Data processing method, device and vehicle

Abstract

Embodiments of the present application disclose a data processing method and device and a vehicle. The method comprises: after obtaining a roll angle speed of the vehicle, obtaining a position of a roll center of the vehicle based on the roll angle speed, a pre-constructed modified roll dynamics model and a cubature Kalman filtering algorithm. In the above manner, the position of the roll center of the vehicle can be obtained based on the obtained roll angle speed, the modified roll dynamics model and the cubature Kalman filtering algorithm. Since the modified roll dynamics model represents the mapping relationship between the roll angle speed and the position of the roll center of the vehicle after introducing noise, the position of the roll center of the vehicle after introducing noise can be corrected based on the cubature Kalman filtering, so that the position of the roll center of the vehicle can be accurately estimated, and a more accurate basis is provided for roll stability control of the vehicle, vehicle roll warning and the like.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicles, and more particularly, to a data processing method and device and vehicle. BACKGROUND

[0002] With the continuous development of vehicle technology, as the basis of roll dynamics, the accurate position of the vehicle roll center is particularly important. In related manners, the position of the vehicle roll center can be obtained based on a large amount of sensor information collected in the vehicle and a corresponding vehicle dynamics model. However, the accuracy of the position of the vehicle roll center in related manners still needs to be improved. SUMMARY

[0003] In view of the above problems, the present application provides a data processing method, device and vehicle to improve the above problems.

[0004] In a first aspect, the present application provides a data processing method, comprising: obtaining a roll angular velocity of a vehicle; and obtaining a position of a vehicle roll center based on the roll angular velocity, a pre-constructed modified roll dynamics model and a cubature Kalman filtering algorithm, the modified roll dynamics model representing a mapping relationship between the roll angular velocity and the position of the vehicle roll center after introducing noise, and the modified roll dynamics model being obtained based on a vehicle roll dynamics model, the vehicle roll dynamics model representing a moment balance of the vehicle roll center when the vehicle rolls in an ideal state.

[0005] In a second aspect, the present application provides a data processing device, comprising: a data acquisition unit configured to obtain a roll angular velocity of a vehicle; and a data processing unit configured to obtain a position of a vehicle roll center based on the roll angular velocity, a pre-constructed modified roll dynamics model and a cubature Kalman filtering algorithm, the modified roll dynamics model representing a mapping relationship between the roll angular velocity and the position of the vehicle roll center after introducing noise, and the modified roll dynamics model being obtained based on a vehicle roll dynamics model, the vehicle roll dynamics model representing a moment balance of the vehicle roll center when the vehicle rolls in an ideal state.

[0006] In a third aspect, the present application provides a vehicle, comprising one or more processors and a memory; one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs are configured to execute the method described above.

[0007] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a program code, and the program code performs the method described above when running.

[0008] The present application provides a data processing method, device, vehicle, and storage medium. After obtaining the roll angular velocity of the vehicle, the position of the vehicle's roll center is obtained based on the roll angular velocity, a pre-constructed modified roll dynamics model, and a volumetric Kalman filter algorithm. The above method allows the position of the vehicle's roll center to be obtained based on the obtained roll angular velocity, the modified roll dynamics model, and the volumetric Kalman filter algorithm. Because the modified roll dynamics model represents the mapping relationship between the roll angular velocity and the position of the vehicle's roll center after the introduction of noise, the position of the vehicle's roll center after the introduction of noise can be corrected based on the volumetric Kalman filter, thereby accurately estimating the position of the vehicle's roll center, providing a more accurate basis for algorithms such as vehicle roll stability control and vehicle roll warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A flowchart of a data processing method proposed in an embodiment of the present application;

[0011] Figure 2 A schematic diagram of a vehicle roll dynamics model proposed in this application is shown;

[0012] Figure 3 Shows this application Figure 1 A flowchart of an implementation method proposed in S120;

[0013] Figure 4 A flow chart of a data processing method proposed in another embodiment of the present application is shown;

[0014] Figure 5 A flowchart showing a preferred data processing method proposed in this application

[0015] Figure 6 A structural block diagram of a data processing device proposed in an embodiment of the present application is shown;

[0016] Figure 7 Shown is a structural block diagram of a vehicle proposed in this application. DETAILED DESCRIPTION

[0017] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0018] In an embodiment of the present application, the inventors propose a data processing method, device, and vehicle. After obtaining the roll angular velocity of the vehicle, the position of the vehicle's roll center is obtained based on the roll angular velocity, a pre-constructed modified roll dynamics model, and a volumetric Kalman filter algorithm. Through the above-mentioned method, the position of the vehicle's roll center can be obtained based on the obtained roll angular velocity, the modified roll dynamics model, and the volumetric Kalman filter algorithm. Since the modified roll dynamics model represents the mapping relationship between the roll angular velocity and the position of the vehicle's roll center after the introduction of noise, the position of the vehicle's roll center after the introduction of noise can be corrected based on the volumetric Kalman filter, thereby accurately estimating the position of the vehicle's roll center, providing a more accurate basis for algorithms such as vehicle roll stability control and vehicle roll warning.

