Vehicle state estimation method and device, storage medium and product

Abstract

The invention provides a vehicle state estimation method and device, a storage medium and a product. According to the method, a Kalman filter is adjusted through a sensor signal of a vehicle at the current moment, Kalman filtering is conducted on the current sensor signal through the adjusted Kalman filter, and estimation information including the estimated vehicle speed and the estimated acceleration at the current moment is obtained. And under the braking working condition, the estimated vehicle speed and the estimated acceleration are corrected according to the estimated acceleration and the first reference vehicle speed at the previous moment, the first reference vehicle speed and the first reference acceleration at the current moment are obtained, and the vehicle is controlled based on the first reference acceleration and the first reference vehicle speed at the current moment. Therefore, according to the adjusted Kalman filter, the corresponding estimated acceleration and the estimated vehicle speed are obtained, and corresponding correction processing is performed on the estimated acceleration and the estimated vehicle speed of different vehicle driving working conditions, so that the accuracy of obtaining the acceleration and the reference vehicle speed in the vehicle state information is improved.

Description

Technical Field

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

[0002] The vehicle's reference speed and longitudinal acceleration, as vehicle status data, have an important impact on vehicle control.

[0003] The existing solutions for obtaining reference vehicle speed and longitudinal acceleration usually determine the corresponding reference vehicle speed and longitudinal acceleration based on a pre-established prediction model and various sensor data of the vehicle. However, the acquisition solution based on the prediction model does not take into account the complexity of the vehicle's driving conditions, and thus cannot accurately obtain the reference vehicle speed and longitudinal acceleration. Summary of the invention

[0004] The present application provides a vehicle state estimation method, device, storage medium and product to achieve the effect of solving the defects of existing solutions that do not consider the complexity of vehicle driving conditions and cannot accurately obtain reference vehicle speed and longitudinal acceleration.

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

[0006] Acquire a sensor signal sent by a sensor on the vehicle at a current moment, wherein the sensor signal includes: an acceleration signal;

[0007] According to the brake pedal opening, steering wheel angle and acceleration vibration amplitude of the vehicle at the current moment, the Kalman filter is adjusted to perform Kalman filtering on the sensor signal through the adjusted Kalman filter to obtain estimated information at the current moment, wherein the estimated information includes: estimated acceleration, the acceleration vibration amplitude is the mean square error of multiple estimated accelerations in a historical time period;

[0008] Under the braking condition, a second reference vehicle speed at the current moment is determined according to the estimated acceleration and the first reference vehicle speed at the previous moment, and a third reference vehicle speed at the next moment is estimated by the average acceleration between the braking start moment and the current moment, and the second reference vehicle speed is corrected to obtain the first reference vehicle speed at the current moment, wherein the average acceleration is the average value of the first reference acceleration between the braking start moment and the current moment;

[0009] The estimated acceleration is corrected according to the first reference vehicle speed at the current moment and the first reference vehicle speed at the previous moment to obtain a first reference acceleration.

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

[0011] According to the first weight and the second weight, the second reference vehicle speed at the current moment and the third reference vehicle speed are weighted to obtain a fourth reference vehicle speed, the first weight being used as the weight of the second reference vehicle speed, and the second weight being used as the weight of the third reference vehicle speed;

[0012] According to the third weight and the fourth weight, the fourth reference vehicle speed and the third reference vehicle speed are weighted to obtain the first reference vehicle speed, 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.

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

[0014] According to the third weight and the fourth weight, the differential acceleration and the estimated acceleration are weighted to obtain a second reference acceleration, the third weight being used as the weight of the differential acceleration, and the fourth weight being used as the weight of the estimated acceleration;

[0015] Determining a fifth weight and a sixth weight according to the accelerator pedal opening at the current moment, wherein the fifth weight is positively correlated to the accelerator pedal opening at the current moment, and the sum of the sixth weight and the fifth weight is the preset maximum weight;

[0016] The differential acceleration and the second reference acceleration are weighted according to the fifth weight and the sixth weight to obtain the first reference acceleration, the fifth weight is used as the weight of the differential acceleration, and the sixth weight is used as the weight of the corrected acceleration.

