Vehicle state estimation method and device, storage medium and product

Abstract

The application provides a vehicle state estimation method, device, storage medium and product. The method adjusts the Kalman filter through the sensor signal of the vehicle at the current time, performs Kalman filtering on the current sensor signal through the adjusted Kalman filter, and obtains estimation information including estimated speed and estimated acceleration at the current time. In the braking working condition, the estimated speed and the estimated acceleration are corrected according to the estimated acceleration and the first reference speed at the last time, and the first reference speed and the first reference acceleration at the current time are obtained, and the vehicle is controlled based on the first reference acceleration and the first reference speed at the current time. Therefore, the application obtains the corresponding estimated acceleration and estimated speed according to the adjusted Kalman filter, and performs corresponding correction processing through the estimated acceleration and the estimated speed of different vehicle driving conditions, thereby improving the accuracy of the obtained acceleration and reference speed in the vehicle state information.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle control, and in particular to a vehicle state estimation method and device, a storage medium and a product. BACKGROUND

[0002] The reference vehicle speed and the longitudinal acceleration of a vehicle as vehicle state data have an important influence on the control of the vehicle.

[0003] The existing scheme for obtaining the reference vehicle speed and the longitudinal acceleration is usually based on a prediction model established in advance and sensor data of the vehicle to determine the corresponding reference vehicle speed and longitudinal acceleration. However, the obtaining scheme based on the prediction model does not consider the complexity of the vehicle driving condition, and thus cannot accurately obtain the reference vehicle speed and the longitudinal acceleration. SUMMARY

[0004] The present application provides a vehicle state estimation method and device, a storage medium and a product to solve the problem that the existing scheme does not consider the complexity of the vehicle driving condition and cannot accurately obtain the reference vehicle speed and the longitudinal acceleration.

[0005] In a first aspect, an embodiment of the present application provides a vehicle state estimation method, comprising:

[0006] Obtaining a sensor signal sent by a sensor on a vehicle at a current time, the sensor signal comprising an acceleration signal;

[0007] Adjusting a Kalman filter according to a brake pedal opening, a steering wheel angle and an acceleration vibration amplitude of the vehicle at the current time, to perform Kalman filtering on the sensor signal by the adjusted Kalman filter to obtain estimation information at the current time, the estimation information comprising an estimated acceleration, and the acceleration vibration amplitude being a mean square error of a plurality of estimated accelerations in a historical time period;

[0008] In a braking condition, determining a second reference vehicle speed at the current time according to the estimated acceleration and a first reference vehicle speed at a previous time, and correcting the second reference vehicle speed by a third reference vehicle speed at a next time estimated by an average acceleration between a brake starting time and the current time, to obtain the first reference vehicle speed at the current time, and the average acceleration being an average value of the first reference acceleration between the brake starting time and the current time;

[0009] Correcting the estimated acceleration according to the first reference vehicle speed at the current time and the first reference vehicle speed at the previous time to obtain a first reference acceleration.

[0010] In a possible implementation, the first weight and the second weight are determined according to the second reference vehicle speed at the current moment, the first weight is positively correlated with the second reference vehicle speed at the current moment, the sum of the second weight and the first weight is a preset maximum weight, the third weight is positively correlated with the brake pedal opening degree at the current moment, and the sum of the fourth weight and the third weight is the preset maximum weight;

[0011] The fourth reference vehicle speed is obtained by weighting the second reference vehicle speed and the third reference vehicle speed according to the first weight and the second weight, the first weight being the weight of the second reference vehicle speed, and the second weight being the weight of the third reference vehicle speed;

[0012] The first reference vehicle speed is obtained by weighting the fourth reference vehicle speed and the third reference vehicle speed according to the third weight and the fourth weight, the third weight being the weight of the fourth reference vehicle speed, and the fourth weight being the weight of the third reference vehicle speed.

[0013] In a possible implementation, the acceleration of the vehicle from the previous moment to the current moment is determined as a differential acceleration according to the first reference vehicle speed at the current moment and the first reference vehicle speed at the previous moment;

[0014] The second reference acceleration is obtained by weighting the differential acceleration and the estimated acceleration according to the third weight and the fourth weight, the third weight being the weight of the differential acceleration, and the fourth weight being the weight of the estimated acceleration;

[0015] The fifth weight and the sixth weight are determined according to the accelerator pedal opening degree at the current moment, the fifth weight being positively correlated with the accelerator pedal opening degree at the current moment, and the sum of the sixth weight and the fifth weight being the preset maximum weight;

[0016] The first reference acceleration is obtained by weighting the differential acceleration and the second reference acceleration according to the fifth weight and the sixth weight, the fifth weight being the weight of the differential acceleration, and the sixth weight being the weight of the corrected acceleration.

[0017] In a possible implementation, the matrix adjustment parameters of the Kalman filter are determined according to the brake pedal opening, the steering wheel angle and the acceleration vibration amplitude at the current moment, and the matrix adjustment parameters include a first adjustment parameter, a second adjustment parameter and a third adjustment parameter, the first adjustment parameter is negatively correlated with the brake pedal opening, the second adjustment parameter is positively correlated with the steering wheel angle, and the third adjustment parameter is positively correlated with the acceleration vibration amplitude.

[0018] The preset covariance matrix of the Kalman filter is adjusted by the first adjustment parameter, the second adjustment parameter and the third adjustment parameter, to obtain a dynamic covariance matrix.

[0019] The sensor signal is Kalman filtered according to the dynamic covariance matrix, to obtain estimated information at the current moment.

[0020] In a possible implementation, the preset covariance matrix includes a preset process noise covariance matrix and a preset measurement noise covariance matrix, and the dynamic covariance matrix includes a dynamic process noise covariance matrix and a dynamic measurement noise covariance matrix.

[0021] The product of the first adjustment parameter, the second adjustment parameter, the third adjustment parameter and the preset process noise covariance matrix is taken as the dynamic process noise covariance matrix.

[0022] The product of the third adjustment parameter and the preset measurement noise covariance matrix is taken as the dynamic measurement noise covariance matrix.