[0019] See also Figure 1 , an embodiment of the present application provides a data processing method, the method comprising:

[0020] S110: Obtain the roll angular velocity of the vehicle.

[0021] The roll angular velocity may be the rate of change over time of the angle between the vehicle and the ground after the vehicle tilts when the vehicle turns sharply to one side at a certain speed.

[0022] As a method, the roll angular velocity of the vehicle can be periodically collected based on a data collection device on the vehicle. In this application, the data collection device may include an acceleration sensor, a gyroscope sensor, an angular velocity sensor, etc.

[0023] Optionally, after periodically collecting the roll angular velocity of the vehicle, the collected roll angular velocity may be stored in a designated location so that the stored roll angular velocity can be used for subsequent processing and analysis when estimating the roll center of the vehicle.

[0024] Optionally, the roll angular velocity of the vehicle may be collected multiple times, thereby obtaining multiple roll angular velocities, that is, there may be multiple roll angular velocities.

[0025] As a method, before obtaining the roll angular velocity of the vehicle, a reference state space equation may be obtained based on the vehicle roll dynamics model, and a modified roll dynamics model may be obtained based on the reference state space equation and noise.

[0026] Among them, the vehicle roll dynamics model can characterize the moment balance of the vehicle roll center when the vehicle rolls under ideal conditions. The reference state space equation characterizes the mapping relationship between the roll angular velocity and the position of the vehicle roll center under ideal conditions. Noise can include process noise and observation noise. Process error can refer to the deviation of the measurement results caused by the influence of factors such as instruments and environment. Observation noise can refer to the deviation of the measurement results caused by the limitations of instruments and equipment. The modified roll dynamics model characterizes the mapping relationship between the roll angular velocity and the position of the vehicle roll center after the introduction of noise. In this application, the modified roll dynamics model is obtained based on the vehicle roll dynamics model. The vehicle roll center can be the point where the vertical axis of the vehicle's center of gravity passes when the vehicle rolls. In addition, the position of the vehicle roll center can determine the stability of the vehicle when it rolls.

[0027] Optional, such as Figure 2 As shown, the vehicle roll center (i.e. Figure 2 A vehicle roll dynamics model is established using the RC point in the figure as the moment center during vehicle roll. In this model, the product of the lateral force and the moment arm corresponding to the lateral force, the product of gravity and the moment arm corresponding to gravity, and the product of the moment of inertia and roll angular acceleration, the product of the suspension roll damping coefficient and roll angular velocity, and the product of the suspension roll stiffness and vehicle roll angle are added together, and the two are equal.

[0028] Among them, the vehicle roll dynamics model can be:

[0029]

[0030] in, It can represent the vehicle roll angle, It can be expressed as the roll angular velocity, It can be expressed as the roll angular acceleration, I x It can represent the vehicle's rolling moment of inertia, It can represent the suspension roll stiffness, It can represent the roll damping coefficient, ms can represent the sprung mass of the vehicle, and h rc It can represent the vehicle roll center (i.e. Figure 2 RC point in the middle) to the centroid (i.e. Figure 2 The distance between the CG point in y It can represent the lateral acceleration of the vehicle, and g can represent the acceleration due to gravity.

[0031] Optionally, the vehicle roll dynamics model can be rewritten to obtain the reference state space equation. Moreover, when the vehicle rolls, the maximum roll angle of the vehicle generally does not exceed 1°, so the reference state space equation is generally simplified (i.e. ).

[0032] Among them, the reference state space equation can be:

[0033]

[0034]

[0035] Among them, x1 can represent the vehicle roll angle (i.e. ), x2 can represent the roll angular velocity (i.e. ), It can be expressed as the roll angular acceleration (i.e. ), I x It can represent the vehicle's rolling moment of inertia, It can represent the suspension roll stiffness, It can represent the roll damping coefficient, ms can represent the sprung mass of the vehicle, and h rc It can represent the distance from the vehicle's roll center to the center of mass, a y It can represent the lateral acceleration of the vehicle, and g can represent the acceleration due to gravity.