[0017] In a possible implementation manner, according to the brake pedal opening, steering wheel angle and acceleration vibration amplitude at the current moment, the matrix adjustment parameters of the Kalman filter are respectively determined, 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] Adjusting 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;

[0019] Kalman filtering is performed on the sensor signal according to the dynamic covariance matrix to obtain estimation information at the current moment.

[0020] In a possible implementation manner, 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 ​​used as the dynamic process noise covariance matrix;

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

[0023] In a possible implementation manner, the sensor signal further includes a vehicle speed signal of each wheel, and the estimation information further includes: an estimated vehicle speed of each wheel;

[0024] Under non-braking conditions, the non-driven axle speed is determined based on the estimated speed of the wheels on the non-driven axle, and the non-driven axle speed is used as the first reference speed at the current moment. The non-driven axle speed is the average of the estimated speeds of the two wheels of the non-driven axle.

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

[0026] An acquisition module, used to acquire a sensor signal sent by a sensor on the vehicle at a current moment, wherein the sensor signal includes: an acceleration signal;

[0027] A first processing module is used to adjust the Kalman filter according to the brake pedal opening, steering wheel angle and acceleration vibration amplitude of the vehicle at the current moment, so as to perform Kalman filtering on the sensor signal through the adjusted Kalman filter to obtain estimated information at the current moment, wherein the estimated information includes: estimated acceleration, and the acceleration vibration amplitude is the mean square error of multiple estimated accelerations in a historical time period;

[0028] a second processing module, for determining, under a braking condition, a second reference vehicle speed at a current moment according to the estimated acceleration and the first reference vehicle speed at a previous moment, and correcting the second reference vehicle speed by estimating a third reference vehicle speed at a next moment by an average acceleration between the braking start moment and the current moment, to obtain the first reference vehicle speed at the current moment, wherein the average acceleration is an average value of the first reference acceleration between the braking start moment and the current moment;

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

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

[0031] According to the first weight and the second weight, the second reference vehicle speed at the current moment and the third reference vehicle speed are weighted to obtain a fourth reference vehicle speed, the first weight being used as the weight of the second reference vehicle speed, and the second weight being used as the weight of the third reference vehicle speed;

[0032] According to the third weight and the fourth weight, the fourth reference vehicle speed and the third reference vehicle speed are weighted to obtain the first reference vehicle speed, 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 the acceleration of the vehicle from the previous moment to the current moment according to the first reference vehicle speed at the current moment and the first reference vehicle speed at the previous moment, as the differential acceleration;

[0034] According to the third weight and the fourth weight, the differential acceleration and the estimated acceleration are weighted to obtain a second reference acceleration, the third weight being used as the weight of the differential acceleration, and the fourth weight being used as the weight of the estimated acceleration;

[0035] Determining a fifth weight and a sixth weight according to the accelerator pedal opening at the current moment, wherein the fifth weight is positively correlated to the accelerator pedal opening at the current moment, and the sum of the sixth weight and the fifth weight is the preset maximum weight;

[0036] The differential acceleration and the second reference acceleration are weighted according to the fifth weight and the sixth weight to obtain the first reference acceleration, the fifth weight is used as the weight of the differential acceleration, and the sixth weight is used as the weight of the corrected acceleration.

[0037] In a possible implementation, the first processing module is specifically used to determine the matrix adjustment parameters of the Kalman filter according to the brake pedal opening, steering wheel angle and acceleration vibration amplitude at the current moment, respectively, the matrix adjustment parameters including: 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;

[0038] Adjusting 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] Kalman filtering is performed on the sensor signal according to the dynamic covariance matrix to obtain estimation 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, 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 used to use the 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] The product of the third adjustment parameter and the preset measurement noise covariance matrix is ​​used as the dynamic measurement noise covariance matrix.

[0042] In one possible implementation, the first processing module is also used to determine the non-drive shaft speed based on the estimated speed of the wheels on the non-drive shaft under non-braking conditions, and use the non-drive shaft speed as the first reference speed at the current moment, and the non-drive shaft speed is the average of the estimated speeds of the two wheels of the non-drive shaft.

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

[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, in which computer-readable storage medium is stored computer-executable instructions, and the computer-executable instructions are used to implement the method described above when executed by a processor.

[0047] In a fifth aspect, an embodiment of the present application provides a computer program product, characterized in that it includes a computer program that implements the above method when executed by a processor.

