A vehicle speed estimation method, device, storage medium and vehicle

Abstract

The application provides a vehicle speed estimation method, device, storage medium and vehicle, wherein the vehicle speed estimation method comprises: obtaining slip estimation labels corresponding to each wheel of the vehicle; determining the adhesion state corresponding to the vehicle according to the slip estimation labels; and performing vehicle speed estimation according to an estimation mode matched with the adhesion state corresponding to the vehicle to obtain the overall vehicle speed of the vehicle. The slip estimation labels of each wheel are obtained, the adhesion state of the vehicle is obtained based on the slip estimation labels, and the overall vehicle speed is obtained by performing vehicle speed estimation according to the estimation mode matched with the adhesion state, so that different estimation modes can be used to perform vehicle speed estimation when the vehicle is in different adhesion states, the influence of wheel slip on the accuracy of the overall vehicle speed obtained by estimation is reduced, and the accuracy and reliability of the overall vehicle speed are improved.

Description

Technical Field

[0001] This invention relates to the field of automotive technology, and more particularly to a vehicle speed estimation method, device, storage medium, and vehicle. Background Technology

[0002] In vehicle dynamics control, vehicle speed is a crucial state variable and one of the key parameters for realizing active safety control, handling stability control, and vehicle drivability control technologies. Current technologies typically use wheel speed sensors to obtain the wheel speeds of all four wheels, and then perform calculations to obtain the overall vehicle speed, such as using the minimum wheel speed method or the maximum wheel speed method for speed prediction. However, under vehicle slippage conditions, the accuracy of the overall vehicle speed estimated in this way is significantly compromised. Summary of the Invention

[0003] This invention provides a vehicle speed estimation method, apparatus, storage medium, and vehicle to address the problem of improving the accuracy of estimated vehicle speeds.

[0004] In a first aspect, embodiments of the present invention provide a vehicle speed estimation method, including:

[0005] Obtain the slippage prediction labels for each wheel of the vehicle;

[0006] The vehicle's adhesion state is determined based on the slippage prediction label.

[0007] The vehicle speed is estimated by using an estimation method that matches the attachment state of the vehicle.

[0008] Optionally, obtaining the slippage prediction labels corresponding to each wheel of the vehicle includes:

[0009] Obtain the vehicle's overall acceleration and the wheel speed of each wheel;

[0010] For each wheel, a slippage prediction label is determined based on the overall vehicle acceleration, the wheel speed, and the wheel acceleration.

[0011] The acceleration of the wheel is determined based on the wheel speed.

[0012] Optionally, obtaining the wheel speed of the wheel includes:

[0013] Determine whether the wheel speed sensor corresponding to the wheel is malfunctioning;

[0014] If the wheel speed sensor is not faulty, the first wheel speed collected by the wheel speed sensor is acquired, and the first wheel speed is determined as the wheel speed of the wheel;

[0015] If the wheel speed sensor fails, the second wheel speed corresponding to the wheel is determined based on the motor resolver signal corresponding to the wheel, and the second wheel speed is determined as the wheel speed of the wheel.

[0016] Optionally, obtain the vehicle acceleration, including:

[0017] Obtain the longitudinal acceleration and vertical acceleration corresponding to the position of the vehicle's center of mass;

[0018] The vehicle acceleration is determined based on the longitudinal acceleration and the vertical acceleration.

[0019] Optionally, based on the vehicle acceleration, the wheel speed, and the wheel acceleration, a slippage prediction label corresponding to the wheel is determined, including:

[0020] Based on the acceleration of the wheel and the acceleration of the whole vehicle, it is determined whether the wheel meets the first slip condition, wherein the wheel meets the first slip condition when the absolute value of the difference between the acceleration of the wheel and the acceleration of the whole vehicle is greater than a first threshold.

[0021] The first vehicle speed is determined based on the overall vehicle acceleration.

[0022] Determine the second vehicle speed based on the wheel speed of the aforementioned wheels;

[0023] Based on the first vehicle speed and the second vehicle speed, determine whether the wheel meets the second slip condition;

[0024] When the wheel simultaneously meets both the first slip condition and the second slip condition, the slip prediction label of the wheel is set to slip.

[0025] Otherwise, set the wheel slippage prediction label to no slippage.

[0026] Optionally, determining whether the wheel meets the second slip condition based on the first vehicle speed and the second vehicle speed includes:

[0027] When the first vehicle speed is less than or equal to a preset speed threshold, if the absolute value of the difference between the first vehicle speed and the second vehicle speed is greater than a second threshold, it is determined that the wheel meets the second slip condition.

[0028] When the first vehicle speed is greater than the preset speed threshold, if the ratio of the absolute value of the difference between the first vehicle speed and the second vehicle speed to the first vehicle speed is greater than a third threshold, it is determined that the wheel meets the second slip condition.

[0029] Optionally, based on the slippage prediction label, the corresponding adhesion state of the vehicle is determined, including:

[0030] The vehicle's adhesion state is determined based on the number of target wheels corresponding to the slippage prediction label.

[0031] Specifically, when the target quantity is 0, the adhesion state is a non-slip state; when the target quantity is 1, the adhesion state is a single-wheel slipping state; when the target quantity is 2, the adhesion state is a two-wheel slipping state; when the target quantity is 3, the adhesion state is a three-wheel slipping state; and when the target quantity is 4, the adhesion state is a four-wheel slipping state.

[0032] Optionally, the vehicle speed is estimated based on an estimation method matching the attachment state corresponding to the vehicle, to obtain the overall vehicle speed, including:

[0033] When the attachment state is the non-slip state, the two-wheel slip state, or the three-wheel slip state, the average wheel speed corresponding to the wheel with the slip prediction label of non-slip is obtained, and the reference vehicle speed is determined based on the average wheel speed.

[0034] When the adhesion state is the single-wheel slippage state, if the vehicle is in an acceleration state, a reference vehicle speed is determined based on the first wheel speed; if the vehicle is in a braking state, the vehicle speed is determined based on the second wheel speed, wherein the first wheel speed is the maximum value among the wheel speeds corresponding to the wheels of the vehicle, and the second wheel speed is the minimum value among the wheel speeds corresponding to the wheels of the vehicle.