[0036] Optionally, a reference modified roll dynamics model may be obtained based on the reference state space equation and noise, and the reference modified roll dynamics model may be discretized to obtain a modified roll dynamics model.

[0037] The reference modified roll dynamics model represents the mapping relationship between the roll angular velocity and the position of the vehicle roll center under continuous conditions.

[0038] The reference corrected roll dynamics model can be:

[0039]

[0040] y=h(x,v)=x²+v

[0041] Among them, x can be represented by [x1 x2 x3] T , [x1 x2 x3] T Can be expressed x1 may represent the vehicle roll angle, x2 may represent the roll angular velocity, and x3 may represent the vehicle roll center; μ may represent an external input quantity, which in this application may be 0; w may represent process noise, v may represent measurement noise, and y may represent an actual measured value of the roll angular velocity.

[0042] Optionally, the first-order Euler formula may be used to discretize the continuous-state reference modified roll dynamics model to obtain a modified roll dynamics model, wherein the modified roll dynamics model may include a state equation and an observation equation.

[0043] Among them, the state equation corresponding to the modified roll dynamics model can be:

[0044]

[0045] Wherein, x(k+1) may represent the vehicle roll angle or roll angular velocity or the position of the vehicle roll center at the current moment, x(k) may represent the vehicle roll angle or roll angular velocity or the position of the vehicle roll center at the previous moment, T s It can represent the acquisition time, x1(k) can represent the vehicle roll angle at the previous moment, x2(k) can represent the roll angular velocity at the previous moment, and x3(k) can represent the position of the vehicle roll center at the previous moment.

[0046] Among them, the observation equation corresponding to the modified roll dynamics model can be:

[0047] y(k)=Hx(k)+V(k)

[0048] Wherein, y(k) may represent the roll angular velocity collected at the last moment, and H may represent [0 1 0]. Furthermore, in this application, w(k) ∼ N(0, Q(k)), v(k) ∼ N(0, R(k)).

[0049] In the embodiment of the present application, a transient roll dynamics model can be used to make the vehicle roll dynamics model more accurate and closer to the actual state of the vehicle itself, thereby making the position of the vehicle roll center more accurately estimated.

[0050] S120: Obtaining the position of the vehicle roll center based on the roll angular velocity, a pre-built modified roll dynamics model, and a volumetric Kalman filter algorithm.

[0051] As a method, a predicted position of the vehicle roll center can be obtained based on the roll angular velocity, a pre-built modified roll dynamics model and a volumetric Kalman filter algorithm, and the predicted position of the vehicle roll center can be directly used as the position of the vehicle roll center.

[0052] Optional, such as Figure 3 As shown, the position of the vehicle's roll center can be obtained based on the roll angular velocity, the pre-built modified roll dynamics model, and the cubature Kalman filter algorithm, including:

[0053] S121: Based on the multiple roll angular velocities, a plurality of volume points in the volumetric Kalman filter algorithm are obtained.

[0054] The volumetric Kalman filter algorithm can be an efficient recursive filtering algorithm. In this application, the volumetric Kalman filter algorithm can approximate the true probability density function by selecting multiple volumetric points in the data space to estimate the roll angular velocity and the vehicle's roll center. The volumetric points can be points used in the volumetric Kalman filter algorithm to approximate the true probability density function. In this application, the volumetric points can include the collected roll angular velocity.

[0055] As one approach, multiple volume points in a volumetric Kalman filter algorithm may be obtained based on the collected multiple roll angular velocities and the volumetric Kalman filter algorithm.

[0056] S122: Obtaining the position of the vehicle roll center based on the multiple volume points, the volumetric Kalman filter algorithm, and the modified roll dynamics model.

[0057] As a method, the state equation and observation equation corresponding to the modified roll dynamics model can be obtained based on the modified roll dynamics model; the predicted value of the roll angular velocity and the predicted position of the vehicle roll center can be calculated based on multiple volume points, the volume Kalman filter algorithm, the state equation and the observation equation, so that the predicted position of the vehicle roll center can be directly used as the position of the vehicle roll center.

[0058] The predicted roll angular velocity may be a value calculated and estimated based on the current roll angular velocity using a volumetric Kalman filter algorithm and a modified roll dynamics model. The predicted position of the vehicle's roll center may be a value calculated and estimated based on the current roll center using a volumetric Kalman filter algorithm and a modified roll dynamics model.

