaluation score and the first operating parameter of the vehicle, a first feature set is determined; wherein the first feature set includes: a vehicle lateral acceleration feature set, a steering wheel angle feature set, and a left front wheel rotation angular velocity feature set;

[0010] The preset model is trained according to the first feature set to generate a center of mass sideslip angle calculation model;

[0011] Acquire a first vehicle lateral acceleration, a first steering wheel angle, and a first left front wheel rotation angular velocity of the vehicle to be measured;

[0012] The center of mass sideslip angle of the vehicle to be measured is calculated according to the first vehicle lateral acceleration, the first steering wheel angle and the first left front wheel rotation angular velocity by using a center of mass sideslip angle calculation model.

[0013] In some embodiments, calculating the importance assessment score according to the first operating parameter of the vehicle includes:

[0014] Generate a dedicated feature set according to the first mapping relationship combined with the first operating parameter of the vehicle; wherein the first mapping relationship is a mapping relationship between the center of mass sideslip angle and the first operating parameter of the vehicle;

[0015] Calculate the Pearson correlation coefficient between each feature in the dedicated feature set;

[0016] Generate a feature set to be evaluated based on the Pearson correlation coefficient combined with the dedicated feature set;

[0017] The importance evaluation score of each feature in the feature set to be evaluated is calculated based on the random forest algorithm.

[0018] In some embodiments, determining a first feature set based on the importance evaluation score combined with a first operating parameter of the vehicle includes:

[0019] Normalizing the importance assessment scores to obtain normalized importance scores;

[0020] Sort the normalized importance scores to obtain a sorting result;

[0021] The first feature set is obtained by screening from the dedicated feature set according to the sorting result and the score threshold.

[0022] In some embodiments, generating a dedicated feature set according to the first mapping relationship in combination with the first operating parameter of the vehicle includes:

[0023] According to the first operating parameter of the vehicle, a first mapping relationship is obtained in combination with a definition formula of the center of mass sideslip angle and a vehicle dynamics equation;

[0024] According to the first mapping relationship and the first operating parameter of the vehicle, a first variable group is obtained by analysis; wherein the first variable group includes: vehicle longitudinal speed, vehicle longitudinal acceleration, vehicle lateral acceleration, vehicle yaw angular velocity, wheel drive torque, wheel rotation angular velocity and steering wheel angle;

[0025] Data is collected according to the first variable group to generate a dedicated feature set.

[0026] In some embodiments, training a preset model according to the first feature set to generate a center of mass sideslip angle calculation model includes:

[0027] Divide the first feature set into a training set, a validation set, and a test set;

[0028] Training a preset model according to a training set; wherein the preset model is a model established by a machine learning algorithm or a model established by a deep learning algorithm;

[0029] Adjust the model parameters of the preset model during the training process according to the validation set;

[0030] When the number of iterations of the model parameters meets the preset requirements, a center of mass sideslip angle calculation model is generated;

[0031] The performance of the center of mass sideslip angle calculation model is verified based on the test set.

[0032] In some embodiments, verifying the performance of the center of mass sideslip angle calculation model according to the test set includes:

[0033] Inputting the test set into the center of mass sideslip angle calculation model to generate a first calculation result;

[0034] Inputting the test set into the simulation platform so that the simulation platform calculates and obtains a second calculation result;

[0035] Calculating a mean square error and an error between the first calculation result and the second calculation result;

[0036] The performance of the center of mass sideslip angle calculation model was verified based on the mean square error and error.

[0037] In some embodiments, data collection is performed according to the first variable group to generate a dedicated feature set, including:

[0038] Obtain preset collection conditions, specific road adhesion coefficients, and specific vehicle speed ranges;

[0039] Under preset collection conditions, specific road adhesion coefficients and specific vehicle speed ranges, the first variable group is collected through a simulation platform to generate a dedicated feature set; wherein the sampling frequency of the simulation sensor in the simulation platform is 100 Hz.

[0040] To achieve the above object, another aspect of the embodiment of the present application provides a vehicle center of mass side slip angle calculation device, the device comprising:

[0041] A first acquisition module, used to acquire a first operating parameter of the vehicle;

[0042] A first calculation module, used for calculating an importance evaluation score according to a first operating parameter of the vehicle;

[0043] A second acquisition module is used to determine a first feature set according to the importance evaluation score combined with the first operating parameter of the vehicle; wherein the first feature set includes: a vehicle lateral acceleration feature set, a steering wheel angle feature set, and a left front wheel rotation angular velocity feature set;

[0044] A training module, used for training a preset model according to the first feature set to generate a center of mass sideslip angle calculation model;

[0045] A third acquisition module is used to acquire a first vehicle lateral acceleration, a first steering wheel angle, and a first left front wheel rotation angular velocity of the vehicle to be measured;

[0046] The second calculation module is used to calculate the center of mass sideslip angle of the vehicle to be measured through a center of mass sideslip angle calculation model according to the first vehicle lateral acceleration, the first steering wheel angle and the first left front wheel rotation angular velocity.