[0048] The vehicle state estimation method, device, storage medium and product provided in the embodiment of the present application adjust the Kalman filter through the sensor signal of the vehicle at the current moment, and perform Kalman filtering on the currently acquired sensor signal through the adjusted Kalman filter to obtain the estimated information including the estimated vehicle speed and the estimated acceleration at the current moment. Under the braking condition, the estimated vehicle speed and the estimated acceleration are corrected according to the estimated acceleration and the first reference vehicle speed at the previous moment, and the first reference vehicle speed and the first reference acceleration at the current moment are obtained, so that the vehicle is controlled based on the first reference acceleration and the first reference vehicle speed at the current moment. The Kalman filter used in the present 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 vehicle speed is corrected multiple times under the braking condition, which can help improve the accuracy of filtering, thereby improving the accuracy of acceleration and reference vehicle speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings, which are incorporated in and constitute a part of this 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 A schematic diagram of the basic framework provided for this application;

[0051] Figure 2Schematic diagram of the vehicle state estimation method provided in this application Figure 1 ;

[0052] Figure 3 Schematic diagram of the vehicle state estimation method provided in this application Figure 2 ;

[0053] Figure 4 A schematic diagram of the structure of the vehicle state estimation device provided in this application;

[0054] Figure 5 A schematic diagram of the structure of the electronic device provided in this application.

[0055] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0056] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0057] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in one or more embodiments of this specification are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards, and corresponding operation entrances shall be provided for users to choose to authorize or refuse.

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

[0059] When realizing the control of commercial vehicles, such as realizing the braking force distribution of the vehicle, anti-lock braking and other functions, accurate vehicle status data is the basis for realizing the control of commercial vehicles. Among them, the reference speed and longitudinal acceleration of the vehicle are important reference data in the vehicle status data. Only by obtaining the accurate reference speed and longitudinal acceleration of the vehicle in the vehicle status data can the control parameters be accurately calculated, and the control of the commercial vehicle can be realized based on the accurate control parameters.

[0060] The existing schemes for obtaining reference vehicle speed and longitudinal acceleration include a method for determining the reference vehicle speed and acceleration through a pre-established prediction model and a method for determining the reference vehicle speed and acceleration through a fuzzy logic algorithm. Among them, for the method for determining the reference vehicle speed and acceleration through a pre-established prediction model, the obtained data of each vehicle sensor is input into the pre-established prediction model to obtain the reference vehicle speed and acceleration of the next moment of the output; for the method for determining the reference vehicle speed and acceleration through a fuzzy logic algorithm, it is necessary to synthesize the obtained data of each vehicle sensor through a fuzzy reasoning mechanism, and determine the reference vehicle speed and acceleration of the vehicle according to the processing results.

[0061] A study of the working process of the above-mentioned existing schemes found that when obtaining the vehicle's reference speed and acceleration through the prediction model, only the vehicle operating conditions with stable speed changes are considered, and the vehicle speed changes under special conditions such as emergency braking are not considered, so that the vehicle reference speed calculated in the prediction simulation results cannot effectively reflect the actual speed; when the fuzzy logic algorithm obtains the vehicle's reference speed and acceleration, the wheel speed credibility fuzzy rules of each wheel are not established when the vehicle is in different driving states such as acceleration, deceleration, and coasting, that is, the complexity of the vehicle operating conditions is not considered, and thus the reference speed and longitudinal acceleration cannot be accurately obtained.

[0062] In view of this, the present application provides a vehicle state estimation method, which adjusts the Kalman filter through the sensor signal of the vehicle at the current moment, so as to perform Kalman filtering on the currently acquired sensor signal through the adjusted Kalman filter, and obtain the estimated information including the estimated vehicle speed and the estimated acceleration at the current moment. Under the braking condition, the estimated acceleration and the estimated acceleration are corrected according to the estimated acceleration and the first reference vehicle speed at the previous moment, and the first reference acceleration and the first reference vehicle speed at the current moment are obtained, and the vehicle is controlled based on the first reference acceleration and the first reference vehicle speed at the current moment. The Kalman filter used in the present 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 vehicle speed is corrected multiple times under the braking condition, which can help improve the accuracy of filtering, thereby improving the accuracy of acceleration and reference vehicle speed.