[0023] In a possible implementation, the sensor signal further includes a wheel speed signal of each wheel, and the estimated information further includes an estimated wheel speed of each wheel.

[0024] In a non-braking condition, a non-drive axle speed is determined according to the estimated wheel speed of the wheel on the non-drive axle, and the non-drive axle speed is taken as a first reference vehicle speed at the current moment, the non-drive axle speed being an average of the estimated wheel speeds of the two wheels of the non-drive axle.

[0025] In a second aspect, an embodiment of the present application provides a vehicle state estimation device, including:

[0026] An acquisition module is configured to acquire a sensor signal sent by a sensor on a vehicle at a current moment, the sensor signal including an acceleration signal.

[0027] The first processing module is configured to adjust a Kalman filter according to a brake pedal opening degree, a steering wheel angle and an acceleration vibration amplitude of the vehicle at a current time, to perform Kalman filtering on the sensor signal by using the adjusted Kalman filter, and to obtain estimated information at the current time, wherein the estimated information comprises an estimated acceleration, and the acceleration vibration amplitude is a mean square error of a plurality of estimated accelerations in a historical time period;

[0028] The second processing module is configured to determine a second reference vehicle speed at the current time according to the estimated acceleration and a first reference vehicle speed at a previous time in a braking condition, and to correct the second reference vehicle speed by a third reference vehicle speed at a next time estimated by an average acceleration between a brake starting time and the current time, to obtain the first reference vehicle speed at the current time, wherein the average acceleration is an average value of the first reference acceleration between the brake starting time and the current time.

[0029] The control module is configured to correct the estimated acceleration according to the first reference vehicle speed at the current time and the first reference vehicle speed at the previous time, to obtain a first reference acceleration.

[0030] In a possible implementation, the second processing module is specifically configured to determine a first weight and a second weight according to the second reference vehicle speed at the current time, and to determine a third weight and a fourth weight according to a brake pedal opening degree at the current time, wherein the first weight is positively correlated with the second reference vehicle speed at the current time, a sum of the second weight and the first weight is a preset maximum weight, the third weight is positively correlated with the brake pedal opening degree at the current time, and a sum of the fourth weight and the third weight is the preset maximum weight.

[0031] The second processing module is configured to weight the second reference vehicle speed at the current time and the third reference vehicle speed according to the first weight and the second weight, to obtain a fourth reference vehicle speed, wherein the first weight is used as the weight of the second reference vehicle speed, and the second weight is used as the weight of the third reference vehicle speed.

[0032] The second processing module is configured to weight the fourth reference vehicle speed and the third reference vehicle speed according to the third weight and the fourth weight, to obtain the first reference vehicle speed, wherein the third weight is used as the weight of the fourth reference vehicle speed, and the fourth weight is used as the weight of the third reference vehicle speed.

[0033] In a possible implementation, the second processing module is specifically configured to determine an acceleration of the vehicle between the previous time and the current time as a differential acceleration according to the first reference vehicle speed at the current time and the first reference vehicle speed at the previous time.

[0034] weighting the difference acceleration and the estimated acceleration according to the third weight and the fourth weight, the third weight being used as the weight of the difference acceleration, and the fourth weight being used as the weight of the estimated acceleration, to obtain a second reference acceleration;

[0035] determining a fifth weight and a sixth weight according to the accelerator pedal opening degree at the current moment, the fifth weight being positively correlated with the accelerator pedal opening degree at the current moment, and the sum of the fifth weight and the sixth weight being the preset maximum weight;

[0036] weighting the difference acceleration and the second reference acceleration according to the fifth weight and the sixth weight, the fifth weight being used as the weight of the difference acceleration, and the sixth weight being used as the weight of the modified acceleration, to obtain the first reference acceleration.

[0037] In a possible implementation, the first processing module is specifically configured to determine a matrix adjustment parameter of the Kalman filter according to the brake pedal opening degree, the steering wheel angle and the acceleration vibration amplitude at the current moment, respectively, the matrix adjustment parameter including a first adjustment parameter, a second adjustment parameter and a third adjustment parameter, the first adjustment parameter being negatively correlated with the brake pedal opening degree, the second adjustment parameter being positively correlated with the steering wheel angle, and the third adjustment parameter being positively correlated with the acceleration vibration amplitude.

[0038] adjust the preset covariance matrix of the Kalman filter by using the first adjustment parameter, the second adjustment parameter and the third adjustment parameter, to obtain a dynamic covariance matrix.

[0039] perform Kalman filtering on the sensor signal according to the dynamic covariance matrix, to obtain estimated information at the current moment.

[0040] In a possible implementation, the preset covariance matrix includes a preset process noise covariance matrix and a preset measurement noise covariance matrix, and the dynamic covariance matrix includes a dynamic process noise covariance matrix and a dynamic measurement noise covariance matrix, and the first processing module is specifically configured to use a product of the first adjustment parameter, the second adjustment parameter, the third adjustment parameter and the preset process noise covariance matrix as the dynamic process noise covariance matrix.

[0041] use a product of the third adjustment parameter and the preset measurement noise covariance matrix as the dynamic measurement noise covariance matrix.

[0042] In a possible implementation, the first processing module is further configured to, in the non-braking working condition, determine a non-drive axle speed according to the estimated speeds of the wheels on the non-drive axle, and take the non-drive axle speed as the first reference speed at the current time, the non-drive axle speed being an average of the estimated speeds of the two wheels of the non-drive axle.

[0043] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, and a memory connected with the processor in communication;

[0044] The memory stores computer-executable instructions.

[0045] The processor executes the computer-executable instructions stored in the memory to implement the method as described above.

[0046] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are executed by a processor to implement the method as described above.

[0047] In a fifth aspect, an embodiment of the present application provides a computer program product, characterized by comprising a computer program, which is executed by a processor to implement the method as described above.