[0059] As one approach, in a volumetric Kalman filter algorithm, a reference predicted value of a state variable in the volumetric Kalman filter algorithm at a current moment can be obtained based on multiple volume points and a time update operation in the volumetric Kalman filter. The state variables in the volumetric Kalman filter algorithm may include the vehicle's roll angle, roll angular velocity, and roll center. Subsequently, based on the reference predicted value of the state variable at the current moment and the observation update operation, the predicted value of the state variable at the current moment (i.e., the predicted value of the vehicle's roll angle, the predicted value of the roll angular velocity, and the predicted position of the vehicle's roll center) can be obtained.

[0060] Optionally, before performing the time update operation, the state quantity and the state error covariance matrix corresponding to the state quantity in the cubature Kalman filter algorithm may be initialized. The initialized state quantity and the state error covariance matrix corresponding to the state quantity may be:

[0061]

[0062] Among them, x0 can represent the state quantity, It can represent the mean of the state quantity, P0 can represent the state error covariance matrix, Can be expressed The transposed matrix of .

[0063] As an approach, the time update operation may include a volume point calculation operation, a volume point propagation operation, and calculation of a reference prediction value of the state quantity at the current moment and a reference state error covariance matrix corresponding to the state quantity at the current moment.

[0064] Optionally, the predicted value of the state quantity at the previous moment can be obtained based on the calculation volume point operation and the state equation, and the predicted value of the state quantity at the previous moment can be updated based on the volume point propagation operation, so that the reference value of the state quantity at the current moment can be obtained, and the reference value after time update is estimated based on multiple volume points, and the reference predicted value of the state quantity at the current moment and the reference state error covariance matrix corresponding to the state quantity at the current moment are calculated.

[0065] The operation for calculating volume points may be:

[0066]

[0067]

[0068] Among them, P k-1|k-1 It can represent the state error covariance matrix of the previous moment, S k-1|k-1 The state error covariance matrix P can be k-1|k-1 The value after Cholesky decomposition, Can represent S k-1|k-1 The transposed matrix of ξ i It can represent a volume point set. In this application, ξ i Can be [1] i can represent the i-th volume point, [1] can represent the identity matrix, in this application, [1] i Can be m can represent the number of volume points. In this application, m can be 2n. n can represent the number of state quantities. In this application, n can be 3. i It can represent the weight corresponding to each volume point. In this application, W i Can be expressed X i，k-1|k-1 It can represent the state quantity of the previous moment. It can represent the predicted value of the state quantity at the previous moment.

[0069] Among them, the volume point propagation operation can be:

[0070]

[0071] in, It can represent the reference value of the state quantity at the current moment.

[0072] Among them, the reference prediction value of the calculated state quantity at the current moment and the reference state error covariance matrix corresponding to the state quantity at the current moment can be:

[0073]

[0074]

[0075] in, It can represent the reference prediction value of the state quantity at the current moment, P k|k-1 It can represent the reference state error covariance matrix corresponding to the current state quantity, Q k-1 Can represent process noise, W i It can represent the weight corresponding to each volume point. In this application, W i Can be expressed

[0076] As a method, the observation update operation may include calculating volume point operations, volume point propagation operations, calculating the predicted observation value of the state quantity at the current moment and the prediction error covariance matrix corresponding to the state quantity at the current moment, calculating the mutual covariance matrix of the state quantity at the current moment, calculating the Kalman gain, and calculating the predicted value of the state quantity at the current moment and the state error covariance matrix corresponding to the state quantity at the current moment.

[0077] Optionally, the observation value of the state quantity at the current moment (i.e., the roll angular velocity collected by the data acquisition device) can be obtained based on the observation equation, and then the reference observation value of the state quantity at the current moment and the reference prediction error covariance matrix of the state quantity at the current moment can be calculated by the volume point operation, and the prediction error covariance matrix of the state quantity at the current moment can be calculated based on the volume point propagation operation, and the cross-covariance matrix of the state quantity at the current moment can be calculated based on the predicted observation value of the state quantity at the current moment and the reference predicted value of the state quantity at the current moment, so that the Kalman gain can be calculated based on the cross-covariance matrix of the state quantity at the current moment and the prediction error covariance matrix of the state quantity at the current moment; and the prediction value of the state quantity at the current moment can be obtained based on the reference predicted value of the state quantity at the current moment, the Kalman gain, the observation value of the state quantity at the current moment and the predicted observation value of the state quantity at the current moment, and the state error covariance matrix corresponding to the state quantity at the current moment can be obtained based on the reference state error covariance matrix corresponding to the state quantity at the current moment, the Kalman gain and the prediction error covariance matrix of the state quantity at the current moment.