[0063] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0064] Figure 1 The basic framework diagram provided for this application is as follows: Figure 2 Schematic diagram of the vehicle state estimation method provided in this application Figure 1 , combined with Figure 1 and Figure 2 As shown, the method includes:

[0065] S101. Acquire a sensor signal sent by a sensor on a vehicle at a current moment, wherein the sensor signal includes an acceleration signal.

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

[0067] S102. Adjust the Kalman filter according to the brake pedal opening, steering wheel angle and acceleration vibration amplitude of the vehicle at the current moment, so as to perform Kalman filtering on the sensor signal through the adjusted Kalman filter to obtain estimated information at the current moment, wherein the estimated information includes: estimated acceleration, and the acceleration vibration amplitude is the mean square error of multiple estimated accelerations in a historical time period.

[0068] Specifically, after obtaining the sensor signal corresponding to the current vehicle, the longitudinal acceleration of the vehicle at the current moment and the first reference speed of the vehicle at the previous moment are processed by the Kalman filter. Then, 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 to adjust the Kalman filter.

[0069] Furthermore, the longitudinal acceleration of the vehicle at the current moment and the reference speed of the vehicle at the previous moment are input into the adjusted Kalman filter for processing to obtain estimated information including the estimated speed and estimated acceleration of each wheel at the current moment.

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

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

[0072] Furthermore, if the vehicle is in a braking condition, 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 in the final stage of the braking condition, the estimated vehicle speed also needs to be corrected based on the average acceleration; if the vehicle is in a non-braking condition, the estimated vehicle speed is corrected based on the non-drive shaft speed to obtain the first reference vehicle speed corresponding to the correction at the current moment.

[0073] S104. Correct the estimated acceleration according to the first reference vehicle speed at the current moment and the first reference vehicle speed at the previous moment to obtain a first reference acceleration.

[0074] Specifically, the estimated vehicle speed is corrected under different driving conditions of the vehicle. After the corrected first reference vehicle speed corresponding to the current moment is obtained, in order to avoid errors caused by slope and impact during the smooth driving of the vehicle, which may cause sudden changes in the estimated acceleration, the estimated acceleration needs to be corrected based on the first reference vehicle speed at the current moment and the first reference vehicle speed at the previous moment.

[0075] Further, based on the first reference vehicle speed at the current moment and the first reference vehicle speed at the previous moment, combined with the correction coefficients corresponding to the brake pedal opening and the accelerator pedal opening, the estimated acceleration in the estimation 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 embodiment of the present application adjusts the Kalman filter through the sensor signal of the vehicle at the current moment, so as to perform Kalman filtering on the currently acquired sensor signal through the adjusted Kalman filter, and obtain the estimated information including the estimated vehicle speed and the estimated acceleration at the current moment. Under the braking condition, the estimated acceleration and the estimated acceleration are corrected according to the estimated acceleration and the first reference vehicle speed at the previous moment, and the first reference acceleration and the first reference vehicle speed at the current moment are obtained, and the vehicle is controlled based on the first reference acceleration and the first reference vehicle speed at the current moment. The Kalman filter used in the present 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 vehicle speed is corrected multiple times under the braking condition, which can help improve the accuracy of filtering, thereby improving the accuracy of acceleration and reference vehicle speed.

[0077] Figure 3 Schematic diagram of the vehicle state estimation method provided in this application Figure 2 ,like Figure 3 As shown, in this embodiment Figure 1 Based on the embodiment, a vehicle state estimation method is described in detail, and the method includes:

[0078] S201. Acquire a sensor signal sent by a sensor on the vehicle at the current moment.

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

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

[0081] Specifically, after obtaining 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 moment and the first reference speed of the vehicle at the previous moment can be filtered based on the adjusted Kalman filter.

[0082] Among them, 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 a preset brake pedal opening curve according to the corresponding brake pedal opening, for the second adjustment parameter, the corresponding adjustment parameter is obtained in a 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 a preset vibration amplitude curve according to the corresponding acceleration vibration amplitude. 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.

[0084] S203. 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.