[0047] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application proposes an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the above-mentioned method for calculating the sideslip angle of the vehicle center of mass is implemented.

[0048] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned method for calculating the sideslip angle of the vehicle center of mass is implemented.

[0049] The embodiments of the present application include at least the following beneficial effects: The present application provides a method and device for calculating the side slip angle of the center of mass of a vehicle, an electronic device and a storage medium. The scheme selects a first feature set according to the importance evaluation score of the first operating parameter of the vehicle, and after the side slip angle calculation model of the center of mass is obtained through training according to the first feature set, the calculation accuracy of the trained side slip angle calculation model of the center of mass can be improved because the vehicle lateral acceleration feature set, the steering wheel angle feature set and the left front wheel rotation angular velocity feature set in the first feature set have high importance evaluation scores for the side slip angle of the center of mass. Furthermore, the side slip angle of the center of mass of the vehicle to be measured is directly calculated by combining the vehicle lateral acceleration, the steering wheel angle and the left front wheel rotation angular velocity of the vehicle to be measured with the side slip angle calculation model obtained through training, which can avoid the problem of large error caused by directly integrating the vehicle-mounted sensor signal to obtain the side slip angle in the prior art; it can also avoid the problem of low calculation accuracy of the side slip angle of the center of mass caused by the inconsistency between the dynamic model and the actual situation in the prior art, thereby better ensuring the calculation accuracy of the side slip angle of the center of mass. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a flow chart of a method for calculating a vehicle center of mass sideslip angle provided in an embodiment of the present application;

[0051] Figure 2 It is a schematic diagram of the correlation R value between each feature in the dedicated feature set;

[0052] Figure 3 It is a schematic diagram of the significance P between each feature in the dedicated feature set;

[0053] Figure 4 is a schematic diagram of normalized importance assessment scores;

[0054] Figure 5 This is a schematic diagram of the calculation results of the center of mass sideslip angle in the first scenario;

[0055] Figure 6 This is a schematic diagram of the calculation results of the center of mass sideslip angle in the second scenario;

[0056] Figure 7 is a schematic structural diagram of a vehicle center of mass sideslip angle calculation device provided in an embodiment of the present application;

[0057] Figure 8 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application, they are only examples of devices and methods consistent with some aspects of the embodiments of the present application.

[0059] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0060] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0062] In the related technology, the current methods for calculating the vehicle center of mass sideslip angle can be mainly divided into two categories: one is a method based on a kinematic model, which analyzes the vehicle kinematic model and directly integrates the on-board sensor signal to obtain the center of mass sideslip angle. This type of method relies heavily on the sensor accuracy, and long-term integration will cause the error to accumulate continuously; the other is a method based on a dynamic model. This type of method usually designs observers, such as the Romberg observer, the sliding film observer, the robust observer, etc., and performs methods such as Kalman filtering, extended Kalman filtering, and unscented Kalman filtering to obtain the center of mass sideslip angle. This type of method needs to be based on a more accurate vehicle dynamics model, and when using Kalman filtering and its variants, it is also necessary to assume that the sensor noise is Gaussian noise, which is inconsistent with the actual situation.

[0063] In view of this, the embodiment of the present application provides a method and device for calculating the side slip angle of the center of mass of a vehicle, an electronic device and a storage medium. The scheme selects a first feature set according to the importance evaluation score of the first operating parameter of the vehicle, and after the side slip angle calculation model of the center of mass is obtained through training according to the first feature set, the calculation accuracy of the trained side slip angle calculation model of the center of mass can be improved because the vehicle lateral acceleration feature set, the steering wheel angle feature set and the left front wheel rotation angular velocity feature set in the first feature set have high importance evaluation scores for the side slip angle of the center of mass. Furthermore, the side slip angle of the center of mass of the vehicle to be measured is directly calculated by combining the vehicle lateral acceleration, the steering wheel angle and the left front wheel rotation angular velocity of the vehicle to be measured with the trained side slip angle calculation model, which can avoid the problem of large error caused by directly integrating the vehicle-mounted sensor signal to obtain the side slip angle in the prior art; it can also avoid the problem of low calculation accuracy of the side slip angle of the center of mass caused by the inconsistency between the dynamic model and the actual situation in the prior art, thereby better ensuring the calculation accuracy of the side slip angle of the center of mass.