[0048] The vehicle state estimation method, device, storage medium and product provided by the embodiments of the present application adjust the Kalman filter through the sensor signal of the vehicle at the current time, perform Kalman filtering on the currently acquired sensor signal through the adjusted Kalman filter, and obtain the estimation information including the estimated speed and the estimated acceleration at the current time. In the braking working condition, the estimated speed and the estimated acceleration are corrected according to the estimated acceleration and the first reference speed at the last time, to obtain the first reference speed and the first reference acceleration at the current time, so that the vehicle is controlled based on the first reference acceleration and the first reference speed at the current time. The Kalman filter used in the present application is dynamically related to the brake pedal opening degree, the steering wheel rotation angle and the acceleration vibration amplitude, which can better reflect the current working condition of the vehicle, and the reference speed is corrected multiple times in the braking working condition, which can help to improve the accuracy of filtering, and further improve the accuracy of the acceleration and the reference speed. BRIEF DESCRIPTION OF DRAWINGS

[0049] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0050] Figure 1 The basic framework schematic diagram provided by the present application is shown in the following figure;

[0051] Figure 2Flowchart of vehicle state estimation method provided in the present application Figure 1 ;

[0052] Figure 3 Flowchart of vehicle state estimation method provided in the present application Figure 2 ;

[0053] Figure 4 Structure diagram of vehicle state estimation device provided in the present application

[0054] Figure 5 Structure diagram of electronic device provided in the present application.

[0055] The specific embodiments of the present application have been shown and described in the above-described drawings, and will be described in more detail hereinafter. These drawings and the written description are not intended to restrict the scope of the present application in any way, but to illustrate the concept of the present application by reference to specific embodiments. DETAILED DESCRIPTION

[0056] The exemplary embodiments will be described in detail herein with reference to the attached drawings. The following description is made with reference to the accompanying drawings in which like reference numerals refer to like elements, unless the context of use indicates otherwise. The following exemplary embodiments described in the following description are not meant to be limiting of the present application in any way, but are merely to provide examples of how the present application can be implemented. Thus, the present application is not intended to be limited to the embodiments described herein, but rather is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0057] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in one or more embodiments of the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards, and provide corresponding operation portal for user to choose authorization or refusal.

[0058] It should be noted that in the embodiments of the present application, some software, components, models and other industry existing solutions may be mentioned, which should be considered as exemplary, and the purpose is only to illustrate the feasibility of the implementation of the technical solutions of the present application, but does not mean that the applicant has or will necessarily use the solution.

[0059] In the implementation of the control of the commercial vehicle, such as the implementation of the functions of the vehicle, such as the brake force distribution, the anti-lock braking, and the like, the accurate vehicle state data is the basis for the implementation of the control of the commercial vehicle. Among them, the reference vehicle speed and the longitudinal acceleration of the vehicle are important reference data in the vehicle state data. Only by obtaining the accurate vehicle reference speed and the longitudinal acceleration in the vehicle state data, the control parameters can be accurately calculated, and the control of the commercial vehicle can be implemented based on the accurate control parameters.

[0060] The existing scheme for obtaining the reference speed and the longitudinal acceleration includes the method for determining the reference speed and the acceleration through the pre-established prediction model and the method for determining the reference speed and the acceleration through the fuzzy logic algorithm. Among them, for the method for determining the reference speed and the acceleration through the pre-established prediction model, the obtained vehicle sensor data is input into the pre-established prediction model to obtain the output reference speed and acceleration at the next time; for the method for determining the reference speed and the acceleration through the fuzzy logic algorithm, the obtained vehicle sensor data needs to be synthesized and processed through the fuzzy inference mechanism, and the reference speed and the acceleration of the vehicle are determined according to the processing result.

[0061] It is found through the research on the working process of the above-mentioned existing scheme that, for the acquisition of the reference speed and the acceleration of the vehicle through the prediction model, only the vehicle working condition with stable speed change is considered, and the speed change of the vehicle under the special working condition such as emergency braking is not considered, so that the calculated vehicle reference speed in the prediction simulation result cannot effectively reflect the actual speed; for the acquisition of the reference speed and the acceleration of the vehicle through the fuzzy logic algorithm, the wheel speed credibility fuzzy rules of each wheel under different driving states of the vehicle such as acceleration, deceleration, and coasting are not established, that is, the complexity of the vehicle working condition is not considered, so that the reference speed and the longitudinal acceleration cannot be accurately obtained.

[0062] Therefore, the present application provides a vehicle state estimation method. The Kalman filter is adjusted through the sensor signal of the vehicle at the current time, so as to perform Kalman filtering on the currently obtained sensor signal through the adjusted Kalman filter, and obtain the estimation information including the estimated speed and the estimated acceleration at the current time. In the braking working condition, the estimated acceleration and the estimated acceleration are corrected according to the estimated acceleration and the first reference speed at the last time, and the first reference acceleration and the first reference speed at the current time are obtained, and the vehicle is controlled based on the first reference acceleration and the first reference speed at the current time. The Kalman filter used in the present application is dynamically related to the brake pedal opening degree, the steering wheel rotation angle, and the acceleration vibration amplitude, which can better reflect the current working condition of the vehicle, and the reference speed is corrected multiple times in the braking working condition, which can help to improve the accuracy of the filtering, and further improve the accuracy of the acceleration and the reference speed.

[0063] The technical solutions of the present application and how the technical solutions of the present application solve the above technical problems will be described in detail below with specific examples. The following specific examples can be combined with each other, and the same or similar concepts or processes can not be described again in some examples. The embodiments of the present application will be described below with reference to the accompanying drawings.

[0064] Figure 1 The basic framework diagram provided for the present application is shown in the figure, Figure 2 The flowchart of the vehicle state estimation method provided for the present application is shown in the figure Figure 1 , combined Figure 2 and Figure 3 , the method comprises:

[0065] S101, acquiring the sensor signal sent by the sensor on the vehicle at the current time, the sensor signal comprising: acceleration signal.

[0066] Specifically, the corresponding sensor signal at the current time is acquired through each vehicle sensor, such as acquiring the acceleration signal of the corresponding vehicle at the current time through the IMU (Inertial Measurement Unit), wherein the acceleration signal is used to indicate the longitudinal acceleration of the current vehicle, and the acceleration vibration amplitude is acquired according to the acceleration signal. The sensor signal also includes the brake pedal opening, steering wheel angle and vehicle speed signal of each wheel at the current time.