[0085] Specifically, after the matrix adjustment parameters are obtained, the preset process noise covariance matrix in the preset covariance matrix is ​​adjusted by the matrix adjustment parameters to obtain the 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 ​​used 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] in, is the first adjustment parameter, is the second adjustment parameter, is the 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.

[0089] Furthermore, the preset measurement noise covariance matrix in the preset covariance matrix is ​​adjusted by using the matrix adjustment parameter to obtain the dynamic measurement noise covariance matrix in the dynamic covariance matrix. The product of the preset measurement noise covariance matrix is ​​used as the dynamic measurement noise covariance matrix R, wherein the dynamic measurement noise covariance matrix R is expressed 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 through the state transfer matrix P and the observation matrix H based on the adjusted dynamic process noise covariance matrix Q and the dynamic measurement noise covariance matrix R to complete the adjustment of the Kalman filter, wherein the state transfer matrix P is shown by the following formula (3):

[0092] ;

[0093] in, It is used to indicate the calculation cycle of the Kalman filter operation. The observation matrix H is shown by the following formula (4):

[0094] ;

[0095] S204: Perform Kalman filtering on the sensor signal according to the dynamic covariance matrix to obtain estimation information at the current moment.

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

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

[0098] S205. Under a braking condition, determine a second reference vehicle speed at a current moment according to the estimated acceleration and the first reference vehicle speed at a previous moment.

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

[0100] Furthermore, when the estimated vehicle speed is further corrected, the second reference vehicle speed at the current moment is calculated based on the estimated acceleration and the first reference vehicle speed at the previous moment, wherein the second reference vehicle speed at the current moment is , obtained by the following formula (5):

[0101] ;

[0102] in, is the second reference speed at the current moment, The first reference speed at the previous moment To estimate the acceleration.

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

[0104] Specifically, when the vehicle is in the final stage of the braking condition, the second reference vehicle speed is calculated by formula (5): , will cause the accumulation of errors, making the second reference speed The calculation result deviation increases, so the second reference speed needs to be Further corrections.

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

[0106] S207. Weight the second reference vehicle speed and the third reference vehicle speed at the current moment 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 estimated from the braking start moment to the current moment is obtained. , according to the first weight, the second weight and the third reference speed , calculate and obtain the fourth reference vehicle speed , where for the fourth reference speed , calculated by the following formula (6):

[0108] ;

[0109] in, is the fourth reference speed, is the second reference speed at the current moment, The third reference vehicle speed at the next moment is estimated from the average acceleration from the braking start moment to the current moment, is the first weight, is the second weight.

[0110] S208. 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.

[0111] Specifically, when the brake pedal of the vehicle is depressed and then completely released, that is, when the vehicle changes from a braking condition to a non-braking condition, in order to avoid a sudden change in 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 , calculate and obtain the first reference vehicle speed at the current moment , where the first reference speed at the current moment is , calculated by the following formula (7):

[0113] ;

[0114] in, is the first reference speed at the current moment, is the fourth reference speed, is the third reference speed, is the third weight, The fourth weight.

[0115] S209: Determine the acceleration of the vehicle from the previous moment to the current moment according to the first reference vehicle speed at the current moment and the first reference vehicle speed at the previous moment as a differential acceleration.

[0116] Specifically, after obtaining the first reference vehicle speed at the current moment and the estimated acceleration in the estimation information, according to the first reference vehicle speed at the current moment and the first reference speed of the vehicle at the previous moment , calculate the differential acceleration , where for the differential acceleration , calculated by the following formula (8):

[0117] ;

[0118] in, To obtain the differential acceleration, is the first reference speed at the current moment, is the first reference speed of the vehicle at the previous moment, Used to indicate the calculation cycle of the Kalman filter operation.

[0119] S210. Weight the differential 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 differential acceleration Then, according to the third weight, the fourth weight, the estimated acceleration and the differential acceleration , calculate and obtain the second reference acceleration , where the third weight is used as the weight of the differential acceleration, and the fourth weight is used as the weight of the estimated acceleration. , calculated by the following formula (9):

[0121] ;

[0122] in, is the second reference acceleration , To estimate the acceleration, The fourth weight.