[0035] When the adhesion state is the four-wheel slipping state, a reference vehicle speed is determined based on the vehicle speed corresponding to the target time before the current time and the vehicle's overall acceleration.

[0036] After obtaining the reference vehicle speed, the reference vehicle speed and the vehicle acceleration are input into a Kalman filter to obtain the vehicle speed.

[0037] Optionally, before determining the slippage prediction label corresponding to the wheel based on the vehicle acceleration, the wheel speed, and the wheel acceleration, the method further includes:

[0038] The vehicle acceleration, the wheel speed, and the wheel acceleration are subjected to first-order filtering.

[0039] Secondly, embodiments of the present invention also provide a vehicle speed estimation system, comprising:

[0040] The acquisition module is used to acquire the slippage prediction labels corresponding to each wheel of the vehicle.

[0041] The first determining module is used to determine the attachment state of the vehicle based on the slippage prediction label.

[0042] The second determining module is used to estimate the vehicle speed according to an estimation method that matches the attachment state corresponding to the vehicle, so as to obtain the overall vehicle speed.

[0043] Optionally, the acquisition module includes:

[0044] The acquisition submodule is used to acquire the vehicle's acceleration and the wheel speed of each wheel;

[0045] The first determination submodule is used to determine the slippage prediction label corresponding to each wheel based on the overall vehicle acceleration, the wheel speed, and the wheel acceleration.

[0046] The acceleration of the wheel is determined based on the wheel speed.

[0047] Optionally, the acquisition submodule includes:

[0048] The first judgment unit is used to determine whether the wheel speed sensor corresponding to the wheel is malfunctioning.

[0049] The first determining unit is configured to, if the wheel speed sensor is not malfunctioning, acquire the first wheel speed collected by the wheel speed sensor and determine the first wheel speed as the wheel speed of the wheel.

[0050] The second determining unit is used to determine the second wheel speed corresponding to the wheel based on the motor resolver signal corresponding to the wheel if the wheel speed sensor fails, and to determine the second wheel speed as the wheel speed of the wheel.

[0051] Optionally, the acquisition submodule includes:

[0052] The acquisition unit is used to acquire the longitudinal acceleration and vertical acceleration corresponding to the center of mass position of the vehicle;

[0053] The third determining unit is used to determine the vehicle acceleration based on the longitudinal acceleration and the vertical acceleration.

[0054] Optionally, the first determining submodule includes:

[0055] The second judgment unit is used to determine whether the wheel meets the first slip condition based on the acceleration of the wheel and the acceleration of the whole vehicle. The wheel meets the first slip condition when the absolute value of the difference between the acceleration of the wheel and the acceleration of the whole vehicle is greater than a first threshold.

[0056] The fourth determining unit is used to determine the first vehicle speed based on the vehicle acceleration;

[0057] The fifth determining unit is used to determine the second vehicle speed based on the wheel speed of the wheels;

[0058] The third judgment unit is used to determine whether the wheel meets the second slip condition based on the first vehicle speed and the second vehicle speed.

[0059] The label setting unit is used to set the wheel's slip prediction label to slip when the wheel simultaneously meets the first slip condition and the second slip condition; otherwise, it sets the wheel's slip prediction label to no slip.

[0060] Optionally, the third judgment unit includes:

[0061] The first determining subunit is used to determine that the wheel meets the second slip condition when the first vehicle speed is less than or equal to a preset speed threshold and the absolute value of the difference between the first vehicle speed and the second vehicle speed is greater than a second threshold.

[0062] The second determining subunit is used to determine that the wheel meets the second slip condition when the first vehicle speed is greater than the preset speed threshold and the ratio of the absolute value of the difference between the first vehicle speed and the second vehicle speed to the first vehicle speed is greater than a third threshold.

[0063] Optionally, the first determining module is further configured to:

[0064] The vehicle's adhesion state is determined based on the number of target wheels corresponding to the slippage prediction label.

[0065] Specifically, when the target quantity is 0, the adhesion state is a non-slip state; when the target quantity is 1, the adhesion state is a single-wheel slipping state; when the target quantity is 2, the adhesion state is a two-wheel slipping state; when the target quantity is 3, the adhesion state is a three-wheel slipping state; and when the target quantity is 4, the adhesion state is a four-wheel slipping state.

[0066] Optionally, the second determining module includes:

[0067] The second determining submodule is used to obtain the average wheel speed corresponding to the wheel with the slip prediction label as not slipping when the attachment state is the non-slipping state, the two-wheel slipping state, or the three-wheel slipping state, and to determine the reference vehicle speed based on the average wheel speed.

[0068] The third determining submodule is used to determine a reference vehicle speed based on a first wheel speed when the adhesion state is the single-wheel slippage state, if the vehicle is in an acceleration state; and to determine the vehicle speed based on a second wheel speed if the vehicle is in a braking state, wherein the first wheel speed is the maximum value among the wheel speeds corresponding to the wheels of the vehicle, and the second wheel speed is the minimum value among the wheel speeds corresponding to the wheels of the vehicle.

[0069] The fourth determining submodule is used to determine a reference vehicle speed based on the vehicle speed corresponding to the target time before the current time and the vehicle's overall acceleration when the adhesion state is the four-wheel slipping state.

[0070] The fifth determining submodule is used to input the reference vehicle speed and the vehicle acceleration into a Kalman filter after obtaining the reference vehicle speed to obtain the vehicle speed.

[0071] Optionally, the system further includes:

[0072] The processing module is used to perform first-order filtering on the vehicle acceleration, the wheel speed, and the wheel acceleration.

[0073] Thirdly, embodiments of the present invention also provide a vehicle speed estimation device, including: a processor, a communication interface, a memory, and a communication bus; wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0074] Memory, used to store computer programs;

[0075] When the processor executes the program stored in the memory, it implements the steps in the vehicle speed estimation method described in the first aspect above.

[0076] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the vehicle speed estimation method described in the first aspect.