[0067] S102, adjusting the Kalman filter according to the brake pedal opening, steering wheel angle and acceleration vibration amplitude of the vehicle at the current time, and performing Kalman filtering on the sensor signal through the adjusted Kalman filter to obtain the estimation information at the current time, the estimation information comprising: estimated acceleration, and the acceleration vibration amplitude is the mean square error of a plurality of estimated accelerations in a historical time period.

[0068] Specifically, after acquiring the sensor signal corresponding to the current vehicle, the process noise covariance matrix and the measurement noise covariance matrix in the Kalman filter are processed according to the brake pedal opening, steering wheel angle and acceleration vibration amplitude in the sensor signal before the longitudinal acceleration of the current vehicle and the first reference speed of the vehicle at the last time are processed through the Kalman filter, so as to realize the adjustment of the Kalman filter.

[0069] Further, the longitudinal acceleration of the current vehicle and the reference speed of the vehicle at the last time are input into the adjusted Kalman filter for processing to obtain the estimation information at the current time, which includes the estimated speed and the estimated acceleration of each wheel.

[0070] S103. Under braking conditions, based on the estimated acceleration and the first reference vehicle speed at the previous moment, determine the second reference vehicle speed at the current moment, and correct the second reference vehicle speed by using the third reference vehicle speed at the next moment estimated by the average acceleration from the braking start moment to the current moment, to obtain the first reference vehicle speed at the current moment. The average acceleration is the average value of the first reference acceleration from the braking start moment to the current moment.

[0071] Specifically, after obtaining the estimated information from the adjusted Kalman filter output, the current driving conditions of the vehicle are obtained, and the estimated speed and estimated acceleration in the estimated information are corrected according to the different driving conditions of the vehicle.

[0072] Furthermore, if the vehicle is under braking conditions, the estimated vehicle speed is corrected based on the estimated acceleration and the first reference vehicle speed at the previous moment. If the vehicle is at the end of the braking condition, the estimated vehicle speed also needs to be corrected based on the average acceleration. If the vehicle is not under braking conditions, the estimated vehicle speed is corrected based on the speed of the non-drive axle to obtain the first reference vehicle speed corresponding to the current moment after correction.

[0073] S104. Based on the first reference vehicle speed at the current moment and the first reference vehicle speed at the previous moment, the estimated acceleration is corrected to obtain the first reference acceleration.

[0074] Specifically, the estimated vehicle speed is corrected under different driving conditions. After obtaining the first reference speed corresponding to the current moment after correction, in order to avoid the estimated acceleration from changing abruptly due to errors caused by slope and impact during the smooth driving process, it is also necessary to correct the estimated acceleration based on the first reference speed at the current moment and the first reference speed at the previous moment.

[0075] Furthermore, based on the first reference vehicle speed at the current moment and the first reference vehicle speed at the previous moment, and combined with the correction coefficients corresponding to the brake pedal opening and accelerator pedal opening, the estimated acceleration in the estimated information is corrected to obtain the first reference acceleration at the current moment. Based on the obtained corrected first reference acceleration and first reference vehicle speed at the current moment, the vehicle is controlled.

[0076] The vehicle state estimation method provided in the application adjusts the Kalman filter through the sensor signal of the vehicle at the current time, performs Kalman filtering on the currently acquired sensor signal through the adjusted Kalman filter, and obtains the estimation information including the estimated speed and the estimated acceleration at the current time. In the braking working condition, the estimated acceleration and the estimated acceleration are corrected according to the estimated acceleration and the first reference speed at the last time, and the first reference acceleration and the first reference speed at the current time are obtained, and the vehicle is controlled based on the first reference acceleration and the first reference speed at the current time. The Kalman filter used in the application is dynamically related to the brake pedal opening, the steering wheel angle and the acceleration vibration amplitude, which can better reflect the current working condition of the vehicle, and the reference speed is corrected multiple times in the braking working condition, which can help to improve the accuracy of filtering, and further improve the accuracy of the acceleration and the reference speed.

[0077] Figure 2 Flowchart of the vehicle state estimation method provided in the application Figure 3 As shown in Figure 1 the embodiment is based on Figure 4 the embodiment, the vehicle state estimation method is described in detail, which comprises the following steps:

[0078] S201, acquiring the sensor signal sent by the sensor on the vehicle at the current time.

[0079] Specifically, the content of this step is the same as that of S101, and will not be repeated here.

[0080] S202, determining the matrix adjustment parameters of the Kalman filter according to the brake pedal opening, the steering wheel angle and the acceleration vibration amplitude at the current time.

[0081] Specifically, after acquiring the sensor signal of the current vehicle, the process noise covariance matrix and the measurement noise covariance matrix in the Kalman filter need to be adjusted, that is, the preset covariance matrix in the Kalman filter needs to be adjusted, so that the input longitudinal acceleration of the vehicle at the current time and the first reference speed of the vehicle at the last time are filtered based on the adjusted Kalman filter.

[0082] The matrix adjustment parameters include a first adjustment parameter, a second adjustment parameter and a third adjustment parameter, the first adjustment parameter is used to indicate a coefficient related to the brake pedal opening, the second adjustment parameter is used to indicate a coefficient related to the steering wheel angle, and the third adjustment parameter is used to indicate a coefficient related to the acceleration vibration amplitude.

[0083] Further, for the first adjustment parameter, the corresponding adjustment parameter is obtained in the preset brake pedal opening degree curve according to the corresponding brake pedal opening degree, for the second adjustment parameter, the corresponding adjustment parameter is obtained in the preset steering wheel angle curve according to the corresponding steering wheel angle, and for the third adjustment parameter, the corresponding adjustment parameter is obtained in the preset vibration amplitude curve according to the corresponding acceleration vibration amplitude. The first adjustment parameter is negatively related to the brake pedal opening degree, the second adjustment parameter is positively related to the steering wheel angle, and the third adjustment parameter is positively related to the acceleration vibration amplitude.

[0084] S203, adjusting the preset covariance matrix of the Kalman filter through the first adjustment parameter, the second adjustment parameter and the third adjustment parameter to obtain a dynamic covariance matrix.