[0123] S211. Determine a fifth weight and a sixth weight according to the current accelerator pedal opening, and weight the differential 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 Then, the fifth weight and the sixth weight are determined according to the accelerator pedal opening at the current moment in the sensor signal, the fifth weight is positively correlated to the accelerator pedal opening at the current moment, and the sum of the sixth weight and the fifth weight is the preset maximum weight.

[0125] Further, according to the fifth weight, the sixth weight, the second reference acceleration and differential acceleration , calculate and obtain the first reference acceleration , where the fifth weight is the differential acceleration The sixth weight is the weight of the modified acceleration, that is, the second reference acceleration. , calculated by the following formula (10):

[0126] ;

[0127] in, is the first reference acceleration, , is the second reference acceleration, By correcting the estimated acceleration in the estimation information, a corrected first reference acceleration is obtained. This avoids the sudden change of estimated acceleration caused by errors caused by slope, impact, etc. during the stable driving of the vehicle. The vehicle is controlled based on the first reference acceleration and the first reference vehicle speed at the current moment after correction.

[0128] S212: Under non-braking conditions, determine the vehicle speed of the non-driving axle according to the estimated vehicle speed of the wheels on the non-driving axle, and use the vehicle speed of the non-driving axle as the first reference vehicle speed at the current moment.

[0129] Specifically, if the current driving condition of the vehicle is a non-braking condition, the speed signals of the two wheels on the non-driving axle, such as the front axle, are obtained from the speed signals of the wheels of the sensor signal, and the speed signals of the wheels of the non-driving axle are input into a Kalman filter including a dynamic covariance matrix to obtain the estimated speeds corresponding to the two wheels, and the average of the estimated speeds corresponding to the two wheels is used as the first reference speed at the current moment. Based on the obtained first reference speed at the current moment and the estimated acceleration, the vehicle is controlled.

[0130] The vehicle state estimation method provided in the embodiment of the present application adjusts the Kalman filter through the sensor signal of the vehicle at the current moment, and performs Kalman filtering on the currently acquired sensor signal through the adjusted Kalman filter to obtain the estimated information including the estimated vehicle speed and the estimated acceleration at the current moment. Under the braking condition, the estimated vehicle speed and the estimated acceleration are corrected according to the estimated acceleration and the first reference vehicle speed at the previous moment to obtain the first reference vehicle speed and the first reference acceleration at the current moment, and the vehicle is controlled based on the first reference acceleration and the first reference vehicle speed at the current moment. The Kalman filter used in the present 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 vehicle speed is corrected multiple times under the braking condition, which can help improve the accuracy of filtering, thereby improving the accuracy of acceleration and reference vehicle speed.

[0131] Figure 4 A schematic diagram of the structure of the vehicle state estimation device provided in this application, such as Figure 4 As shown, the device 40 includes:

[0132] The acquisition module 401 is used to acquire a sensor signal sent by a sensor on the vehicle at a current moment, wherein the sensor signal includes an acceleration signal.

[0133] The first processing module 402 is used to adjust the Kalman filter according to the brake pedal opening, steering wheel angle and acceleration vibration amplitude of the vehicle at the current moment, so as to perform Kalman filtering on the sensor signal through the adjusted Kalman filter to obtain estimated information at the current moment, and the estimated information includes: estimated acceleration, and the acceleration vibration amplitude is the mean square error of multiple estimated accelerations in a historical time period.

[0134] The second processing module 403 is used to determine the second reference vehicle speed at the current moment under braking conditions based on the estimated acceleration and the first reference vehicle speed at the previous moment, and to correct the second reference vehicle speed by estimating the third reference vehicle speed at the next moment through the average acceleration between the start of braking and the current moment, so as to obtain the first reference vehicle speed at the current moment, and the average acceleration is the average value of the first reference acceleration between the start of braking and the current moment.

[0135] The control module 404 is configured to correct the estimated acceleration based on the first reference vehicle speed at the current moment and the first reference vehicle speed at the previous moment to obtain the first reference vehicle speed.

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

[0137] According to the first weight and the second weight, the second reference vehicle speed at the current moment and the third reference vehicle speed are weighted to obtain a fourth reference vehicle speed, the first weight being used as the weight of the second reference vehicle speed, and the second weight being used as the weight of the third reference vehicle speed;

[0138] According to the third weight and the fourth weight, the fourth reference vehicle speed and the third reference vehicle speed are weighted to obtain the first reference vehicle speed, 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.