[0064] The vehicle center of mass side slip angle calculation method provided in the embodiment of the present application relates to the technical field of intelligent vehicle state measurement. The vehicle center of mass side slip angle calculation method provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a vehicle-mounted terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or it can be configured as a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements the vehicle center of mass side slip angle calculation method, etc., but is not limited to the above forms.

[0065] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the first context of computer-executable instructions executed by a computer, such as program modules. First, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0066] Figure 1 is an optional flow chart of a method for calculating the vehicle center of mass sideslip angle provided in an embodiment of the present application, Figure 1 The method may include but is not limited to steps S101 to S106.

[0067] S101: Obtain a first vehicle operating parameter.

[0068] S102: Calculate an importance assessment score based on a first operating parameter of the vehicle.

[0069] Further, the importance evaluation score is calculated according to the first operating parameter of the vehicle, including:

[0070] Generate a dedicated feature set according to the first mapping relationship combined with the first operating parameter of the vehicle; wherein the first mapping relationship is a mapping relationship between the center of mass sideslip angle and the first operating parameter of the vehicle;

[0071] Calculate the Pearson correlation coefficient between each feature in the dedicated feature set;

[0072] Generate a feature set to be evaluated based on the Pearson correlation coefficient combined with the dedicated feature set;

[0073] The importance evaluation score of each feature in the feature set to be evaluated is calculated based on the random forest algorithm.

[0074] Furthermore, a dedicated feature set is generated according to the first mapping relationship in combination with the first operating parameter of the vehicle, including:

[0075] According to the first operating parameter of the vehicle, a first mapping relationship is obtained in combination with a definition formula of the center of mass sideslip angle and a vehicle dynamics equation;

[0076] According to the first mapping relationship and the first operating parameter of the vehicle, a first variable group is obtained by analysis; wherein the first variable group includes: vehicle longitudinal speed, vehicle longitudinal acceleration, vehicle lateral acceleration, vehicle yaw angular velocity, wheel drive torque, wheel rotation angular velocity and steering wheel angle;

[0077] Data is collected according to the first variable group to generate a dedicated feature set.

[0078] In a specific embodiment, the formula for defining the center of mass sideslip angle is as follows:

[0079]

[0080] Where: β is the sideslip angle of the center of mass; v x is the vehicle longitudinal velocity, v y is the lateral speed of the vehicle.

[0081] It can be seen that the center of mass sideslip angle β mainly uses the vehicle longitudinal velocity v x and the vehicle lateral velocity v y Considering the nonlinear characteristics of the vehicle in high-speed and low-adhesion scenarios, the vehicle dynamics equation should be used to derive and calculate the vehicle longitudinal velocity v x and the vehicle lateral velocity v y The vehicle dynamics model considering the longitudinal, lateral and yaw of the vehicle body can fully describe the movement of the vehicle on the plane, so the vehicle dynamics equation is as follows:

[0082]

[0083] Among them, a is the distance from the center of mass to the front axle, b is the distance to the rear axle; m is the mass of the vehicle; a xis the longitudinal acceleration of the vehicle, a y is the vehicle lateral acceleration; γ is the yaw rate; δ is the front wheel turning angle; d f is the front wheel track; d r F is the rear wheel track; x is the tire longitudinal force; F y is the tire lateral force; ij=fl,fr,rl,rr represent the left front wheel fl, the right front wheel fr, the left rear wheel rl, and the right rear wheel rr respectively.

[0084] The front wheel steering angle δ can be calculated by the following formula:

[0085]

[0086] Among them, δ sw is the steering wheel angle; i is the steering gear ratio.

[0087] From the above, we can know that in order to further solve the vehicle longitudinal velocity v x , vehicle lateral speed v y , the vehicle longitudinal acceleration a needs to be known x 、Vehicle lateral acceleration a y , the vehicle yaw rate γ. In addition, the vehicle longitudinal acceleration a x and the vehicle lateral acceleration a y The longitudinal force F of each tire needs to be xij 、Tire lateral force F yij Calculated. Tire longitudinal force F xij 、Tire lateral force F yij The mapping can be done through the following functions:

[0088]

[0089] Where: μ is the road adhesion coefficient; F z is the tire vertical load; λ is the wheel slip / rotation rate; α is the tire side slip angle.

[0090] When the vehicle is driving, load transfer will occur due to steering, braking, etc. z Generally not equal, each tire vertical load F z The calculation is as follows:

[0091]

[0092] Where: L is the wheelbase; h g is the height of the center of mass; g is the acceleration due to gravity.

[0093] The wheel slip / turn rate λ can be obtained by the following formula:

[0094]

[0095] Where: ω is the angular velocity of the wheel; v xij is the tire center speed; R e is the effective rolling radius of the tire.