[0085] Specifically, after obtaining the matrix adjustment parameter, the preset process noise covariance matrix in the preset covariance matrix is adjusted through the matrix adjustment parameter to obtain a dynamic process noise covariance matrix in the dynamic covariance matrix.

[0086] Further, the product of the first adjustment parameter, the second adjustment parameter, the third adjustment parameter and the preset process noise covariance matrix is taken as the dynamic process noise covariance matrix Q, wherein the dynamic process noise covariance matrix Q is shown by the following formula (1):

[0087] ;

[0088] Wherein, is the first adjustment parameter, is the second adjustment parameter, is the third adjustment parameter. The first adjustment parameter is negatively related to the brake pedal opening degree, the second adjustment parameter is positively related to the steering wheel angle, and the third adjustment parameter is positively related to the acceleration vibration amplitude.

[0089] Further, the preset measurement noise covariance matrix in the preset covariance matrix is adjusted through the matrix adjustment parameter to obtain a dynamic measurement noise covariance matrix in the dynamic covariance matrix. The product of the third adjustment parameter and the preset measurement noise covariance matrix is taken as the dynamic measurement noise covariance matrix R, wherein the dynamic measurement noise covariance matrix R is shown by the following formula (2):

[0090] ;

[0091] Further, after adjusting the preset process noise covariance matrix and the preset measurement noise covariance matrix in the Kalman filter according to the first adjustment parameter, the second adjustment parameter and the third adjustment parameter, the Kalman filter algorithm is implemented based on the adjusted dynamic process noise covariance matrix Q and the dynamic measurement noise covariance matrix R through a state transition matrix P and an observation matrix H to complete the adjustment of the Kalman filter, wherein the state transition matrix P is shown by the following formula (3):

[0092] ;

[0093] wherein, is used to indicate the calculation period of the Kalman filter running, and the observation matrix H is shown by the following formula (4):

[0094] ;

[0095] S204, Kalman filtering the sensor signal according to the dynamic covariance matrix to obtain the estimated information at the current time.

[0096] Specifically, after adjusting the preset covariance matrix of the Kalman filter to obtain the dynamic covariance matrix, the current longitudinal acceleration in the sensor signal and the first reference vehicle speed of the vehicle at the last time are input into the Kalman filter including the dynamic covariance matrix to obtain the estimated information at the current time output by the adjusted Kalman filter.

[0097] wherein, the estimated information includes an estimated speed, an estimated acceleration and an estimated deviation amount, and the estimated deviation value is used to indicate the deviation amount of the estimated acceleration.

[0098] S205, in the braking working condition, determining the second reference vehicle speed at the current time according to the estimated acceleration and the first reference vehicle speed at the last time.

[0099] Specifically, after obtaining the estimated information output by the adjusted Kalman filter, the running condition of the current vehicle is obtained, and if the running condition of the current vehicle is the braking working condition, a large deviation may be generated between the actual vehicle speed and the estimated speed of the current vehicle, and therefore the estimated speed needs to be further corrected.

[0100] Further, in the further correction process of the estimated speed, the second reference vehicle speed at the current time is calculated and obtained according to the estimated acceleration and the first reference vehicle speed at the last time, wherein the second reference vehicle speed at the current time is obtained by the following formula (5):

[0101] ;

[0102] wherein, a second reference vehicle speed at the current moment, a first reference vehicle speed at the last moment an estimated acceleration.

[0103] S206, determining a first weight and a second weight according to the second reference vehicle speed at the current moment, and determining a third weight and a fourth weight according to the brake pedal opening at the current moment.

[0104] Specifically, when the vehicle is at the end of the braking working condition, the second reference vehicle speed at the current moment calculated by formula (5) will cause accumulation of errors, so that the second reference vehicle speed at the current moment calculation result deviation increases, so the second reference vehicle speed at the current moment further correction is needed.

[0105] Further, after obtaining the second reference vehicle speed at the current moment , the first weight and the second weight are determined according to the second reference vehicle speed at the current moment , wherein the first weight is positively correlated with the second reference vehicle speed at the current moment , the sum of the second weight and the first weight is a preset maximum weight, the third weight is positively correlated with the brake pedal opening at the current moment, and the sum of the fourth weight and the third weight is a preset maximum weight.

[0106] S207, weighting the second reference vehicle speed at the current moment and the third reference vehicle speed according to the first weight and the second weight, to obtain a fourth reference vehicle speed.

[0107] Specifically, after obtaining the first weight and the second weight, the third reference vehicle speed at the next moment of the average acceleration estimation from the brake starting moment to the current moment is obtained , the first weight, the second weight and the third reference vehicle speed are used to calculate the fourth reference vehicle speed , wherein the fourth reference vehicle speed is calculated by formula (6) as follows:

[0108] ;

[0109] wherein, is the fourth reference vehicle speed, is the second reference vehicle speed at the current moment, is the third reference vehicle speed at the next moment of the average acceleration estimation from the brake starting moment to the current moment, is the first weight, is the second weight.

[0110] S208, weighting the fourth reference vehicle speed and the third reference vehicle speed according to the third weight and the fourth weight to obtain the first reference vehicle speed.

[0111] Specifically, when the brake pedal of the vehicle is pressed to be fully released, that is, when the vehicle changes from the braking condition to the non-braking condition, in order to avoid the sudden change of the reference vehicle speed, the fourth reference vehicle speed needs to be further corrected.

[0112] Further, after obtaining the fourth reference vehicle speed, according to the third weight, the fourth weight, the fourth reference vehicle speed and the third reference vehicle speed , the first reference vehicle speed at the current time is calculated and obtained , wherein the first reference vehicle speed at the current time is calculated and obtained by the following formula (7):

[0113] ;

[0114] wherein, is the first reference vehicle speed at the current time, is the fourth reference vehicle speed, is the third reference vehicle speed, is the third weight, is the fourth weight.

[0115] S209, according to the first reference vehicle speed at the current time and the first reference vehicle speed at the last time, determining the acceleration of the vehicle from the last time to the current time as the differential acceleration.