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

[0140] According to the third weight and the fourth weight, the differential acceleration and the estimated acceleration are weighted to obtain a second reference acceleration, the third weight being used as the weight of the differential acceleration, and the fourth weight being used as the weight of the estimated acceleration;

[0141] Determining a fifth weight and a sixth weight according to the accelerator pedal opening at the current moment, wherein the fifth weight is positively correlated to the accelerator pedal opening at the current moment, and the sum of the sixth weight and the fifth weight is the preset maximum weight;

[0142] The differential acceleration and the second reference acceleration are weighted according to the fifth weight and the sixth weight to obtain the first reference acceleration, the fifth weight is used as the weight of the differential acceleration, and the sixth weight is used as the weight of the corrected acceleration.

[0143] In a possible implementation, the first processing module 402 is specifically used to determine 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 moment, respectively, 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;

[0144] Adjusting 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] Kalman filtering is performed on the sensor signal according to the dynamic covariance matrix to obtain estimation 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, 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 used 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 a dynamic covariance matrix, including:

[0147] The product of the first adjustment parameter, the second adjustment parameter, the third adjustment parameter and the preset process noise covariance matrix is ​​used as the dynamic process noise covariance matrix;

[0148] The product of the third adjustment parameter and the preset measurement noise covariance matrix is ​​used as the dynamic measurement noise covariance matrix.

[0149] In one possible implementation, the first processing module 402 is also used to determine the non-drive shaft speed based on the estimated speed of the wheels on the non-drive shaft under non-braking conditions, and use the non-drive shaft speed as the first reference speed at the current moment, and the non-drive shaft speed is the average of the estimated speeds of the two wheels of the non-drive shaft.

[0150] The vehicle state estimation device provided by this embodiment can perform the methods provided by the above-mentioned method, and its implementation principles and technical effects are similar. This embodiment will not be described here.

[0151] Figure 5 This is a schematic diagram of the structure of the electronic device provided in this application. Figure 5 As shown, the electronic device 50 provided in this embodiment includes: at least one processor 501 and a memory 502. Optionally, the device 50 also includes a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected via a bus 504.

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

[0153] For the specific implementation process of the processor 501, refer to the above-mentioned method embodiments, whose implementation principles and technical effects are similar, and this embodiment will not be described here.

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

[0155] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (NVM), such as at least one disk storage.

[0156] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (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 ease of representation, the bus in the drawings of this application is not limited to only one bus or one type of bus.

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

[0158] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0159] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, 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 memory, 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 a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0161] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0162] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0163] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0164] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

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

[0166] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A vehicle state estimation method, characterized in that: include: Acquire a sensor signal sent by a sensor on the vehicle at a current moment, wherein the sensor signal includes: an acceleration signal; According to the brake pedal opening, steering wheel angle and acceleration vibration amplitude of the vehicle at the current moment, the Kalman filter is adjusted to perform Kalman filtering on the sensor signal through the adjusted Kalman filter to obtain estimated information at the current moment, wherein the estimated information includes: estimated acceleration, the acceleration vibration amplitude is the mean square error of multiple estimated accelerations in a historical time period; Under the braking condition, a second reference vehicle speed at the current moment is determined according to the estimated acceleration and the first reference vehicle speed at the previous moment, and a third reference vehicle speed at the next moment is estimated by the average acceleration between the braking start moment and the current moment, and the second reference vehicle speed is corrected to obtain the first reference vehicle speed at the current moment, wherein the average acceleration is the average value of the first reference acceleration between the braking start moment and the current moment; The estimated acceleration is corrected according to the first reference vehicle speed at the current moment and the first reference vehicle speed at the previous moment to obtain a first reference acceleration.

2. The method according to claim 1, characterized in that The third reference vehicle speed at the next moment is estimated by the average acceleration from the braking start moment to the current moment, and the second reference vehicle speed is corrected to obtain the first reference vehicle speed at the current moment, including: Determine a first weight and a second weight according to a second reference vehicle speed at a current moment, and determine a third weight and a fourth weight according to a brake pedal opening at a current moment, wherein the first weight is positively correlated to 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 to the brake pedal opening at the current moment, and the sum of the fourth weight and the third weight is the preset maximum weight; According to the first weight and the second weight, the second reference vehicle speed at the current moment and the third reference vehicle speed are weighted to obtain a fourth reference vehicle speed, the first weight being used as the weight of the second reference vehicle speed, and the second weight being used as the weight of the third reference vehicle speed; According to the third weight and the fourth weight, the fourth reference vehicle speed and the third reference vehicle speed are weighted to obtain the first reference vehicle speed, 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.