[0077] Fifthly, embodiments of the present invention provide a vehicle including the aforementioned vehicle speed estimation device.

[0078] Compared with prior art, the present invention has the following advantages:

[0079] In this embodiment of the invention, by obtaining the slippage prediction label of each wheel, the vehicle's adhesion state is obtained based on the slippage prediction label, and the vehicle speed is estimated by an estimation method that matches the adhesion state. This allows for the use of different estimation methods to estimate the vehicle speed when the vehicle is in different adhesion states, reducing the impact of wheel slippage on the accuracy of the estimated vehicle speed and improving the accuracy and reliability of the vehicle speed.

[0080] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0081] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0082] Figure 1 This is one of the structural schematic diagrams of the vehicle speed estimation method provided in the embodiments of the present invention;

[0083] Figure 2 This is the second schematic diagram of the vehicle speed estimation method provided in the embodiments of the present invention;

[0084] Figure 3 This is a schematic diagram of the vehicle speed estimation system provided in an embodiment of the present invention;

[0085] Figure 4 This is a schematic diagram of the vehicle speed estimation device provided in an embodiment of the present invention. Detailed Implementation

[0086] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.

[0087] This invention provides a vehicle speed estimation method, applied to a vehicle speed estimation controller, such as... Figure 1 As shown, the method includes:

[0088] Step 101: Obtain the slippage prediction labels corresponding to each wheel of the vehicle.

[0089] The vehicle speed estimation method provided in this embodiment of the invention is applied to a vehicle speed estimation controller, which can be installed independently on the vehicle or integrated into other controllers within the vehicle. After the vehicle starts, the vehicle speed estimation controller becomes active, enabling it to acquire the slippage prediction labels corresponding to each wheel in real time during vehicle operation.

[0090] Specifically, the vehicle speed estimation controller can acquire vehicle driving parameters in real time and then predict whether each wheel is slipping based on these parameters. These vehicle driving parameters include the overall vehicle acceleration and the wheel speed of each wheel.

[0091] For each wheel, if the slippage prediction result for that wheel indicates that it is in a slipping state, then the slippage prediction label for that wheel is set to slipping; if the slippage prediction result for that wheel indicates that it is not in a slipping state, then the slippage prediction label for that wheel is set to not slipping.

[0092] Step 102: Determine the adhesion state of the vehicle based on the slippage prediction label.

[0093] After obtaining the slippage prediction labels for each wheel, the vehicle's current adhesion status can be determined based on these labels. The adhesion status characterizes the adhesion between the vehicle's four wheels and the ground.

[0094] Step 103: Estimate the vehicle speed according to the estimation method that matches the attachment state corresponding to the vehicle, and obtain the overall vehicle speed.

[0095] After determining the vehicle's attachment state, an estimation method matching that attachment state can be obtained. Then, the vehicle speed can be estimated based on this estimation method to obtain the vehicle's overall speed.

[0096] Specifically, different estimation methods are used for different adhesion states. By acquiring the vehicle's adhesion state in real time and using the corresponding estimation method to estimate the vehicle speed, the accuracy of the overall vehicle speed can be improved.

[0097] The above implementation process obtains the slippage prediction label of each wheel, obtains the vehicle's adhesion state based on the slippage prediction label, and estimates the vehicle speed using an estimation method that matches the adhesion state. This allows for different estimation methods to be used to estimate the vehicle speed when the vehicle is in different adhesion states, reducing the impact of wheel slippage on the accuracy of the estimated vehicle speed and improving the accuracy and reliability of the overall vehicle speed.

[0098] In an optional embodiment of the present invention, obtaining the slippage prediction labels corresponding to each wheel of the vehicle includes:

[0099] Obtain the vehicle's overall acceleration and the wheel speed of each wheel;

[0100] For each wheel, a slippage prediction label is determined based on the overall vehicle acceleration, the wheel speed, and the wheel acceleration.

[0101] The acceleration of the wheel is determined based on the wheel speed.

[0102] In this embodiment, when obtaining the slippage prediction label corresponding to each vehicle, the vehicle acceleration and the wheel speed of each wheel can be obtained. Then, for each wheel, the wheel acceleration is determined based on the wheel speed. Specifically, the wheel acceleration can be obtained by differentiating the vehicle speed obtained by the wheel speed. The slippage prediction label of the wheel is determined based on the vehicle acceleration, the wheel speed, and the wheel acceleration.

[0103] The above-described embodiments of the present invention obtain the vehicle acceleration and wheel speed of each wheel to obtain the slippage prediction label corresponding to each wheel. The vehicle driving parameters can be obtained directly from the vehicle's existing sensors without the need to add additional sensors, thus saving hardware costs.

[0104] In an optional embodiment of the present invention, obtaining the wheel speed of the wheel includes:

[0105] Determine whether the wheel speed sensor corresponding to the wheel is malfunctioning;

[0106] If the wheel speed sensor is not faulty, the first wheel speed collected by the wheel speed sensor is acquired, and the first wheel speed is determined as the wheel speed of the wheel;

[0107] If the wheel speed sensor fails, the second wheel speed corresponding to the wheel is determined based on the motor resolver signal corresponding to the wheel, and the second wheel speed is determined as the wheel speed of the wheel.

[0108] Specifically, for distributed drive vehicles, wheel speeds can be obtained from wheel speed sensors or calculated based on motor resolver information. Considering the possibility of wheel speed sensor failure, the process first checks for sensor malfunction. If the sensor is functioning correctly, the first wheel speed is acquired and determined as the wheel speed. If the sensor is faulty, a second wheel speed is calculated based on the motor resolver signal and also determined as the wheel speed. In other words, the first wheel speed is prioritized, and the second wheel speed is used when the wheel speed sensor fails.

[0109] In the above-described embodiments of the present invention, when obtaining wheel speeds, the vehicle's architecture features of a distributed drive architecture are fully utilized to obtain wheel speeds from two sources. This eliminates the need for high dependence on wheel speed sensor signals, avoids the impact of wheel speed sensor failure on the determination of whether the wheel is slipping, and improves the accuracy of the final vehicle speed.