[0096] The calculation formula of tire side slip angle α is as follows:

[0097]

[0098] The tire center speed is:

[0099]

[0100] The wheel rotation angular velocity ω is also related to the driving torque and F x related:

[0101]

[0102] Where: J is the wheel moment of inertia; T d The driving torque for each wheel.

[0103] According to the analysis process of the first mapping relationship obtained by combining the mass center sideslip angle definition formula and the vehicle dynamics equation, the first vehicle operating parameters having a mapping relationship with the mass center sideslip angle include: vehicle longitudinal acceleration a x 、Vehicle lateral acceleration a y , vehicle longitudinal speed v x , vehicle lateral speed v y , vehicle yaw rate γ, steering wheel angle δ sw 、Wheel rotation angular velocity ω ij , wheel drive torque T dij and road adhesion coefficient μ.

[0104] Further, according to the first mapping relationship combined with the first operating parameter of the vehicle, the first variable group is analyzed to include: the longitudinal acceleration a of the vehicle x 、Vehicle lateral acceleration a y , vehicle longitudinal speed v x , vehicle yaw rate γ, steering wheel angle δ sw 、Wheel rotation angular velocity ω ij and wheel drive torque T dij .

[0105] In a specific embodiment, data collection is performed according to the first variable group to generate a dedicated feature set, including:

[0106] Obtain preset collection conditions, specific road adhesion coefficients, and specific vehicle speed ranges;

[0107] Under preset collection conditions, specific road adhesion coefficients and specific vehicle speed ranges, the first variable group is collected through a simulation platform to generate a dedicated feature set; wherein the sampling frequency of the simulation sensor in the simulation platform is 100 Hz.

[0108] When collecting data, considering the high nonlinearity of the system when the vehicle is driving, the established data set should include the dynamic response characteristics of the vehicle under low speed, high speed, low adhesion and high adhesion scenarios, that is, data collection is required under normal conditions and extreme conditions. Therefore, the preset collection conditions include: double lane change conditions and serpentine conditions; and the danger is extremely high in extreme conditions, so the Matlab / Simulink and Carsim joint simulation platform is used for data collection. In the data collection, the specific speed range is set to 20-100km / h, and the speed interval of each group is 10km / h; the specific road adhesion coefficients are set to 0.3, 0.5, and 0.85, respectively, representing low, medium, and high adhesion roads; the sampling frequency of each sensor in the simulation platform is 100Hz; the vehicle can complete the scenario test under various speeds and road adhesion coefficients. The specific data collection scenarios are shown in Table 1:

[0109] Table 1 Specific data collection scenarios

[0110]

[0111] The vehicle longitudinal acceleration a is collected x 、Vehicle lateral acceleration a y , vehicle longitudinal speed v x , vehicle yaw rate γ, steering wheel angle δ sw 、Wheel rotation angular velocity ω ij , wheel drive torque T dij , and the side slip angle β of the center of mass are a set of data, a total of 39 sets of data, which can fully characterize the nonlinear response of the car when it is driving. x 、Vehicle lateral acceleration a y , vehicle longitudinal speed v x , vehicle yaw rate γ, steering wheel angle δ sw 、Wheel rotation angular velocity ω ij and wheel drive torque T dij The constructed data set is the dedicated feature set.

[0112] S103: Determine a first feature set based on the importance evaluation score and a first operating parameter of the vehicle.

[0113] Among them, the first feature set includes: a vehicle lateral acceleration feature set, a steering wheel angle feature set and a left front wheel rotation angular velocity feature set.

[0114] Further, according to the importance evaluation score and the first operating parameter of the vehicle, a first feature set is determined, including:

[0115] Normalizing the importance assessment scores to obtain normalized importance scores;

[0116] Sort the normalized importance scores to obtain a sorting result;

[0117] The first feature set is obtained by screening from the dedicated feature set according to the sorting result and the score threshold.

[0118] S104: Training a preset model according to the first feature set to generate a center of mass sideslip angle calculation model.

[0119] Furthermore, the preset model is trained according to the first feature set to generate a center of mass sideslip angle calculation model, including:

[0120] Divide the first feature set into a training set, a validation set, and a test set;

[0121] Training a preset model according to a training set; wherein the preset model is a model established by a machine learning algorithm or a model established by a deep learning algorithm;

[0122] Adjust the model parameters of the preset model during the training process according to the validation set;

[0123] When the number of iterations of the model parameters meets the preset requirements, a center of mass sideslip angle calculation model is generated;

[0124] The performance of the center of mass sideslip angle calculation model is verified based on the test set.

