Vehicle attachment coefficient estimation method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention relates to a vehicle adhesion coefficient estimation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a tire slip angle, a tire rotation speed and a relative movement speed of a tire and the ground which are collected for a tire, and a transverse acceleration and a longitudinal acceleration which are collected for the vehicle in a vehicle driving process; determining a tire slip rate based on the tire rotation speed and the relative movement speed; determining a used adhesion coefficient between tires of the vehicle and the ground based on the lateral acceleration and the longitudinal acceleration; and performing adhesion coefficient prediction on the tire slip rate, the tire slip angle and the used adhesion coefficient by using a preset adhesion coefficient estimation model to obtain a predicted adhesion coefficient of the vehicle. According to the embodiment of the invention, the tire slip rate and the tire slip angle can be introduced into the estimation of the adhesion coefficient, and the non-linear factor of the estimation of the adhesion coefficient is reflected, so that the adhesion coefficient between the vehicle and the ground can be accurately and quickly estimated.

Description

Technical Field

[0001] This application relates to the technical field of intelligent vehicles, and in particular, to a method, device, electronic device, and storage medium for estimating vehicle adhesion coefficient. Background Art

[0002] In vehicle dynamic control algorithms, it is usually necessary to use the adhesion coefficient to calculate braking distance, acceleration performance, steering performance, etc. Traditional algorithms calculate in real time according to mechanical formulas, and this calculation method has the following problems:

[0003] 1. The adhesion coefficient can only be calculated accurately when the vehicle dynamics are large. With the development of technology, the control algorithm of the by-wire chassis needs to evaluate the vehicle state for pre-control before the vehicle dynamics become severe. The current adhesion coefficient algorithm cannot meet the requirements.

[0004] 2. When performing the driving operation of counter-steering the steering wheel, the lateral force of the tire will pass through zero, and the adhesion coefficient calculation during this period is inaccurate. Summary of the Invention

[0005] In view of this, to solve the above-mentioned part or all of the technical problems, embodiments of this application provide a method, device, electronic device, and storage medium for estimating vehicle adhesion coefficient.

[0006] In a first aspect, embodiments of this application provide a method for estimating vehicle adhesion coefficient. The method includes: obtaining the tire slip angle, tire rotation speed, relative moving speed between the tire and the ground collected for the tire, and the lateral acceleration and longitudinal acceleration collected for the vehicle during the vehicle driving process; determining the tire slip ratio based on the tire rotation speed and the relative moving speed; determining the used adhesion coefficient between the vehicle tire and the ground based on the lateral acceleration and the longitudinal acceleration; and predicting the adhesion coefficient for the tire slip ratio, tire slip angle, and used adhesion coefficient by using a preset adhesion coefficient estimation model to obtain the predicted adhesion coefficient of the vehicle.

[0007] In a possible implementation manner, predicting the adhesion coefficient for the tire slip ratio, tire slip angle, and used adhesion coefficient by using a preset adhesion coefficient estimation model to obtain the predicted adhesion coefficient of the vehicle includes: estimating the ground potential for the tire slip ratio and the tire slip angle by using the adhesion coefficient estimation model to obtain the potential adhesion coefficient; and calculating the predicted adhesion coefficient based on the used adhesion coefficient and the potential adhesion coefficient.

[0008] In a possible implementation, based on the lateral acceleration and longitudinal acceleration, determining the utilized adhesion coefficient between the tires of the vehicle and the ground includes: determining the yaw angular velocity of the vehicle; for each tire on the vehicle, determining the distance between the axle where the tire is located and the center of mass of the vehicle as the center-of-mass distance, and determining the distance between the tire and the opposite coaxial tire as the inter-wheel distance; based on the center-of-mass distance, the inter-wheel distance, as well as the lateral acceleration, longitudinal acceleration, and yaw angular velocity, determining the tire lateral acceleration, tire longitudinal acceleration, and tire normal acceleration corresponding to the tire, where the tire normal acceleration is the acceleration corresponding to the direction of the normal force of the tire; based on the tire lateral acceleration, tire longitudinal acceleration, and tire normal acceleration, determining the utilized adhesion coefficient corresponding to the tire.

[0009] In a possible implementation, using a preset adhesion coefficient estimation model to perform adhesion coefficient prediction on the tire slip ratio, tire sideslip angle, and utilized adhesion coefficient to obtain the predicted adhesion coefficient of the vehicle includes: for each tire on the vehicle, using the preset adhesion coefficient estimation model to perform adhesion coefficient prediction on the utilized adhesion coefficient, tire slip ratio, and tire sideslip angle corresponding to the tire to obtain the initial predicted adhesion coefficient corresponding to the tire; based on the obtained initial predicted adhesion coefficients, determining the predicted adhesion coefficient of the vehicle.