[0110] In an optional embodiment of the present invention, obtaining the vehicle acceleration includes:

[0111] Obtain the longitudinal acceleration and vertical acceleration corresponding to the position of the vehicle's center of mass;

[0112] The vehicle acceleration is determined based on the longitudinal acceleration and the vertical acceleration.

[0113] Specifically, when obtaining the vehicle speed, an inertial measurement unit (IMU) can be installed at the vehicle's center of gravity to obtain the acceleration in three directions within the vehicle's coordinate system. The longitudinal acceleration is the longitudinal acceleration corresponding to the center of gravity, and the vertical acceleration is the vertical acceleration corresponding to the center of gravity. The overall vehicle acceleration can be calculated using the following formula.

[0114]

[0115] Among them, a ref For the acceleration of the whole vehicle, a x For longitudinal acceleration, a z This is the vertical acceleration.

[0116] In the above-described embodiments of the present invention, when obtaining the vehicle acceleration, the inertial measurement unit can be directly placed at the center of gravity of the vehicle, and the vehicle acceleration can be calculated based on the acceleration measured by the inertial measurement unit. Since the inertial measurement unit is completely independent, the measurement results are not limited by motion or any specific environment and position, thereby obtaining the vehicle acceleration with high reliability in real time.

[0117] In an optional embodiment of the present invention, determining the slippage prediction label corresponding to the wheel based on the vehicle acceleration, the wheel speed, and the wheel acceleration includes:

[0118] Based on the acceleration of the wheel and the acceleration of the whole vehicle, it is determined whether the wheel meets the first slip condition, wherein the wheel meets the first slip condition when the absolute value of the difference between the acceleration of the wheel and the acceleration of the whole vehicle is greater than a first threshold.

[0119] The first vehicle speed is determined based on the overall vehicle acceleration.

[0120] Determine the second vehicle speed based on the wheel speed of the aforementioned wheels;

[0121] Based on the first vehicle speed and the second vehicle speed, determine whether the wheel meets the second slip condition;

[0122] When the wheel simultaneously meets both the first slip condition and the second slip condition, the slip prediction label of the wheel is set to slip.

[0123] Otherwise, set the wheel slippage prediction label to no slippage.

[0124] Specifically, when determining the slippage prediction label for a wheel, slippage can be judged from two dimensions. The first dimension is from the perspective of acceleration, i.e., whether the wheel meets the first slippage condition. The second dimension is from the perspective of vehicle speed, i.e., whether the wheel meets the second slippage condition. If both the first and second slippage conditions are met, the wheel's slippage prediction label is slipping; otherwise, the vehicle's slippage prediction label is not slipping. If only one of the slippage conditions is met, the wheel is predicted not to be slipping.

[0125] Based on the acceleration of the wheels and the acceleration of the whole vehicle, it can be determined whether the wheels meet the first slip condition; based on the first vehicle speed and the second vehicle speed, it can be determined whether the wheels meet the second slip condition. Here, the first vehicle speed is the speed calculated based on the acceleration of the whole vehicle, and the second vehicle speed is the speed calculated based on the wheel speed.

[0126] In the above-described embodiment of the present invention, when determining the slippage prediction label corresponding to a wheel, it is necessary to determine whether the wheel meets the first slippage condition based on the wheel's acceleration and the vehicle's acceleration. Then, a first vehicle speed is calculated based on the vehicle's acceleration, and a second vehicle speed is calculated based on the wheel's speed. Based on the first and second vehicle speeds, it is determined whether the wheel meets the second slippage condition. If both conditions are met, the slippage prediction label is determined to be slipping; otherwise, it is determined to be not slipping. Since the wheel slippage situation is determined from two dimensions, the accuracy of the slippage prediction label can be improved, avoiding misjudgment of wheel slippage and improving the accuracy of the final estimated vehicle speed.

[0127] In an optional embodiment of the present invention, determining whether the wheel meets the second slip condition based on the first vehicle speed and the second vehicle speed includes:

[0128] When the first vehicle speed is less than or equal to a preset speed threshold, if the absolute value of the difference between the first vehicle speed and the second vehicle speed is greater than a second threshold, it is determined that the wheel meets the second slip condition.

[0129] When the first vehicle speed is greater than the preset speed threshold, if the ratio of the absolute value of the difference between the first vehicle speed and the second vehicle speed to the first vehicle speed is greater than a third threshold, it is determined that the wheel meets the second slip condition.

[0130] Specifically, when determining whether the wheels meet the second slip condition based on the first vehicle speed and the second vehicle speed, different judgment conditions need to be set for different vehicle speeds. The first vehicle speed is the speed calculated based on the vehicle acceleration. The vehicle speed can be compared with a preset speed threshold to determine whether the vehicle is traveling at a low speed or a high speed.

[0131] When the vehicle is traveling at low speed, that is, when the first speed is less than or equal to a preset speed threshold, the system determines whether the wheels meet the second slip condition by judging whether the absolute value of the difference between the first speed and the second speed is greater than a second threshold. At this time, the vehicle speed is low, and there is a significant difference between the speed difference when the wheels slip and when the wheels do not slip. Therefore, the absolute value of the speed difference can be compared with the second threshold to determine whether the wheels meet the second slip condition.

[0132] When a vehicle is traveling at high speed, specifically when the first speed exceeds a preset speed threshold, the system determines whether the second slip condition is met by comparing the ratio of the absolute value of the difference between the first and second speeds to the first speed itself, which is greater than a third threshold. At this high speed, the speed difference between wheel slippage and non-slippage is significantly different. Therefore, directly comparing the absolute value of the speed difference with the second threshold cannot effectively distinguish between slippage and non-slippage. However, at higher speeds, the ratio of the absolute value of the difference between the first and second speeds to the first speed shows a significant difference between wheel slippage and non-slippage. Therefore, this parameter can be compared with the third threshold to determine whether the vehicle meets the second slip condition.

[0133] In the above-described embodiments of the present invention, when determining whether a wheel meets the second slip condition, different judgment thresholds are set for different vehicle speeds. This allows for the determination of whether a wheel is slipping at different vehicle speeds, thereby improving the accuracy of the slip prediction label, avoiding misjudgments of wheel slippage, and improving the accuracy of the final estimated vehicle speed.