[0125] In a specific embodiment, verifying the performance of the center of mass sideslip angle calculation model according to the test set includes:

[0126] Inputting the test set into the center of mass sideslip angle calculation model to generate a first calculation result;

[0127] Inputting the test set into the simulation platform so that the simulation platform calculates and obtains a second calculation result;

[0128] Calculating a mean square error and an error between the first calculation result and the second calculation result;

[0129] The performance of the center of mass sideslip angle calculation model was verified based on the mean square error and error.

[0130] S105: Acquire a first vehicle lateral acceleration, a first steering wheel angle, and a first left front wheel rotation angular velocity of the vehicle to be measured.

[0131] S106: Calculate the center of mass sideslip angle of the vehicle to be measured by using a center of mass sideslip angle calculation model according to the first vehicle lateral acceleration, the first steering wheel angle and the first left front wheel rotation angular velocity.

[0132] In a specific embodiment, the Pearson correlation coefficient is used to analyze the relationship between the features in the dedicated feature set, the random forest algorithm is applied to evaluate the importance of each feature in the dedicated feature set, and the important features are screened out as the first feature set.

[0133] After establishing the dedicated feature set, feature engineering analysis is performed to reduce data dimensions while ensuring the accuracy of the center of mass side slip angle estimation, thereby improving effective data utilization and algorithm performance. Therefore, two aspects of analysis are performed to analyze the relationship between the variables and the relationship between the variables and the center of mass side slip angle. The Pearson correlation coefficient between each feature in the dedicated feature set is calculated, specifically: based on the vehicle longitudinal acceleration a in the acquired dedicated feature set x 、Vehicle lateral acceleration a y , vehicle longitudinal speed v x , vehicle yaw rate γ, steering wheel angle δ sw 、Wheel rotation angular velocity ω ij and wheel drive torque T dij Pearson correlation coefficient (PCC) analysis was performed between variables, and the correlation R value and significance P value were obtained.

[0134] (1) Correlation judgment: calculate the correlation coefficient R value between each variable. The calculation formula is as follows:

[0135]

[0136] Where: N is the number of samples; X and Y are two variables respectively; are the means of the two variables respectively.

[0137] When |R|≥0.8, it can be judged that there is a strong correlation between X and Y, and X can be linearly represented by Y; further, when |R|=1, X and Y are completely correlated, one variable can be replaced by the other variable, and at this time, X and Y have fault tolerance.

[0138] (2) Significance judgment: calculate the t statistic between each variable, and then determine the significant P value by looking up the t distribution table. The t statistic calculation formula is as follows:

[0139]

[0140] When P≤0.05, it can be judged that the significance level between the two variables is high; when P>0.05, the significance level is insufficient, and it cannot be ruled out that the large correlation coefficient R value is caused by accidental factors.

[0141] Please refer to Figure 2 , Figure 2 It is a schematic diagram of the correlation R value between each feature in the dedicated feature set. Figure 2 It can be obtained that the vehicle lateral acceleration a y and the vehicle yaw rate γ, the vehicle yaw rate γ and the steering wheel angle δ sw , left front wheel drive torque T dfl and right front wheel drive torque T dfr , right rear wheel driving torque T drr , right front wheel driving torque T dfr and the left rear wheel drive torque T drl , left rear wheel driving torque T drl and the right rear wheel drive torque T drr There is a strong correlation; the vehicle longitudinal speed v x and the wheel rotation angular velocity ω ij , wheel rotation angular velocity ω ij and the left front wheel drive torque T dfl and the left rear wheel drive torque T drl , right front wheel driving torque T dfr and the right rear wheel drive torque T drr There is a complete correlation between them.

[0142] Please refer to Figure 3 , Figure 3 It is a schematic diagram of the significant P value between each feature in the dedicated feature set. Figure 3 It can be seen that the above correlation analysis results are not caused by accidental factors.

[0143] Furthermore, from the PCC Pearson correlation coefficient analysis, it can be seen that the wheel rotation angular velocity ω ij Between, left front wheel drive torque T dfl and the left rear wheel drive torque T drl , right front wheel drive torque T dfr and the right rear wheel drive torque T drr If the sensor signal is completely correlated, it provides sensor signal fault tolerance for data-driven center of mass slip angle estimation. When a wheel speed sensor fails, other speed sensors can provide signals; the same is true for the torque signals of the left front wheel and left rear wheel, right front wheel and right rear wheel of the vehicle. Therefore, the typical left front wheel rotation angular velocity ω is selected fl , left front wheel drive torque T dfl and right front wheel drive torque T dfr The signal is used as the feature for subsequent importance evaluation, and the sensor signal that is completely related to it is used as the error tolerance signal of the estimation system. Therefore, the feature set to be evaluated includes the data set composed of the following parameters: left front wheel rotation angular velocity ω fl , left front wheel drive torque T dfl, right front wheel drive torque T dfr , vehicle longitudinal acceleration a x 、Vehicle lateral acceleration a y , vehicle longitudinal speed v x , vehicle yaw rate γ and steering wheel angle δ sw .