[0078] The operation for calculating volume points may be:

[0079]

[0080]

[0081] wherein P k|k-1 may represent the reference prediction error covariance matrix of the state quantity at the current moment, S k|k-1 may be the reference prediction error covariance matrix P k|k-1 of the state quantity at the current moment, and may represent the transpose matrix of S k|k-1 , X i,k|k-1 may represent the reference observation value of the state quantity at the current moment, ξ i may represent the set of volume points, and in the present application, ξ i may be may represent the reference prediction value of the state quantity at the current moment.

[0082] wherein the volume point propagation operation can be:

[0083] Y i,k|k-1 = h(X i,k|k-1 )

[0084] wherein Y i,k|k-1 may represent the observation value of the state quantity at the current moment.

[0085] wherein the calculation of the prediction observation value of the state quantity at the current moment and the prediction error covariance matrix corresponding to the state quantity at the current moment can be:

[0086]

[0087]

[0088] wherein may represent the prediction observation value of the state quantity at the current moment, W i may represent the weight corresponding to each volume point, and in the present application, W i may represent P yy,k|k-1 may represent the prediction error covariance matrix corresponding to the state quantity at the current moment, may represent

[0089] the transpose matrix of R k may represent the observation noise.

[0090] wherein the calculation of the cross-covariance matrix of the state quantity at the current moment can be:

[0091]

[0092] Among them, P xy,k|k-1 It can represent the mutual covariance matrix of the state quantity at the current moment, W i It can represent the weight corresponding to each volume point. In this application, W i Can be expressed It can represent the reference predicted value of the state quantity at the current moment, Can be expressed The transposed matrix of .

[0093] Among them, the calculation of Kalman gain can be:

[0094]

[0095] Among them, W k It can be expressed as Kalman gain, P xy,k|k-1 It can represent the reference predicted value of the state quantity at the current moment, It can represent P yy,k|k-1 The inverse matrix of .

[0096] Among them, the predicted value of the state quantity at the current moment and the state error covariance matrix corresponding to the state quantity at the current moment can be calculated as follows:

[0097]

[0098]

[0099] in, It can represent the predicted value of the current state quantity, It can represent the reference prediction value of the state quantity at the current moment, W k It can be expressed as Kalman gain, y k It can represent the observed value of the state quantity at the current moment, It can represent the predicted observation value of the state quantity at the current moment, P k|k It can represent the state error covariance matrix corresponding to the current moment of the state, P k|k-1 It can represent the reference state error covariance matrix corresponding to the current state quantity, P yy,k|k-1 It can represent the prediction error covariance matrix of the state quantity at the current moment, The transposed matrix of the Kalman gain can be expressed as

[0100] In an embodiment of the present application, the predicted position of the vehicle roll center can be obtained based on the roll angular velocity, a pre-constructed modified roll dynamics model and a volumetric Kalman filter algorithm, so that the predicted position of the vehicle roll center can be directly used as the position of the vehicle roll center, and the position of the vehicle roll center is estimated using only the collected roll angular velocity in combination with the modified roll dynamics model and the volumetric Kalman filter algorithm, thereby simplifying the calculation process of the vehicle roll center.

[0101] This embodiment provides a data processing method that, after acquiring a vehicle's roll angular velocity, determines the location of the vehicle's roll center based on the roll angular velocity, a pre-constructed modified roll dynamics model, and a volumetric Kalman filter algorithm. This method allows the location of the vehicle's roll center to be determined based on the acquired roll angular velocity, the modified roll dynamics model, and the volumetric Kalman filter algorithm. Because the modified roll dynamics model represents the mapping relationship between the roll angular velocity and the location of the vehicle's roll center after the introduction of noise, the location of the vehicle's roll center after the introduction of noise can be corrected using the volumetric Kalman filter, thereby accurately estimating the location of the vehicle's roll center and providing a more accurate basis for algorithms such as vehicle roll stability control and vehicle roll warning.

[0102] See also Figure 4 , an embodiment of the present application provides a data processing method, the method comprising:

[0103] S210: Obtain the roll angular velocity of the vehicle.

[0104] S220: Based on the multiple roll angular velocities, obtain multiple volume points in the volumetric Kalman filter algorithm.