[0010] In a possible implementation, based on the obtained initial predicted adhesion coefficients, determining the predicted adhesion coefficient of the vehicle includes: based on the tire slip ratio and tire sideslip angle respectively corresponding to each tire on the vehicle, determining the dynamic coefficient corresponding to each tire; based on the obtained dynamic coefficients, determining the target initial predicted adhesion coefficient from the initial predicted adhesion coefficients respectively corresponding to each tire as the predicted adhesion coefficient of the vehicle.

[0011] In a possible implementation, based on the tire rotational speed and relative moving speed, determining the tire slip ratio includes: calculating the absolute value of the difference between the tire rotational speed and the relative moving speed; if the tire rotational speed is greater than or equal to the relative moving speed, dividing the absolute value by the tire rotational speed to obtain the tire slip ratio; if the tire rotational speed is less than the relative moving speed, dividing the absolute value by the relative moving speed to obtain the tire slip ratio.

[0012] In a possible implementation, the adhesion coefficient estimation model is pre-trained according to the following steps: Under preset normal conditions and extreme conditions, obtain a set of sample data collected when the vehicle travels on at least one road surface. Each set of sample data in the set of sample data includes a sample tire slip angle, a sample tire rotation speed, a sample relative moving speed, a sample lateral acceleration, a sample longitudinal acceleration, and a sample yaw rate; Based on the set of sample data, calculate the sample used adhesion coefficient and the sample tire slip ratio corresponding to each road surface in at least one road surface; For each road surface in at least one road surface, determine, from the obtained various used adhesion coefficients, the sample used adhesion coefficient corresponding to this road surface and under extreme conditions as the sample predicted adhesion coefficient corresponding to this road surface; Use the sample used adhesion coefficient, the sample predicted adhesion coefficient, the sample tire slip angle, and the sample tire slip ratio to adjust the parameters of the preset initial adhesion coefficient estimation model, and use the initial adhesion coefficient estimation model with adjusted parameters as the trained adhesion coefficient estimation model.

[0013] In a second aspect, an embodiment of the present application provides a vehicle adhesion coefficient estimation device, which includes: an acquisition module, configured to acquire a tire slip angle, a tire rotation speed, and a relative moving speed between the tire and the ground collected for the tire during the vehicle's travel, as well as a lateral acceleration and a longitudinal acceleration collected for the vehicle; a first determination module, configured to determine the tire slip ratio based on the tire rotation speed and the relative moving speed; a second determination module, configured to determine the used adhesion coefficient between the vehicle's tire and the ground based on the lateral acceleration and the longitudinal acceleration; a prediction module, configured to use a preset adhesion coefficient estimation model to perform adhesion coefficient prediction on the tire slip ratio, the tire slip angle, and the used adhesion coefficient to obtain the predicted adhesion coefficient of the vehicle.

[0014] In a third aspect, an embodiment of the present application provides an electronic device, which includes: a memory, configured to store a computer program; a processor, configured to execute the computer program stored in the memory, and when the computer program is executed, implement the method of any one of the embodiments of the vehicle adhesion coefficient estimation method in the first aspect of the present application.

[0015] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the method of any one of the embodiments of the vehicle adhesion coefficient estimation method in the first aspect as described above.

[0016] In a fifth aspect, an embodiment of the present application provides a computer program, which includes computer-readable code. When the computer-readable code runs on a device, it causes the processor in the device to implement the method of any one of the embodiments of the vehicle adhesion coefficient estimation method in the first aspect as described above.