[0144] Random forest (RF) integrates multiple decision trees to improve the accuracy of regression and can evaluate the importance of features, so it is used to analyze the relationship between each variable and the sideslip angle of the center of mass. For the RF algorithm, the mean square error (MSE) is selected as the node impurity function. For a certain node, the impurity calculation formula is:

[0145]

[0146] The importance score of a feature can be calculated as follows:

[0147] n m =w m G m -w left G left -w right G right

[0148]

[0149] Where: N is the number of training samples; X is a feature in the sample; V is the segmentation value of feature X; X left and X right are the training sample sets of the left and right child nodes of node m respectively; y l and r They are the left and right child node values ​​of node m respectively; and are the average values ​​of the target variables of the left and right child nodes of node m; G m (X,V) is the impurity of node m under feature X; w m 、w left 、w right are the ratios of the number of training samples in node m and its left and right child nodes to the total number of training samples; G m , G left , G right are the impurities of node m and its left and right child nodes respectively; n m is the importance of node m; M is the total number of nodes in a decision tree; n i Indicates that the feature is X i I is the node with feature X i The number of nodes; f(X i ) indicates that the feature is X iThe importance of; K is the number of sample features; f(X i ) nor For feature X i The normalized importance assessment score of .

[0150] The final normalized importance evaluation score 1 / K (i.e., score threshold) is used as the standard, and features with scores exceeding this score threshold are selected as input for subsequent training. Among them, K is the number of features to be evaluated for importance.

[0151] In this embodiment, the Random Forest algorithm is applied to the longitudinal acceleration a of the vehicle in the feature set to be evaluated. x 、Vehicle lateral acceleration a y , vehicle longitudinal speed v x , vehicle yaw rate γ, steering wheel angle δ sw 、Left front wheel rotation angular velocity ω fl , left front wheel drive torque T dfl , right front wheel drive torque T dfr A total of 8 features were evaluated for importance. For the final normalized importance evaluation score and its ranking, please refer to Figure 4 .

[0152] Figure 4 is a schematic diagram of normalized importance assessment scores, Figure 4 It can be obtained that the features with importance scores greater than 1 / 8 of the score threshold are vehicle lateral acceleration a y , steering wheel angle δ sw 、Left front wheel rotation angular velocity ω fl Therefore, the vehicle lateral acceleration a is selected y , steering wheel angle δ sw 、Left front wheel rotation angular velocity ω fl The corresponding data set is used as the first feature set (including: vehicle lateral acceleration feature set, steering wheel angle feature set and left front wheel rotation angular velocity feature set), and the first feature set is input into the preset model for training.

[0153] Furthermore, the back propagation neural network (BPNN) is one of the most typical training algorithms in the field of deep learning. In this embodiment, a typical BPNN is used to train the first feature set. BPNN includes three layers, an input layer, a hidden layer, and an output layer. The input layer has three neurons, and the input information is: the vehicle lateral acceleration a y , steering wheel angle δ sw and the left front wheel rotation angular velocity ω flThe corresponding data set; the hidden layer contains 20 neurons; the output layer has 1 neuron, and the output information is the centroid side slip angle β. The training set, test set, and validation set are divided according to a certain ratio, and the Levenberg-Marquardt algorithm is used for training, and the performance indicator is MSE.

[0154] After training, the model is deployed with the vehicle lateral acceleration a y , steering wheel angle δ sw 、Left front wheel rotation angular velocity ω fl As the model input, the Matlab / Simulink and Carsim joint simulation platform is used for verification.

[0155] Under the first scenario setting of vehicle speed of 65km / h, road adhesion coefficient μ of 0.6 and double lane change condition, please refer to the calculation results of the center of mass slip angle Figure 5 , Figure 5 This is a schematic diagram of the calculation results of the center of mass sideslip angle in the first scenario. Figure 5 It can be seen that the method for calculating the sideslip angle of the center of mass proposed in the present invention can accurately estimate the sideslip angle of the center of mass of the vehicle, and the mean square error (RMSE) is used as an evaluation index, and its value is 0.0004.

[0156] In the second scenario setting of vehicle speed of 55km / h, road adhesion coefficient μ of 0.7 and serpentine working condition, please refer to the estimation result of the center of mass side slip angle Figure 6 , Figure 6 The figure is a schematic diagram of the calculation results of the center of mass sideslip angle in the second scenario. Figure 6 It can be obtained that the RMSE is 0.0007, and the center of mass sideslip angle can also be accurately calculated.