[0105] S230: Based on the modified roll dynamics model, obtain a state equation and an observation equation corresponding to the modified roll dynamics model.

[0106] S240: Based on the multiple volume points, the volumetric Kalman filter algorithm, the state equation and the observation equation, calculate and obtain the predicted value of the roll angular velocity and the predicted position of the vehicle roll center.

[0107] S250: Obtaining the position of the vehicle roll center based on the roll angular velocity, the predicted value of the roll angular velocity, and the predicted position of the vehicle roll center.

[0108] As a method, an error can be obtained based on the roll angular velocity and the predicted value of the roll angular velocity. If the error is less than an error threshold, the predicted position of the vehicle roll center is used as the position of the vehicle roll center; if the error is greater than or equal to the error threshold, the position of the vehicle roll center can be obtained based on the historical position and predicted position of the vehicle roll center.

[0109] The error threshold can be a maximum value of an error between the roll angle velocity and the predicted value of the roll angle velocity based on a plurality of test results.

[0110] Optionally, a historical position of the vehicle roll center can be obtained before the position of the vehicle roll center is obtained. In the present application, the position of the vehicle roll center obtained each time can be saved to a designated position, so that when the historical position of the vehicle roll center is needed, the historical position of the vehicle roll center can be directly obtained from the designated position.

[0111] Optionally, an error can be obtained based on a difference between the roll angle velocity and the predicted value of the roll angle velocity, and the size relationship between the error and the error threshold is determined. If the error is less than the error threshold, the predicted position of the vehicle roll center can be taken as the position of the vehicle roll center.

[0112] Optionally, if the error is greater than or equal to the error threshold, the respective weights of the historical position and the predicted position of the vehicle roll center can be determined based on the error; and the position of the vehicle roll center can be obtained based on the respective weights of the historical position and the predicted position of the vehicle roll center, the historical position and the predicted position of the vehicle roll center.

[0113] The greater the error, the greater the weight corresponding to the historical position of the vehicle roll center, and the smaller the weight corresponding to the predicted position of the vehicle roll center.

[0114] For example, if the error is equal to the error threshold, the weight corresponding to the historical position of the vehicle roll center can be set to 0.5, and the weight corresponding to the predicted position of the vehicle roll center is 0.5; if the error is close to the error threshold, the weight corresponding to the historical position of the vehicle roll center can be set to 0.6, and the weight corresponding to the predicted position of the vehicle roll center is 0.4; if the error is much greater than the error threshold, the predicted position of the vehicle roll center can be discarded directly.

[0115] This embodiment provides a data processing method that, through the aforementioned method, allows the location of the vehicle's roll center to be determined based on the acquired roll angular velocity, a modified roll dynamics model, and a volumetric Kalman filter algorithm. Because the modified roll dynamics model represents the mapping relationship between the roll angular velocity and the location of the vehicle's roll center after the introduction of noise, the location of the vehicle's roll center after the introduction of noise can be corrected based on the volumetric Kalman filter, thereby accurately estimating the location of the vehicle's roll center and providing a more accurate basis for algorithms such as vehicle roll stability control and vehicle roll warning. Furthermore, in this application, the weights corresponding to the historical and predicted locations of the vehicle's roll center can be determined based on the magnitude of the error, thereby comprehensively considering the impact of the historical and predicted locations on the location of the vehicle's roll center, thereby obtaining a more accurate location of the vehicle's roll center.

[0116] In order to better understand the solutions in the embodiments of the present application, the process of a preferred implementation scheme is introduced below.

[0117] See also Figure 5 A vehicle roll dynamics model can be constructed based on step S1, and the model can be transformed based on step S2 to obtain a modified roll dynamics model. Then, the state equation and observation equation corresponding to the modified roll dynamics model can be input into the cubature Kalman filter algorithm based on step S3, so that the position of the vehicle roll center can be estimated based on step S4.

[0118] See also Figure 6 The present application provides a data processing device 800, the device 800 comprising:

[0119] The data acquisition unit 810 is used to acquire the roll angular velocity of the vehicle.

[0120] The data processing unit 820 is used to obtain the position of the vehicle roll center based on the roll angular velocity, a pre-constructed modified roll dynamics model and a volumetric Kalman filter algorithm, wherein the modified roll dynamics model represents the mapping relationship between the roll angular velocity and the position of the vehicle roll center after the introduction of noise, and the modified roll dynamics model is obtained based on the vehicle roll dynamics model, and the vehicle roll dynamics model represents the moment balance of the vehicle roll center when the vehicle rolls under ideal conditions.