[0134] In an optional embodiment of the present invention, determining the adhesion state of the vehicle based on the slippage prediction label includes:

[0135] The vehicle's adhesion state is determined based on the number of target wheels corresponding to the slippage prediction label.

[0136] Specifically, when the target quantity is 0, the adhesion state is a non-slip state; when the target quantity is 1, the adhesion state is a single-wheel slipping state; when the target quantity is 2, the adhesion state is a two-wheel slipping state; when the target quantity is 3, the adhesion state is a three-wheel slipping state; and when the target quantity is 4, the adhesion state is a four-wheel slipping state.

[0137] Specifically, after determining the slippage prediction label for each wheel, the target number of wheels with the slippage prediction label can be obtained. Then, based on the target number, the vehicle's adhesion state can be determined.

[0138] The final determined vehicle adhesion state is one of the following: no slippage, single-wheel slippage, two-wheel slippage, three-wheel slippage, and four-wheel slippage.

[0139] The above-described embodiments of the present invention determine the vehicle's adhesion state by using the slippage prediction label as the target number corresponding to the slipping wheels. This facilitates subsequent speed estimation based on an estimation method that matches the adhesion state, thereby improving the accuracy of the final estimated vehicle speed.

[0140] In an optional embodiment of the present invention, the vehicle speed is estimated according to an estimation method matching the attachment state corresponding to the vehicle, to obtain the overall vehicle speed, including:

[0141] When the attachment state is the non-slip state, the two-wheel slip state, or the three-wheel slip state, the average wheel speed corresponding to the wheel with the slip prediction label of non-slip is obtained, and the reference vehicle speed is determined based on the average wheel speed.

[0142] When the adhesion state is the single-wheel slippage state, if the vehicle is in an acceleration state, a reference vehicle speed is determined based on the first wheel speed; if the vehicle is in a braking state, the vehicle speed is determined based on the second wheel speed, wherein the first wheel speed is the maximum value among the wheel speeds corresponding to the wheels of the vehicle, and the second wheel speed is the minimum value among the wheel speeds corresponding to the wheels of the vehicle.

[0143] When the adhesion state is the four-wheel slipping state, a reference vehicle speed is determined based on the vehicle speed corresponding to the target time before the current time and the vehicle's overall acceleration.

[0144] After obtaining the reference vehicle speed, the reference vehicle speed and the vehicle acceleration are input into a Kalman filter to obtain the vehicle speed.

[0145] Specifically, different estimation methods matching the adhesion state can be used to predict vehicle speed for different adhesion states.

[0146] When the adhesion state is non-slip, two-wheel slip, or three-wheel slip, the average wheel speed corresponding to the wheel with the slip prediction label of non-slip is obtained, and the reference vehicle speed is determined based on the average wheel speed. Specifically, when the adhesion state is non-slip, the average wheel speed is determined based on the four wheel speeds; when the adhesion state is two-wheel slip, the average wheel speed is determined based on the wheel speeds of the two non-slipping wheels; and when the adhesion state is three-wheel slip, the average wheel speed is determined based on the wheel speed of the one non-slipping wheel.

[0147] When the adhesion state is a single wheel slippage state, it is necessary to further determine the vehicle's driving conditions. When the vehicle is accelerating, the reference speed is determined based on the first wheel speed, which is the maximum value among the wheel speeds of the vehicle's wheels. When the vehicle is braking, the reference speed is determined based on the second wheel speed, which is the minimum value among the wheel speeds of the vehicle's wheels.

[0148] When the adhesion state is a four-wheel slippage state, since all the wheels of the vehicle are judged to be slipping, the reference vehicle speed cannot be determined by the wheel speed. Instead, the reference vehicle speed needs to be determined based on the vehicle speed and the acceleration of the whole vehicle at the target time. Here, the target time is the time before the current time. Preferably, the target time is the time before the current time.

[0149] After determining the reference vehicle speed, in order to further improve the accuracy of the overall vehicle speed, it is necessary to perform Kalman filtering on the reference vehicle speed. Specifically, the reference vehicle speed and the overall vehicle acceleration are input into the Kalman filter to obtain the overall vehicle speed.

[0150] Specifically, the state equation of the Kalman filter is: X(k) = AX(k-1) + BU(k) + W(k);

[0151] The observation equation of the Kalman filter is: Z(k)=HX(k)+V(k).

[0152] The time update equation and state update equation of the discretized Kalman filter are as follows:

[0153] X(k|k-1)=AX(k-1|k-1)+BU(k)

[0154] P(k|k-1)=AP(k-1|k-1)A T +Q

[0155] X(k|k)=X(k|k-1)+K(k)[Z(k)-HX(k|k-1)]

[0156] K(k)=P(k|k-1)H T / [HP(k|k-1)H T +R]

[0157] P(k|k)=[IK(k)H]P(k|k-1)

[0158] Where X(k) is the state vector, including the observed target; A is the state transition matrix; B is the control variable matrix; W(k) is the noise of the control system, which follows a Gaussian distribution; Q is the covariance matrix of W(k); Z(k) is the measurement vector; H is the transformation matrix from the state vector to the measurement vector, representing the relationship connecting the state and the observation. In Kalman filtering, this is a linear relationship. It is responsible for converting m-dimensional measurements into n-dimensional data to conform to the data form of state variables, and is one of the prerequisites for filtering; V(k) is the measurement noise, which follows a Gaussian distribution; R is the covariance matrix of V(k); K(k) is the Kalman gain matrix, which is an intermediate calculation result of the filtering, also known as the Kalman coefficients; X(k-1|k-1) and X(k|k) are also present. The values ​​of X(k-1) and X(k-1) represent the posterior state estimates at time k-1 and time k, respectively. These are one of the results of filtering, i.e., the updated result, which is the optimal estimate. X(k|k-1) represents the prior state estimate at time k. This is an intermediate calculation result of filtering, i.e., the result predicted at time k based on the optimal estimate at the previous time (time k-1). This is the result of the prediction equation. P(k-1|k-1) and P(k|k) represent the posterior estimate covariances at time k-1 and time k, respectively. These are the covariances of X(k-1|k-1) and X(k|k), representing the uncertainty of the state. This is one of the results of filtering. P(k|k-1) represents the prior estimate covariance at time k, i.e., the covariance of X(k|k-1). This is an intermediate calculation result of filtering.