[0157] The trained center of mass sideslip angle calculation model is deployed, and the selected features (i.e., the first vehicle lateral acceleration, the first steering wheel angle, and the first left front wheel rotation angular velocity of the vehicle to be measured) are used as inputs to the center of mass sideslip angle calculation model to finally obtain the center of mass sideslip angle of the vehicle to be measured.

[0158] Furthermore, it also includes sensor failure judgment and fault-tolerant switching process, specifically: judging whether the variable signal of PCC correlation analysis |R|=1 in the data-driven vehicle center of mass side slip angle estimation method is invalid or wrong, for example: judging whether the vehicle sensor ω fl Signal failure or error, switch immediately fr ,ω rl ,ω rr If one of the three signals fails again, the rotational angular velocity signals of the remaining wheels are switched in the same manner.

[0159] See also Figure 7 , Figure 7A device for calculating the sideslip angle of the center of mass of a vehicle is shown, wherein the device comprises:

[0160] A first acquisition module 701, used to acquire a first operating parameter of the vehicle;

[0161] A first calculation module 702, configured to calculate an importance evaluation score according to a first operating parameter of the vehicle;

[0162] The second acquisition module 703 is used to determine a first feature set according to the importance evaluation score combined with the first operating parameter of the vehicle; wherein the first feature set includes: a vehicle lateral acceleration feature set, a steering wheel angle feature set and a left front wheel rotation angular velocity feature set;

[0163] A training module 704 is used to train a preset model according to the first feature set to generate a center of mass sideslip angle calculation model;

[0164] The third acquisition module 705 is used to acquire a first vehicle lateral acceleration, a first steering wheel angle, and a first left front wheel rotation angular velocity of the vehicle to be measured;

[0165] The second calculation module 706 is used to calculate the center of mass sideslip angle of the vehicle to be measured through the center of mass sideslip angle calculation model according to the first vehicle lateral acceleration, the first steering wheel angle and the first left front wheel rotation angular velocity.

[0166] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0167] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned vehicle center of mass side slip angle calculation method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a vehicle-mounted computer, etc.

[0168] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0169] See also Figure 8 , Figure 8 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0170] The processor 801 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0171] The memory 802 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 802 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 802, and the processor 801 calls and executes the vehicle center of mass sideslip angle calculation method of the embodiment of this application;

[0172] Input / output interface 803, used to implement information input and output;

[0173] The communication interface 804 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0174] A bus 805 that transmits information between the various components of the device (e.g., the processor 801, the memory 802, the input / output interface 803, and the communication interface 804);

[0175] The processor 801 , the memory 802 , the input / output interface 803 and the communication interface 804 are connected to each other in communication within the device via a bus 805 .

[0176] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned vehicle center of mass sideslip angle calculation method is implemented.

[0177] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0178] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0179] The vehicle center of mass side slip angle calculation method, vehicle center of mass side slip angle calculation device, electronic device and storage medium provided in the embodiment of the present application select a first feature set according to the importance evaluation score of the first operating parameter of the vehicle, and after the center of mass side slip angle calculation model is obtained through training according to the first feature set, since the vehicle lateral acceleration feature set, the steering wheel angle feature set and the left front wheel rotation angular velocity feature set in the first feature set have high importance evaluation scores for the center of mass side slip angle, the calculation accuracy of the trained center of mass side slip angle calculation model can be improved. Furthermore, the center of mass side slip angle of the vehicle to be measured is directly calculated by combining the vehicle lateral acceleration, steering wheel angle and left front wheel rotation angular velocity of the vehicle to be measured with the trained center of mass side slip angle calculation model, which can avoid the problem of large error caused by directly integrating the vehicle-mounted sensor signal to obtain the center of mass side slip angle in the prior art; it can also avoid the problem of low center of mass side slip angle calculation accuracy caused by the inconsistency between the dynamic model and the actual situation in the prior art, thereby better ensuring the calculation accuracy of the center of mass side slip angle.

[0180] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0181] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0182] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0183] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0184] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0185] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " first indicates that the associated objects before and after are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0186] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0187] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0188] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0189] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0190] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for calculating the sideslip angle of a vehicle center of mass, characterized in that: The method comprises: Obtaining a first operating parameter of the vehicle; Calculating an importance assessment score according to the first operating parameter of the vehicle; Determine a first feature set according to the importance evaluation score combined with the first operating parameter of the vehicle; wherein the first feature set includes: a vehicle lateral acceleration feature set, a steering wheel angle feature set, and a left front wheel rotation angular velocity feature set; Training a preset model according to the first feature set to generate a center of mass sideslip angle calculation model; Acquire a first vehicle lateral acceleration, a first steering wheel angle, and a first left front wheel rotation angular velocity of the vehicle to be measured; The center of mass sideslip angle of the vehicle to be measured is obtained by calculating the center of mass sideslip angle calculation model according to the first vehicle lateral acceleration, the first steering wheel angle and the first left front wheel rotation angular velocity.