[0121] As a method, the data processing unit 820 is specifically used to obtain a reference state space equation based on the vehicle roll dynamics model, wherein the reference state space equation represents the mapping relationship between the roll angular velocity and the position of the vehicle roll center under ideal conditions; and obtain the modified roll dynamics model based on the reference state space equation and the noise.

[0122] Optionally, the data processing unit 820 is specifically configured to obtain a reference modified roll dynamics model based on the reference state space equation and the noise, the reference modified roll dynamics model representing a mapping relationship between the roll angular velocity and the position of the vehicle roll center in a continuous state; and perform discretization processing on the reference modified roll dynamics model to obtain the modified roll dynamics model.

[0123] As one way, the data processing unit 820 is specifically configured to obtain a plurality of cubature points in the cubature Kalman filtering algorithm based on a plurality of roll angular velocities; and obtain the position of the vehicle roll center based on the plurality of cubature points, the cubature Kalman filtering algorithm and the modified roll dynamics model.

[0124] Optionally, the data processing unit 820 is specifically configured to obtain a state equation and an observation equation corresponding to the modified roll dynamics model based on the modified roll dynamics model; calculate a predicted value of the roll angular velocity and a predicted position of the vehicle roll center based on the plurality of cubature points, the cubature Kalman filtering algorithm, the state equation and the observation equation; and obtain the position of the vehicle roll center based on the roll angular velocity, the predicted value of the roll angular velocity and the predicted position of the vehicle roll center.

[0125] Optionally, the data processing unit 820 is specifically configured to obtain an error based on the roll angular velocity and the predicted value of the roll angular velocity; and if the error is less than an error threshold, take the predicted position of the vehicle roll center as the position of the vehicle roll center.

[0126] As one way, the data processing unit 820 is specifically configured to obtain a historical position of the vehicle roll center; and the method further comprises: if the error is greater than or equal to the error threshold, obtaining the position of the vehicle roll center based on the historical position and the predicted position of the vehicle roll center.

[0127] The following will be described in combination with Figure 7 A vehicle is described.

[0128] Please refer to Figure 7 Based on the above data processing method and device, another vehicle 100 that can execute the foregoing data processing method is further provided. The vehicle 100 comprises a processor 102, a memory 104 and a data acquisition device 106, wherein the memory 104 stores a program that can execute the content in the foregoing embodiments, and the processor 102 can execute the program stored in the memory 104.

[0129] The processor 102 may include one or more processing cores. The processor 102 utilizes various interfaces and circuits to connect various components within the vehicle 100. It executes instructions, programs, code sets, or instruction sets stored in the memory 104 and accesses data stored in the memory 104 to perform various functions and process data for the vehicle 100. Optionally, the processor 102 may be implemented in the form of at least one of a network processor (NPU), a digital signal processor (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 102 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), a network processor (NPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing display content; the NPU is responsible for processing multimedia data such as video and images; and the modem is responsible for wireless communication. It is understandable that the above-mentioned modem may not be integrated into the processor 102, but may be implemented separately through a communication chip.

[0130] The memory 104 may include random access memory (RAM), read-only memory (ROM), and double data rate synchronous dynamic random access memory (DDR). The memory 104 may be used to store instructions, programs, codes, code sets, or instruction sets. The memory 104 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the various method embodiments described below, etc. The data storage area may also store data created by the vehicle 100 during use (such as a phone book, audio and video data, chat history data, etc.).

[0131] The data acquisition device 106 may be used to periodically acquire the roll angular velocity of the vehicle 100. In the present application, the data acquisition device may include an acceleration sensor, a gyroscope sensor, an angular velocity sensor, and the like.

[0132] The embodiment of the present application provides a computer readable storage medium. The computer readable storage medium stores program codes, and the program codes can be invoked by a processor to execute the method described in the above method embodiment.

[0133] The computer readable storage medium can be an electronic storage such as a flash memory, an EEPROM (electrically erasable programmable read-only memory), an EPROM, a hard disk or a ROM. Alternatively, the computer readable storage medium comprises a non-volatile computer readable storage medium. The computer readable storage medium has a storage space for storing program codes for executing any method steps in the above method. The program codes can be read from or written into one or more computer program products. The program codes can be compressed in a suitable form, for example.