[0116] Specifically, after obtaining the first reference vehicle speed at the current time and the estimated acceleration in the estimation information, according to the first reference vehicle speed at the current time and the first reference vehicle speed of the vehicle at the last time , the differential acceleration is calculated and obtained, wherein the differential acceleration is calculated and obtained by the following formula (8):

[0117] ;

[0118] wherein, is the differential acceleration, is the first reference vehicle speed at the current time, is the first reference vehicle speed of the vehicle at the last time, is used to indicate the calculation period of the Kalman filter.

[0119] S210, weighting the difference acceleration and the estimated acceleration according to the third weight and the fourth weight to obtain a second reference acceleration.

[0120] Specifically, after obtaining the difference acceleration , a second reference acceleration is calculated according to a third weight, a fourth weight, an estimated acceleration and the difference acceleration , wherein the third weight is a weight of the difference acceleration, the fourth weight is a weight of the estimated acceleration, and the second reference acceleration is calculated by the following formula (9):

[0121] ;

[0122] wherein, the second reference acceleration , is the estimated acceleration, and the fourth weight.

[0123] S211, determining a fifth weight and a sixth weight according to a current accelerator pedal opening degree, and weighting the difference acceleration and the second reference acceleration according to the fifth weight and the sixth weight to obtain the first reference acceleration.

[0124] Specifically, after obtaining the second reference acceleration , a fifth weight and a sixth weight are determined according to a current accelerator pedal opening degree in the sensor signal, the fifth weight is positively correlated with the current accelerator pedal opening degree, and the sum of the fifth weight and the sixth weight is a preset maximum weight.

[0125] Further, the first reference acceleration is calculated according to the fifth weight, the sixth weight, the second reference acceleration and the difference acceleration , wherein the fifth weight is a weight of the difference acceleration , the sixth weight is a weight of the corrected acceleration, i.e., the second reference acceleration, and the first reference acceleration is calculated by the following formula (10):

[0126] ;

[0127] wherein, the first reference acceleration, , the second reference acceleration, and the sixth weight. By correcting the estimated acceleration in the estimated information, a corrected first reference acceleration is obtained , avoid the error caused by the slope, impact and other reasons in the process of smooth driving, resulting in the estimated acceleration mutation. Based on the obtained first reference acceleration and the first reference vehicle speed at the current time, the vehicle is controlled.

[0128] S212, in the non-braking condition, the non-driving axle speed is determined according to the estimated speed of the wheel on the non-driving axle, and the non-driving axle speed is taken as the first reference speed at the current time.

[0129] Specifically, if the current vehicle driving condition is non-braking condition, in the speed signal of each wheel of the sensor signal, the speed signal of the two wheels on the non-driving axle such as the front axle is obtained, the speed signal of the non-driving axle wheel is input into the Kalman filter including the dynamic covariance matrix, the estimated speed corresponding to the two wheels is obtained, and the average of the estimated speed corresponding to the two wheels is taken as the first reference speed at the current time. Based on the obtained first reference speed and the estimated acceleration at the current time, the vehicle is controlled.

[0130] The vehicle state estimation method provided by the embodiment of the application adjusts the Kalman filter through the sensor signal of the vehicle at the current time, and performs Kalman filtering on the currently obtained sensor signal through the adjusted Kalman filter to obtain the estimation information including the estimated speed and the estimated acceleration at the current time. In the braking condition, the estimated speed and the estimated acceleration are corrected according to the estimated acceleration and the first reference speed at the last time, to obtain the first reference speed and the first reference acceleration at the current time, and the vehicle is controlled based on the first reference acceleration and the first reference speed at the current time. The Kalman filter used in the application is dynamically related to the brake pedal opening, the steering wheel angle and the acceleration vibration amplitude, which can better reflect the current condition of the vehicle, and the reference speed is corrected multiple times in the braking condition, which can help to improve the accuracy of filtering, and further improve the accuracy of the acceleration and the reference speed.

[0131] Figure 4 The structure diagram of the vehicle state estimation device provided by the application is shown in Figure 5 The device 40 comprises:

[0132] The acquisition module 401 is configured to acquire the sensor signal sent by the sensor on the vehicle at the current time, and the sensor signal comprises an acceleration signal.

[0133] The first processing module 402 is configured to adjust a Kalman filter according to a brake pedal opening degree, a steering wheel rotation angle and an acceleration vibration amplitude of the vehicle at a current time, to perform Kalman filtering on the sensor signal by using the adjusted Kalman filter, and to obtain estimated information at the current time, wherein the estimated information comprises an estimated acceleration, and the acceleration vibration amplitude is a mean square error of a plurality of estimated accelerations in a historical time period.

[0134] The second processing module 403 is configured to determine a second reference vehicle speed at the current time according to the estimated acceleration and a first reference vehicle speed at a previous time in a braking condition, and to correct the second reference vehicle speed by using a third reference vehicle speed at a next time estimated by using an average acceleration between a brake starting time and the current time, to obtain the first reference vehicle speed at the current time, wherein the average acceleration is an average value of the first reference acceleration between the brake starting time and the current time.

[0135] The control module 404 is configured to correct the estimated acceleration according to the first reference vehicle speed at the current time and the first reference vehicle speed at the previous time, to obtain a first reference acceleration.

[0136] In a possible implementation, the second processing module 403 is specifically configured to determine a first weight and a second weight according to the second reference vehicle speed at the current time, and to determine a third weight and a fourth weight according to a brake pedal opening degree at the current time, wherein the first weight is positively correlated with the second reference vehicle speed at the current time, a sum of the second weight and the first weight is a preset maximum weight, the third weight is positively correlated with the brake pedal opening degree at the current time, and a sum of the fourth weight and the third weight is the preset maximum weight.

[0137] The second processing module 403 is configured to weight the second reference vehicle speed at the current time and the third reference vehicle speed according to the first weight and the second weight, to obtain a fourth reference vehicle speed, wherein the first weight is used as a weight of the second reference vehicle speed, and the second weight is used as a weight of the third reference vehicle speed.

[0138] The second processing module 403 is configured to weight the fourth reference vehicle speed and the third reference vehicle speed according to the third weight and the fourth weight, to obtain the first reference vehicle speed at the current time, wherein the third weight is used as a weight of the fourth reference vehicle speed, and the fourth weight is used as a weight of the third reference vehicle speed.