[0017] The vehicle adhesion coefficient estimation method, device, electronic device, and storage medium provided by the embodiments of the present application collect the tire sideslip angle, tire rotation speed, relative movement speed between the tire and the ground, as well as the vehicle's lateral acceleration, longitudinal acceleration, and yaw rate. Based on the tire rotation speed and relative movement speed, the tire slip ratio is determined. Based on the lateral acceleration, longitudinal acceleration, and yaw rate, the used adhesion coefficient between the vehicle's tire and the ground is determined. Using the adhesion coefficient estimation model, the adhesion coefficient is predicted for the tire slip ratio, tire sideslip angle, and used adhesion coefficient, and the predicted adhesion coefficient of the vehicle is obtained. Based on calculating the used adhesion coefficient using mechanical formulas, the embodiments of the present application utilize the adhesion coefficient estimation model to introduce the tire slip ratio and tire sideslip angle into the estimation of the adhesion coefficient. This model can reflect the influence of the interaction between the ground and the tire on the adhesion coefficient estimation and also reflects the non-linear factors in the adhesion coefficient estimation, thereby enabling accurate and rapid estimation of the adhesion coefficient between the vehicle and the ground, which helps improve the accuracy and reliability of the by-wire chassis control algorithm. Additionally, when the vehicle's dynamics are small, the adhesion coefficient can be accurately calculated using this adhesion coefficient estimation model, and when the vehicle changes direction, it is not affected by the zero crossing point of the tire lateral force. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0019] To more clearly illustrate the technical solutions in the embodiments of the present application or in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0020] One or more embodiments are exemplarily illustrated by the pictures in the corresponding accompanying drawings. These exemplary illustrations do not limit the embodiments. Elements with the same reference numerals in the drawings are represented as similar elements, unless otherwise stated, and the drawings in the figures do not constitute a proportional limitation.

[0021] Figure 1 It is a schematic flowchart of a vehicle adhesion coefficient estimation method provided by the embodiments of the present application;

[0022] Figure 2 It is a schematic flowchart of a second vehicle adhesion coefficient estimation method provided by the embodiments of the present application;

[0023] Figure 3 It is a schematic flowchart of a third vehicle adhesion coefficient estimation method provided by the embodiments of the present application;

[0024] Figure 4 Schematic flowchart of the fourth method for estimating vehicle adhesion coefficient provided by an embodiment of the present application;

[0025] Figure 5 Flowchart for calculating the predicted adhesion coefficient of a vehicle for four tires;

[0026] Figure 6 Schematic flowchart of the fifth method for estimating vehicle adhesion coefficient provided by an embodiment of the present application;

[0027] Figure 7 Schematic flowchart of the process for training an adhesion coefficient estimation model provided by an embodiment of the present application;

[0028] Figure 8 Schematic structural diagram of a vehicle adhesion coefficient estimation device provided by an embodiment of the present application;

[0029] Figure 9 Schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0030] Now, various exemplary embodiments of the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. It should be noted that: unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions, and values set forth in these embodiments do not limit the scope of the present application.

[0031] Those skilled in the art can understand that terms such as "first", "second", etc. in the embodiments of the present application are only used to distinguish different steps, devices, or modules, etc., and do not represent any specific technical meaning, nor do they represent the logical order between them.

[0032] It should also be understood that in this embodiment, "a plurality" may refer to two or more, and "at least one" may refer to one, two, or more.

[0033] It should also be understood that for any component, data, or structure mentioned in the embodiments of the present application, without clear limitation or contrary indication in the context, it is generally understood to be one or more.

[0034] In addition, the term "and / or" in the present application is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in the present application generally represents an "or" relationship between the associated objects before and after.

[0035] It should also be understood that the description of each embodiment in this application emphasizes the differences between the embodiments, and their similarities or similarities can be referred to each other. For the sake of brevity, they will not be elaborated one by one.

[0036] The following description of at least one exemplary embodiment is actually only illustrative and in no way limits the application or use of this application.

[0037] Techniques, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the above techniques, methods, and devices should be regarded as part of the specification.

[0038] It should be noted that like reference numerals and letters indicate like items in the following figures, and thus, once an item is defined in one figure, it does not need to be further discussed in subsequent figures.

[0039] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. To facilitate the understanding of the embodiments of this application, the following will refer to the accompanying drawings and combine with the embodiments to detail this application. Obviously, the described embodiments are part of the embodiments of this application, rather than all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application.

[0040] To solve the technical problem of low accuracy in estimating the adhesion coefficient between a vehicle and the ground in the prior art, this application provides a method for estimating the vehicle adhesion coefficient, which improves the accuracy of estimating the adhesion coefficient by using a pre-trained adhesion coefficient estimation model.

[0041] Figure 1 It is a schematic flow diagram of a method for estimating the vehicle adhesion coefficient provided by an embodiment of this application. This method can be applied to a vehicle and executed by a controller on the vehicle. Additionally, it can also be executed by other electronic devices connected to the vehicle, such as one or more electronic devices like a smartphone, a laptop computer, a desktop computer, a portable computer, a server, etc. Furthermore, the execution subject of this method can be hardware or software. When the above execution subject is hardware, the execution subject can be one or more of the above electronic devices. For example, a single electronic device can execute this method, or multiple electronic devices can cooperate with each other to execute this method. When the above execution subject is software, this method can be implemented as multiple software or software modules, or can also be implemented as a single software or software module. No specific limitation is made here.