[0134] To sum up, the data processing method and device and vehicle provided by the present application can obtain the position of the roll center of the vehicle based on the roll angular velocity, the pre-constructed correction roll dynamics model and the cubature Kalman filtering algorithm. In this way, the position of the roll center of the vehicle can be obtained based on the obtained roll angular velocity, the correction roll dynamics model and the cubature Kalman filtering algorithm. Since the correction roll dynamics model represents the mapping relationship between the roll angular velocity and the position of the roll center of the vehicle after introducing noise, the position of the roll center of the vehicle after introducing noise can be corrected based on the cubature Kalman filtering, so that the position of the roll center of the vehicle can be accurately estimated, and a more accurate basis is provided for the roll stability control of the vehicle, the vehicle roll warning algorithm and the like.

[0135] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part of the technical features; and these modifications or replacements do not drive the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A data processing method, characterized in that: The method comprises: Obtaining a roll angular velocity of the vehicle; wherein the roll angular velocity may be multiple; Based on the multiple roll angular velocities, multiple volume points in a pre-built volumetric Kalman filter algorithm are obtained; Based on a pre-constructed modified roll dynamics model, a state equation and an observation equation corresponding to the modified roll dynamics model are obtained; the modified roll dynamics model represents a mapping relationship between the roll angular velocity and the position of the vehicle roll center after the introduction of noise, and the modified roll dynamics model is obtained based on a vehicle roll dynamics model, and the vehicle roll dynamics model represents, under an ideal state, a moment balance at the vehicle roll center when the vehicle rolls; Calculating a predicted value of the roll angular velocity and a predicted position of the vehicle roll center based on the plurality of volume points, the volumetric Kalman filter algorithm, the state equation, and the observation equation; The position of the vehicle roll center is obtained based on the roll angular velocity, the predicted value of the roll angular velocity, and the predicted position of the vehicle roll center.

2. The method according to claim 1, characterized in that Before obtaining the roll angular velocity of the vehicle, the method further includes: Based on the vehicle roll dynamics model, a reference state space equation is obtained, wherein the reference state space equation represents a mapping relationship between the roll angular velocity and the position of the vehicle roll center under an ideal state; The modified roll dynamics model is obtained based on the reference state-space equation and the noise.

3. The method according to claim 2, characterized in that The step of obtaining the modified roll dynamics model based on the reference state-space equation and the noise includes: Based on the reference state-space equation and the noise, a reference modified roll dynamics model is obtained, wherein the reference modified roll dynamics model represents a mapping relationship between the roll angular velocity and the position of the vehicle roll center under a continuous state; Discretization is performed on the reference modified roll dynamics model to obtain the modified roll dynamics model.

4. The method according to claim 1, wherein The obtaining of the position of the vehicle roll center based on the roll angular velocity, the predicted value of the roll angular velocity, and the predicted position of the vehicle roll center comprises: obtaining an error based on the roll angular velocity and the predicted value of the roll angular velocity; If the error is less than an error threshold, the predicted position of the vehicle roll center is used as the position of the vehicle roll center.

5. The method according to claim 4, characterized in that Before obtaining the position of the vehicle roll center based on the roll angular velocity, the pre-built modified roll dynamics model and the volumetric Kalman filter algorithm, the method further includes: Obtaining a historical position of the vehicle roll center; The method further comprises: If the error is greater than or equal to the error threshold, the position of the vehicle roll center is obtained based on the historical position and the predicted position of the vehicle roll center.

6. A data processing device, characterized in that: The device comprises: A data acquisition unit, configured to acquire the roll angular velocity of the vehicle; A data processing unit is used to obtain multiple volume points in a pre-constructed volumetric Kalman filter algorithm based on multiple roll angular velocities; obtain a state equation and an observation equation corresponding to the modified roll dynamics model based on a pre-constructed modified roll dynamics model; calculate the predicted value of the roll angular velocity and the predicted position of the vehicle roll center based on the multiple volume points, the volumetric Kalman filter algorithm, the state equation and the observation equation; obtain the position of the vehicle roll center based on the roll angular velocity, the predicted value of the roll angular velocity and the predicted position of the vehicle roll center; the modified roll dynamics model represents the mapping relationship between the roll angular velocity and the position of the vehicle roll center after noise is introduced, and the modified roll dynamics model is obtained based on the vehicle roll dynamics model, and the vehicle roll dynamics model represents the moment balance of the vehicle roll center when the vehicle rolls under ideal conditions.

7. A vehicle, characterized in that: including one or more processors and memory; One or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs are configured to execute the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores program code, wherein when the program code is run, the method according to any one of claims 1 to 5 is executed.

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