[0139] In a possible implementation, the second processing module 403 is specifically configured to determine an acceleration of the vehicle between the previous time and the current time as a differential acceleration according to the first reference vehicle speed at the current time and the first reference vehicle speed at the previous time.

[0140] weighting the differential acceleration and the estimated acceleration according to the third weight and the fourth weight, to obtain a second reference acceleration, the third weight being a weight of the differential acceleration, and the fourth weight being a weight of the estimated acceleration;

[0141] determining a fifth weight and a sixth weight according to the accelerator pedal opening degree at the current moment, the fifth weight being positively correlated with the accelerator pedal opening degree at the current moment, and the sum of the fifth weight and the sixth weight being the preset maximum weight;

[0142] weighting the differential acceleration and the second reference acceleration according to the fifth weight and the sixth weight, to obtain the first reference acceleration, the fifth weight being a weight of the differential acceleration, and the sixth weight being a weight of the corrected acceleration.

[0143] In a possible implementation, the first processing module 402 is specifically configured to determine a matrix adjustment parameter of the Kalman filter according to the brake pedal opening degree, the steering wheel angle and the acceleration vibration amplitude at the current moment, respectively, the matrix adjustment parameter including a first adjustment parameter, a second adjustment parameter and a third adjustment parameter, the first adjustment parameter being negatively correlated with the brake pedal opening degree, the second adjustment parameter being positively correlated with the steering wheel angle, and the third adjustment parameter being positively correlated with the acceleration vibration amplitude.

[0144] adjust the preset covariance matrix of the Kalman filter by using the first adjustment parameter, the second adjustment parameter and the third adjustment parameter, to obtain a dynamic covariance matrix.

[0145] perform Kalman filtering on the sensor signal according to the dynamic covariance matrix, to obtain estimated information at the current moment.

[0146] In a possible implementation, the preset covariance matrix includes a preset process noise covariance matrix and a preset measurement noise covariance matrix, and the dynamic covariance matrix includes a dynamic process noise covariance matrix and a dynamic measurement noise covariance matrix, and the first processing module 402 is configured to adjust the preset covariance matrix of the Kalman filter by using the first adjustment parameter, the second adjustment parameter and the third adjustment parameter, to obtain the dynamic covariance matrix, including:

[0147] multiplying the first adjustment parameter, the second adjustment parameter, the third adjustment parameter and the preset process noise covariance matrix, to obtain the dynamic process noise covariance matrix.

[0148] multiplying the third adjustment parameter and the preset measurement noise covariance matrix, to obtain the dynamic measurement noise covariance matrix.

[0149] In a possible implementation, the first processing module 402 is further configured to, in the non-braking working condition, determine a non-drive axle speed according to the estimated speeds of the wheels on the non-drive axle, and take the non-drive axle speed as the first reference speed at the current time, the non-drive axle speed being an average of the estimated speeds of the two wheels of the non-drive axle.

[0150] The vehicle state estimation apparatus provided in this embodiment can execute the method provided in the method embodiment, and has similar implementation principles and technical effects. Details are not described herein again.

[0151] Figure 5 A structural schematic diagram of an electronic device provided in the present application is shown in FIG. 1. As shown in the figure, the electronic device 50 provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the device 50 further includes a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected through a bus 504. ​

[0152] In the implementation process, the at least one processor 501 executes the computer-executed instructions stored in the memory 502, so that the at least one processor 501 executes the method described above.

[0153] The specific implementation process of the processor 501 can refer to the method embodiments described above, and has similar implementation principles and technical effects. Details are not described herein again.

[0154] In the above embodiments, it should be understood that the processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), etc. The general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the method disclosed in the present application can be directly embodied as execution completed by a hardware processor, or executed by a combination of hardware and software modules in the processor.

[0155] The memory can include a random access memory (RAM), and can also include a non-volatile memory (NVM), for example, at least one disk memory.

[0156] ​The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, the bus in the drawings of the present application does not limit to only one bus or one type of bus.

[0157] The present application also provides a computer program product, comprising a computer program, which, when executed by a processor, implements the method described above.

[0158] The present application also provides a computer readable storage medium, which stores computer execution instructions, and when a processor executes the computer execution instructions, the method described above is implemented.

[0159] The readable storage medium described above can be realized by any type of volatile or non-volatile storage device or their combination, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special purpose computer.

[0160] An exemplary readable storage medium is coupled to the processor, so that the processor can read information from the readable storage medium, and can write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in the device.

[0161] The division of units is only a logical functional division, and in actual implementation, there can be another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0162] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, i.e., may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0163] In addition, each functional unit in various embodiments of the application can be integrated into one processing unit, or each unit can exist physically, or two or more units can be integrated into one unit.

[0164] If the function is realized in the form of a software function 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 solutions of the application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the various embodiment methods of the application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0165] Those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction related hardware. The aforementioned program can be stored in a computer readable storage medium. The program executes to perform the steps of the above-mentioned method embodiments; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, and various media that can store program codes.

[0166] Finally, it should be noted that those skilled in the art, after considering the specification and practicing the application disclosed herein, will easily think of other embodiments of the application. The application is intended to cover any variations, uses, or adaptations of the application that follow the general principles of the application and include common knowledge or conventional technical means in the art that are not disclosed by the application, and is not limited to the precise structure described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the application is only limited by the appended claims.

Claims

1. A vehicle state estimation method characterized by comprising: The method comprises: obtaining a sensor signal sent by a sensor on a vehicle at a current time, the sensor signal comprising an acceleration signal; adjusting a Kalman filter according to a brake pedal opening, a steering wheel angle and an acceleration vibration amplitude of the vehicle at the current time, so as to perform Kalman filtering on the sensor signal by the adjusted Kalman filter to obtain estimation information at the current time, the estimation information comprising an estimated acceleration, and the acceleration vibration amplitude being a mean square error of a plurality of estimated accelerations in a historical time period; in a braking condition, determining a second reference vehicle speed at the current time according to the estimated acceleration and a first reference vehicle speed at a previous time, and correcting the second reference vehicle speed by a third reference vehicle speed at a next time estimated by an average acceleration between a brake starting time and the current time, so as to obtain the first reference vehicle speed at the current time, and the average acceleration being an average value of a first reference acceleration between the brake starting time and the current time; correcting the estimated acceleration according to the first reference vehicle speed at the current time and the first reference vehicle speed at the previous time to obtain a first reference acceleration.