[0159] The above are the basic equations for Kalman filtering of stochastic linear discrete systems. Given an initial value, the state value Xk can be estimated by recursively calculating based on the observed value Zk.

[0160] Specifically, during the acquisition of vehicle signals (wheel speed, acceleration, etc.), various road environment noises affect the acquired signals, which often contain glitches and errors. These glitches will affect the accuracy of the estimation as the errors accumulate. Therefore, Kalman filtering is required to filter the signals.

[0161] The Kalman filter is constructed, and the state equation and measurement equation are as follows:

[0162] X(k) = AX(k-1) + Ba(k) + W(k)

[0163] Y(k)=CX(k)+V(k)

[0164] Wherein, the state vector v(k) is the reference vehicle speed, a(k) is the vehicle acceleration, and the state transition matrix is... Δt is the sampling time interval, and the control variable matrix is... noise of the control system Y(k)=[v wheel], C=[1 0], V(k)=[v1(k)], v wheel Let w1, w2, and v1 be the wheel speed signals. Here, we assume that w1, w2, and v1 are all independent Gaussian random signals with zero mean.

[0165] By processing the reference vehicle speed and vehicle acceleration using the Kalman filter described above to obtain the vehicle speed, the influence of road environment noise during signal acquisition can be filtered out, thus improving the accuracy of the vehicle speed.

[0166] The above-described embodiments of the present invention obtain a reference vehicle speed by using an estimation method that matches the adhesion state, and then perform Kalman filtering on the reference vehicle speed to obtain the overall vehicle speed. This allows different estimation methods to be used for different adhesion states to obtain the overall vehicle speed. At the same time, Kalman filtering can further improve the accuracy of the overall vehicle speed.

[0167] In an optional embodiment of the present invention, before determining the slippage prediction label corresponding to the wheel based on the vehicle acceleration, the wheel speed, and the wheel acceleration, the method further includes:

[0168] The vehicle acceleration, the wheel speed, and the wheel acceleration are subjected to first-order filtering.

[0169] Specifically, before determining the slippage prediction label corresponding to the wheel based on the vehicle acceleration, wheel speed, and wheel acceleration, a first-order filter can be applied to the vehicle acceleration, wheel speed, and wheel acceleration to eliminate wheel speed jitter caused by the wheel speed sensor or click refractive index, as well as acceleration jitter caused by the inertial measurement unit.

[0170] The above-described embodiments of the present invention, through first-order filtering, can improve the accuracy of vehicle acceleration, wheel speed, and wheel acceleration, and further improve the accuracy of slippage prediction labels obtained based on vehicle acceleration, wheel speed, and wheel acceleration.

[0171] The overall implementation process of the embodiments of this application is described below, such as... Figure 2 As shown, it includes:

[0172] Step 201: Calculate the wheel speed of each wheel using the signal collected by the wheel speed sensor or the motor resolver signal.

[0173] Step 202: Perform first-order filtering on the wheel speed of each wheel and calculate the corresponding acceleration of each wheel.

[0174] Step 203: Obtain the longitudinal acceleration and vertical acceleration corresponding to the center of mass position by using an inertial measurement unit installed at the center of mass position of the vehicle, and determine the vehicle acceleration based on the longitudinal acceleration and vertical acceleration.

[0175] Step 204: For each wheel, determine the slippage prediction label based on the vehicle acceleration, wheel speed, and wheel acceleration. Specifically, for each wheel, calculate the absolute value of the difference between the vehicle acceleration and the wheel acceleration. If this absolute value is greater than a first threshold, the first slippage condition is met. Calculate the first vehicle speed based on the vehicle acceleration and the second vehicle speed based on the wheel speed. If the first vehicle speed is less than or equal to a preset speed threshold, and the absolute value of the difference between the first and second speeds is greater than the second threshold, the wheel is determined to meet the second slippage condition. If the first vehicle speed is greater than the preset speed threshold, and the ratio of the absolute value of the difference between the first and second speeds to the first speed is greater than a third threshold, the wheel is determined to meet the second slippage condition. When a wheel simultaneously meets both the first and second slippage conditions, the slippage prediction label is "slippage"; otherwise, it is "no slippage".

[0176] Step 205: Determine the vehicle's corresponding adhesion status based on the slippage prediction label.

[0177] Step 206: Estimate the vehicle speed according to the estimation method that matches the vehicle's attachment state to obtain the reference vehicle speed.

[0178] Specifically, when the vehicle is in a non-slipping state, the average wheel speed method is used to calculate the reference vehicle speed. When the vehicle is in an acceleration state with single-wheel slippage, the minimum wheel speed method is used to calculate the reference vehicle speed. When the vehicle is in a braking state with single-wheel slippage, the maximum wheel speed method is used to calculate the reference vehicle speed. When the vehicle is in a two-wheel slippage state, there are three cases: co-axle slippage, same-side slippage, and diagonal slippage. For co-axle slippage, the stable axle speed method is used to calculate the reference vehicle speed; for same-side slippage, the stable side wheel speed method is used; and for diagonal slippage, the stable diagonal wheel speed method is used. When the vehicle is in a three-wheel slippage state, the stable wheel speed method is used to calculate the reference vehicle speed. When the vehicle is in a four-wheel slippage state, the slope method is used to calculate the reference vehicle speed.

[0179] Step 207: Input the reference vehicle speed and the vehicle acceleration into the Kalman filter to obtain the vehicle speed.

[0180] In this embodiment of the invention, by acquiring the slippage prediction label of each wheel, the vehicle's adhesion state is obtained based on the slippage prediction label, and the vehicle speed is estimated by using an estimation method that matches the adhesion state. This allows for the use of different estimation methods to estimate the vehicle speed when the vehicle is in different adhesion states, thereby reducing the impact of wheel slippage on the accuracy of the estimated vehicle speed and improving the accuracy and reliability of the vehicle speed.