3. The method according to claim 2, characterized in that The step of correcting the estimated acceleration according to the first reference vehicle speed at the current moment and the first reference vehicle speed at the previous moment to obtain the first reference acceleration includes: Determine the acceleration of the vehicle from the last moment to the current moment as a differential acceleration according to the first reference vehicle speed at the current moment and the first reference vehicle speed at the last moment; According to the third weight and the fourth weight, the differential acceleration and the estimated acceleration are weighted to obtain a second reference acceleration, the third weight being used as the weight of the differential acceleration, and the fourth weight being used as the weight of the estimated acceleration; Determining a fifth weight and a sixth weight according to the accelerator pedal opening at the current moment, wherein the fifth weight is positively correlated to the accelerator pedal opening at the current moment, and the sum of the sixth weight and the fifth weight is the preset maximum weight; The differential acceleration and the second reference acceleration are weighted according to the fifth weight and the sixth weight to obtain the first reference acceleration, the fifth weight is used as the weight of the differential acceleration, and the sixth weight is used as the weight of the corrected acceleration.

4. The method according to claim 1, characterized in that The Kalman filter is adjusted according to the brake pedal opening, steering wheel angle and acceleration vibration amplitude of the vehicle at the current moment, so as to perform Kalman filtering on the sensor signal through the adjusted Kalman filter to obtain the estimated information at the current moment, including: Determine the matrix adjustment parameters of the Kalman filter according to the brake pedal opening, steering wheel angle and acceleration vibration amplitude at the current moment, respectively, the matrix adjustment parameters comprising: 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; Adjusting 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; Kalman filtering is performed on the sensor signal according to the dynamic covariance matrix to obtain estimation information at the current moment.

5. The method according to claim 4, characterized in that 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; The method of adjusting 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 includes: The product of the first adjustment parameter, the second adjustment parameter, the third adjustment parameter and the preset process noise covariance matrix is ​​used as the dynamic process noise covariance matrix; The product of the third adjustment parameter and the preset measurement noise covariance matrix is ​​used as the dynamic measurement noise covariance matrix.

6. The method according to any one of claims 1 to 5, characterized in that The sensor signal also includes a vehicle speed signal of each wheel, the estimated information also includes: an estimated vehicle speed of each wheel, and the method further includes: Under non-braking conditions, the non-driven axle speed is determined based on the estimated speed of the wheels on the non-driven axle, and the non-driven axle speed is used as the first reference speed at the current moment. The non-driven axle speed is the average of the estimated speeds of the two wheels of the non-driven axle.

7. A vehicle state estimation device, characterized in that: include: An acquisition module, used to acquire a sensor signal sent by a sensor on the vehicle at a current moment, wherein the sensor signal includes: an acceleration signal; A first processing module is used to adjust the Kalman filter according to the brake pedal opening, steering wheel angle and acceleration vibration amplitude of the vehicle at the current moment, so as to perform Kalman filtering on the sensor signal through the adjusted Kalman filter to obtain estimated information at the current moment, wherein the estimated information includes: estimated acceleration, and the acceleration vibration amplitude is the mean square error of multiple estimated accelerations in a historical time period; a second processing module, for determining, under a braking condition, a second reference vehicle speed at a current moment according to the estimated acceleration and the first reference vehicle speed at a previous moment, and correcting the second reference vehicle speed by estimating a third reference vehicle speed at a next moment by an average acceleration between the braking start moment and the current moment, to obtain the first reference vehicle speed at the current moment, wherein the average acceleration is an average value of the first reference acceleration between the braking start moment and the current moment; The control module is used to correct the estimated acceleration according to the first reference vehicle speed at the current moment and the first reference vehicle speed at the previous moment to obtain a first reference acceleration.

8. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; 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, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed by a processor.

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