2. The method of claim 1, wherein, The correction of the second reference vehicle speed by the third reference vehicle speed at the next time estimated by the average acceleration between the brake starting time and the current time to obtain the first reference vehicle speed at the current time comprises: determining a first weight and a second weight according to the second reference vehicle speed at the current time, and determining a third weight and a fourth weight according to a brake pedal opening at the current time, the first weight being positively correlated with the second reference vehicle speed at the current time, the sum of the second weight and the first weight being a preset maximum weight, the third weight being positively correlated with the brake pedal opening at the current time, and the sum of the fourth weight and the third weight being the preset maximum weight; weighting the second reference vehicle speed at the current time and the third reference vehicle speed according to the first weight and the second weight to obtain a fourth reference vehicle speed, the first weight being a weight of the second reference vehicle speed and the second weight being a weight of the third reference vehicle speed; weighting the fourth reference vehicle speed and the third reference vehicle speed according to the third weight and the fourth weight to obtain the first reference vehicle speed, the third weight being a weight of the fourth reference vehicle speed and the fourth weight being a weight of the third reference vehicle speed.

3. The method of claim 2, wherein, The correction of the estimated acceleration according to the first reference vehicle speed at the current time and the first reference vehicle speed at the previous time to obtain a first reference acceleration comprises: determining an acceleration of the vehicle from the previous time to the current time as a differential acceleration according to the first reference vehicle speed at the current time and the first reference vehicle speed at the previous time; weighting the differential acceleration and the estimated acceleration according to the third weight and the fourth weight to obtain a second reference acceleration, the third weight being a weight of the differential acceleration and the fourth weight being a weight of the estimated acceleration. determining a fifth weight and a sixth weight according to an accelerator pedal opening degree at a current time, the fifth weight being positively correlated with the accelerator pedal opening degree at the current time, and a sum of the fifth weight and the sixth weight being the preset maximum weight; weighting the differential acceleration and the second reference acceleration according to the fifth weight and the sixth weight to obtain the first reference acceleration, the fifth weight being used as a weight of the differential acceleration, and the sixth weight being used as a weight of the second reference acceleration.

4. The method of claim 1, wherein, adjusting a Kalman filter according to a brake pedal opening degree, a steering wheel angle and an acceleration vibration amplitude of the vehicle at a current time, to perform Kalman filtering on the sensor signal by using the adjusted Kalman filter to obtain estimation information at the current time, including: determining a matrix adjustment parameter of the Kalman filter according to the brake pedal opening degree, the steering wheel angle and the acceleration vibration amplitude at the current time, the matrix adjustment parameter including a first adjustment parameter, a second adjustment parameter and a third adjustment parameter, the first adjustment parameter being negatively correlated with the brake pedal opening degree, the second adjustment parameter being positively correlated with the steering wheel angle, and the third adjustment parameter being positively correlated with the acceleration vibration amplitude; adjusting a preset covariance matrix of the Kalman filter by using the first adjustment parameter, the second adjustment parameter and the third adjustment parameter to obtain a dynamic covariance matrix; performing Kalman filtering on the sensor signal according to the dynamic covariance matrix to obtain estimation information at the current time.

5. The method of claim 4, wherein, the preset covariance matrix including a preset process noise covariance matrix and a preset measurement noise covariance matrix, and the dynamic covariance matrix including a dynamic process noise covariance matrix and a dynamic measurement noise covariance matrix; the adjusting of the preset covariance matrix of the Kalman filter by using the first adjustment parameter, the second adjustment parameter and the third adjustment parameter to obtain the dynamic covariance matrix including: multiplying the first adjustment parameter, the second adjustment parameter, the third adjustment parameter and the preset process noise covariance matrix to obtain the dynamic process noise covariance matrix; and multiplying the third adjustment parameter and the preset measurement noise covariance matrix to obtain the dynamic measurement noise covariance matrix.

6. The method according to any one of claims 1 to 5, characterized in that, the sensor signal further including a vehicle speed signal of each wheel, and the estimation information further including an estimated vehicle speed of each wheel, and the method further including: in a non-braking condition, determining a non-driving axle vehicle speed according to an estimated vehicle speed of a wheel on a non-driving axle, and taking the non-driving axle vehicle speed as a first reference vehicle speed at the current time, the non-driving axle vehicle speed being an average of estimated vehicle speeds of two wheels of the non-driving axle.

7. A vehicle state estimation device characterized by comprising: including: an acquisition module, configured to acquire a sensor signal sent by a sensor on a vehicle at a current time, the sensor signal including an acceleration signal; The first processing module is configured to adjust a Kalman filter according to a brake pedal opening degree, a steering wheel angle and an acceleration vibration amplitude of the vehicle at a current time, to perform Kalman filtering on the sensor signal by using the adjusted Kalman filter, and to obtain estimated information at the current time, wherein the estimated information comprises an estimated acceleration, and the acceleration vibration amplitude is a mean square error of a plurality of estimated accelerations in a historical time period. The second processing module is configured to determine a second reference vehicle speed at the current time according to the estimated acceleration and a first reference vehicle speed at a previous time in a braking condition, and to correct the second reference vehicle speed by a third reference vehicle speed at a next time estimated by an average acceleration between a brake starting time and the current time, to obtain the first reference vehicle speed at the current time, wherein the average acceleration is an average value of the first reference acceleration between the brake starting time and the current time. The control module is configured to correct the estimated acceleration according to the first reference vehicle speed at the current time and the first reference vehicle speed at the previous time, to obtain a first reference acceleration.

8. An electronic device, comprising: The method comprises: a processor and a memory connected to the processor in communication; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are executed by the processor to implement the method according to any one of claims 1 to 6.

10. A computer program product, characterised in that, The computer program is executed by the processor to implement the method according to any one of claims 1 to 6.

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