[0181] The vehicle speed estimation method provided by the embodiments of this application has been described above. The vehicle speed estimation system provided by the embodiments of this application will be described below with reference to the accompanying drawings.

[0182] This application also provides a vehicle speed estimation system, such as... Figure 3 As shown, the system includes:

[0183] The acquisition module 301 is used to acquire the slippage prediction labels corresponding to each wheel of the vehicle.

[0184] The first determining module 302 is used to determine the attachment state of the vehicle based on the slippage prediction label.

[0185] The second determining module 303 is used to estimate the vehicle speed according to an estimation method that matches the attachment state corresponding to the vehicle, and to obtain the overall vehicle speed of the vehicle.

[0186] Optionally, the acquisition module includes:

[0187] The acquisition submodule is used to acquire the vehicle's acceleration and the wheel speed of each wheel;

[0188] The first determination submodule is used to determine the slippage prediction label corresponding to each wheel based on the overall vehicle acceleration, the wheel speed, and the wheel acceleration.

[0189] The acceleration of the wheel is determined based on the wheel speed.

[0190] Optionally, the acquisition submodule includes:

[0191] The first judgment unit is used to determine whether the wheel speed sensor corresponding to the wheel is malfunctioning.

[0192] The first determining unit is configured to, if the wheel speed sensor is not malfunctioning, acquire the first wheel speed collected by the wheel speed sensor and determine the first wheel speed as the wheel speed of the wheel.

[0193] The second determining unit is used to determine the second wheel speed corresponding to the wheel based on the motor resolver signal corresponding to the wheel if the wheel speed sensor fails, and to determine the second wheel speed as the wheel speed of the wheel.

[0194] Optionally, the acquisition submodule includes:

[0195] The acquisition unit is used to acquire the longitudinal acceleration and vertical acceleration corresponding to the center of mass position of the vehicle;

[0196] The third determining unit is used to determine the vehicle acceleration based on the longitudinal acceleration and the vertical acceleration.

[0197] Optionally, the first determining submodule includes:

[0198] The second judgment unit is used to determine whether the wheel meets the first slip condition based on the acceleration of the wheel and the acceleration of the whole vehicle. The wheel meets the first slip condition when the absolute value of the difference between the acceleration of the wheel and the acceleration of the whole vehicle is greater than a first threshold.

[0199] The fourth determining unit is used to determine the first vehicle speed based on the vehicle acceleration;

[0200] The fifth determining unit is used to determine the second vehicle speed based on the wheel speed of the wheels;

[0201] The third judgment unit is used to determine whether the wheel meets the second slip condition based on the first vehicle speed and the second vehicle speed.

[0202] The label setting unit is used to set the wheel's slip prediction label to slip when the wheel simultaneously meets the first slip condition and the second slip condition; otherwise, it sets the wheel's slip prediction label to no slip.

[0203] Optionally, the third judgment unit includes:

[0204] The first determining subunit is used to determine that the wheel meets the second slip condition when the first vehicle speed is less than or equal to a preset speed threshold and the absolute value of the difference between the first vehicle speed and the second vehicle speed is greater than a second threshold.

[0205] The second determining subunit is used to determine that the wheel meets the second slip condition when the first vehicle speed is greater than the preset speed threshold and the ratio of the absolute value of the difference between the first vehicle speed and the second vehicle speed to the first vehicle speed is greater than a third threshold.

[0206] Optionally, the first determining module is further configured to:

[0207] The vehicle's adhesion state is determined based on the number of target wheels corresponding to the slippage prediction label.

[0208] Specifically, when the target quantity is 0, the adhesion state is a non-slip state; when the target quantity is 1, the adhesion state is a single-wheel slipping state; when the target quantity is 2, the adhesion state is a two-wheel slipping state; when the target quantity is 3, the adhesion state is a three-wheel slipping state; and when the target quantity is 4, the adhesion state is a four-wheel slipping state.

[0209] Optionally, the second determining module includes:

[0210] The second determining submodule is used to obtain the average wheel speed corresponding to the wheel with the slip prediction label as not slipping when the attachment state is the non-slipping state, the two-wheel slipping state, or the three-wheel slipping state, and to determine the reference vehicle speed based on the average wheel speed.

[0211] The third determining submodule is used to determine a reference vehicle speed based on a first wheel speed when the adhesion state is the single-wheel slippage state, if the vehicle is in an acceleration state; and to determine the vehicle speed based on a second wheel speed if the vehicle is in a braking state, wherein the first wheel speed is the maximum value among the wheel speeds corresponding to the wheels of the vehicle, and the second wheel speed is the minimum value among the wheel speeds corresponding to the wheels of the vehicle.

[0212] The fourth determining submodule is used to determine a reference vehicle speed based on the vehicle speed corresponding to the target time before the current time and the vehicle's overall acceleration when the adhesion state is the four-wheel slipping state.

[0213] The fifth determining submodule is used to input the reference vehicle speed and the vehicle acceleration into a Kalman filter after obtaining the reference vehicle speed to obtain the vehicle speed.

[0214] Optionally, the system further includes:

[0215] The processing module is used to perform first-order filtering on the vehicle acceleration, the wheel speed, and the wheel acceleration.

[0216] The vehicle speed estimation system provided in this invention obtains the slippage prediction label of each wheel, obtains the vehicle's adhesion state based on the slippage prediction label, and estimates the vehicle speed using an estimation method matched to the adhesion state. This allows for the use of different estimation methods to estimate vehicle speed under different adhesion states, reducing the impact of wheel slippage on the accuracy of the estimated vehicle speed and improving the accuracy and reliability of the overall vehicle speed.

[0217] For the above system embodiments, since they are basically similar to the vehicle speed estimation method embodiments, the relevant parts can be referred to in the description of the method embodiments.

[0218] This invention also provides a vehicle speed estimation device, such as... Figure 4 As shown, it includes a processor 401, a communication interface 402, a memory 403, and a communication bus 404, wherein the processor 401, the communication interface 402, and the memory 403 communicate with each other through the communication bus 404.