2. The method for calculating the vehicle center of mass sideslip angle according to claim 1, characterized in that: The calculating the importance evaluation score according to the first operating parameter of the vehicle includes: Generate a dedicated feature set according to a first mapping relationship combined with the first operating parameter of the vehicle; wherein the first mapping relationship is a mapping relationship between the sideslip angle of the center of mass and the first operating parameter of the vehicle; Calculating the Pearson correlation coefficient between each feature in the dedicated feature set; Generate a feature set to be evaluated based on the Pearson correlation coefficient and the dedicated feature set; The importance evaluation score of each feature in the feature set to be evaluated is calculated according to the random forest algorithm.

3. The method for calculating the vehicle center of mass sideslip angle according to claim 2, characterized in that: The determining of a first feature set based on the importance evaluation score and the first operating parameter of the vehicle includes: Normalizing the importance assessment score to obtain a normalized importance score; Sorting the normalized importance scores to obtain a sorting result; A first feature set is obtained by screening the dedicated feature set according to the sorting result and the score threshold.

4. The method for calculating the vehicle center of mass sideslip angle according to claim 3, characterized in that: The step of generating a dedicated feature set according to the first mapping relationship and in combination with the first operating parameter of the vehicle includes: According to the first operating parameter of the vehicle, in combination with the mass center sideslip angle definition formula and the vehicle dynamics equation, the first mapping relationship is obtained; According to the first mapping relationship and the first vehicle operating parameter, a first variable group is obtained by analysis; wherein the first variable group includes: vehicle longitudinal velocity, vehicle longitudinal acceleration, vehicle lateral acceleration, vehicle yaw angular velocity, wheel drive torque, wheel rotation angular velocity and steering wheel angle; Data collection is performed according to the first variable group to generate the dedicated feature set.

5. The method for calculating the vehicle center of mass sideslip angle according to claim 4, characterized in that: The training of the preset model according to the first feature set to generate a center of mass sideslip angle calculation model includes: Dividing the first feature set into a training set, a validation set and a test set; The preset model is trained according to the training set; wherein the preset model is a model established by a machine learning algorithm or a model established by a deep learning algorithm; Adjusting the model parameters of the preset model during the training process according to the verification set; When the number of iterations of the model parameters meets the preset requirements, the center of mass sideslip angle calculation model is generated; The performance of the center of mass sideslip angle calculation model is verified based on the test set.

6. The method for calculating the vehicle center of mass sideslip angle according to claim 5, characterized in that: The verifying the performance of the center of mass sideslip angle calculation model according to the test set includes: Inputting the test set into the center of mass sideslip angle calculation model to generate a first calculation result; Inputting the test set into a simulation platform so that the simulation platform calculates and obtains a second calculation result; Calculating a mean square error and an error between the first calculation result and the second calculation result; The performance of the center of mass sideslip angle calculation model is verified according to the mean square error and the error.

7. The method for calculating the vehicle center of mass sideslip angle according to claim 6, characterized in that: The step of collecting data according to the first variable group to generate the dedicated feature set includes: Obtain preset collection conditions, specific road adhesion coefficients, and specific vehicle speed ranges; Under the preset collection conditions, the specific road adhesion coefficient and the specific vehicle speed range, the first variable group is collected through the simulation platform to generate the dedicated feature set; wherein the sampling frequency of the simulation sensor in the simulation platform is 100 Hz.

8. A vehicle center of mass side slip angle calculation device, characterized in that: The device comprises: A first acquisition module, used to acquire a first operating parameter of the vehicle; A first calculation module, used for calculating an importance evaluation score according to the first operating parameter of the vehicle; A second acquisition module is used to determine a first feature set according to the importance evaluation score combined with the first operating parameter of the vehicle; wherein the first feature set includes: a vehicle lateral acceleration feature set, a steering wheel angle feature set and a left front wheel rotation angular velocity feature set; A training module, used for training a preset model according to the first feature set to generate a center of mass sideslip angle calculation model; A third acquisition module is used to acquire a first vehicle lateral acceleration, a first steering wheel angle, and a first left front wheel rotation angular velocity of the vehicle to be measured; The second calculation module is used to calculate the center of mass sideslip angle of the vehicle to be measured through the center of mass sideslip angle calculation model according to the first vehicle lateral acceleration, the first steering wheel angle and the first left front wheel rotation angular velocity.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the vehicle center of mass sideslip angle calculation method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the vehicle center of mass sideslip angle calculation method according to any one of claims 1 to 7 is implemented.

Images