[0219] Memory 403 is used to store computer programs.

[0220] When the processor 401 executes the program stored in the memory 403, it performs the following steps: obtaining the slippage prediction labels corresponding to each wheel of the vehicle; determining the adhesion state of the vehicle according to the slippage prediction labels; and estimating the vehicle speed according to the estimation method matching the adhesion state of the vehicle to obtain the overall vehicle speed.

[0221] The processor 401 can also perform other steps in the above vehicle speed estimation method, which will not be described in detail here.

[0222] The communication bus mentioned in the aforementioned vehicle speed estimation device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0223] The communication interface is used for communication between the aforementioned vehicle speed estimation device and other devices.

[0224] The memory may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0225] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0226] In another embodiment of the present invention, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform the vehicle speed estimation method described in the above embodiments.

[0227] In another embodiment of the present invention, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute the vehicle speed estimation method described in the above embodiments.

[0228] In another embodiment of the present invention, a vehicle is also provided, including the vehicle speed estimation device described above.

[0229] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk (SSD)).

[0230] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0231] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. For embodiments of the apparatus, computer-readable storage medium, and computer program products containing instructions therein, the descriptions are relatively simple because they are substantially similar to the method embodiments; relevant parts can be referred to the descriptions of the method embodiments.

[0232] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A method for estimating vehicle speed, characterized in that, include: Obtain the vehicle's overall acceleration and the wheel speed of each wheel; Based on the acceleration of the wheel and the acceleration of the whole vehicle, it is determined whether the wheel meets the first slip condition. The wheel meets the first slip condition when the absolute value of the difference between the acceleration of the wheel and the acceleration of the whole vehicle is greater than a first threshold. The acceleration of the wheel is determined based on the wheel speed. The first vehicle speed is determined based on the overall vehicle acceleration. Determine the second vehicle speed based on the wheel speed of the aforementioned wheels; Based on the first vehicle speed and the second vehicle speed, determine whether the wheel meets the second slip condition; When the wheel simultaneously meets both the first slip condition and the second slip condition, the slip prediction label of the wheel is set to slip. Otherwise, set the wheel slippage prediction label to no slippage; Based on the slippage prediction label, the corresponding adhesion state of the vehicle is determined; the adhesion state is one of the following: no slippage, single-wheel slippage, two-wheel slippage, three-wheel slippage, and four-wheel slippage. When the attachment state is the non-slip state, the two-wheel slip state, or the three-wheel slip state, the average wheel speed corresponding to the wheel with the slip prediction label of non-slip is obtained, and the reference vehicle speed is determined based on the average wheel speed. When the adhesion state is the single-wheel slippage state, if the vehicle is in an acceleration state, a reference vehicle speed is determined based on the first wheel speed; if the vehicle is in a braking state, the reference vehicle speed is determined based on the second wheel speed, wherein the first wheel speed is the maximum value among the wheel speeds corresponding to the wheels of the vehicle, and the second wheel speed is the minimum value among the wheel speeds corresponding to the wheels of the vehicle. When the adhesion state is the four-wheel slipping state, a reference vehicle speed is determined based on the vehicle speed corresponding to the target time before the current time and the vehicle's overall acceleration. After obtaining the reference vehicle speed, the reference vehicle speed and the vehicle acceleration are input into a Kalman filter to obtain the vehicle speed.

2. The vehicle speed estimation method according to claim 1, characterized in that, Obtaining the wheel speed of the wheel includes: Determine whether the wheel speed sensor corresponding to the wheel is malfunctioning; If the wheel speed sensor is not faulty, the first wheel speed collected by the wheel speed sensor is acquired, and the first wheel speed is determined as the wheel speed of the wheel; If the wheel speed sensor fails, the second wheel speed corresponding to the wheel is determined based on the motor resolver signal corresponding to the wheel, and the second wheel speed is determined as the wheel speed of the wheel.

3. The vehicle speed estimation method according to claim 1, characterized in that, To obtain the vehicle's acceleration, including: Obtain the longitudinal acceleration and vertical acceleration corresponding to the position of the vehicle's center of mass; The vehicle acceleration is determined based on the longitudinal acceleration and the vertical acceleration.

4. The vehicle speed estimation method according to claim 1, characterized in that, Based on the first vehicle speed and the second vehicle speed, determine whether the wheel meets the second slip condition, including: When the first vehicle speed is less than or equal to a preset speed threshold, if the absolute value of the difference between the first vehicle speed and the second vehicle speed is greater than a second threshold, it is determined that the wheel meets the second slip condition. When the first vehicle speed is greater than the preset speed threshold, if the ratio of the absolute value of the difference between the first vehicle speed and the second vehicle speed to the first vehicle speed is greater than a third threshold, it is determined that the wheel meets the second slip condition.

5. The vehicle speed estimation method according to claim 1, characterized in that, Based on the slippage prediction label, the corresponding adhesion state of the vehicle is determined, including: The vehicle's adhesion state is determined based on the number of target wheels corresponding to the slippage prediction label. Specifically, when the target quantity is 0, the adhesion state is a non-slip state; when the target quantity is 1, the adhesion state is a single-wheel slipping state; when the target quantity is 2, the adhesion state is a two-wheel slipping state; when the target quantity is 3, the adhesion state is a three-wheel slipping state; and when the target quantity is 4, the adhesion state is a four-wheel slipping state.

6. The vehicle speed estimation method according to claim 1, characterized in that, Before determining whether the wheel meets the first slip condition based on the wheel's acceleration and the vehicle's overall acceleration, the method further includes: The vehicle acceleration, the wheel speed, and the wheel acceleration are subjected to first-order filtering.

7. A vehicle speed estimation device, characterized in that, include: The system includes a processor, a communication interface, a memory, and a communication bus; the processor, communication interface, and memory communicate with each other via the communication bus. Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the steps in the vehicle speed estimation method as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the vehicle speed estimation method as described in any one of claims 1 to 6.

9. A vehicle, characterized in that, Includes the vehicle speed estimation device as described in claim 7.

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