[0042] As Figure 1 shown, the method specifically includes:

[0043] Step 101: Obtain the tire sideslip angle, tire rotation speed, relative moving speed between the tire and the ground, lateral acceleration, and longitudinal acceleration collected for the tire during vehicle driving.

[0044] In some embodiments, the tire sideslip angle can represent the degree of deflection of the tire. The tire sideslip angle can be collected by an angle sensor on the vehicle. In this step, the tire sideslip angle, tire rotation speed, and relative moving speed of each tire of the vehicle can be collected, or the tire sideslip angle, tire rotation speed, and relative moving speed of one or more of the tires can be collected.

[0045] The above-mentioned tire rotation speed is usually the linear speed of the tire and can be collected by a rotation speed sensor.

[0046] The above-mentioned relative moving speed can be collected by a vehicle moving speed sensor.

[0047] The above-mentioned lateral acceleration and longitudinal acceleration can be collected by an acceleration sensor.

[0048] Step 102: Determine the tire slip ratio based on the tire rotation speed and the relative moving speed.

[0049] In some embodiments, the tire slip ratio represents the degree of slip between the tire and the ground during rotation. The tire slip ratio is related to the tire rotation speed and the relative moving speed. As an example, the tire slip ratio can be the quotient obtained by dividing the difference between the relative moving speed and the tire rotation speed by the relative moving speed.

[0050] Step 103: Determine the used adhesion coefficient between the vehicle's tire and the ground based on the lateral acceleration and the longitudinal acceleration.

[0051] In some embodiments, the used adhesion coefficient between the tire and the ground can be calculated according to a preset mechanical formula.

[0052] As an example, the used adhesion coefficient can be calculated by the following formula:

[0053]

[0054] where F x is the force in the x direction (towards the front of the vehicle) in the vehicle coordinate system, F y is the force in the y direction (i.e., the direction perpendicular to the front of the vehicle), and F z is the gravity of the vehicle.

[0055] Equation (1) can be simplified to:

[0056]

[0057] where μused is the adhesion coefficient used by the whole vehicle, a x is the longitudinal acceleration, a y is the lateral acceleration, and g is the acceleration due to gravity.

[0058] Using Equation (2), the used adhesion coefficient can be calculated.

[0059] Optionally, the used adhesion coefficient can also be calculated using the above lateral acceleration, longitudinal acceleration, and yaw rate. The used adhesion coefficient can be calculated for each tire or for the vehicle as a whole. When calculated for each tire, the used adhesion coefficient corresponding to each tire can be obtained.

[0060] Step 104: Use a preset adhesion coefficient estimation model to predict the adhesion coefficient for the tire slip ratio, tire sideslip angle, and used adhesion coefficient, to obtain the predicted adhesion coefficient of the vehicle.

[0061] In some embodiments, the adhesion coefficient estimation model can represent the corresponding relationship between the tire slip ratio, tire sideslip angle, used adhesion coefficient, and predicted adhesion coefficient. The adhesion coefficient estimation model can be implemented through a calculation formula. Substitute the tire slip ratio, tire sideslip angle, and used adhesion coefficient into the calculation formula to calculate the predicted adhesion coefficient.

[0062] The adhesion coefficient estimation model can also be implemented through a pre-statistical table. According to the tire slip ratio, tire sideslip angle, and used adhesion coefficient, look up the corresponding predicted adhesion coefficient in the table.

[0063] The adhesion coefficient estimation model can also be obtained by pre-training a basic model using machine learning methods. That is, sample data is obtained in advance, and machine learning methods are used to adjust the parameters of the initial model until the training end condition is reached, and the initial model with adjusted parameters is used as the adhesion coefficient estimation model.

[0064] The vehicle adhesion coefficient estimation method provided by the embodiments of the present application collects the tire sideslip angle, tire rotation speed, relative moving speed between the tire and the ground, as well as the lateral acceleration, longitudinal acceleration and yaw rate of the vehicle. Based on the tire rotation speed and relative moving speed, the tire slip ratio is determined. Based on the lateral acceleration, longitudinal acceleration and yaw rate, the used adhesion coefficient between the vehicle's tire and the ground is determined. Using the adhesion coefficient estimation model, the tire slip ratio, tire sideslip angle and used adhesion coefficient are used for adhesion coefficient prediction to obtain the predicted adhesion coefficient of the vehicle. Based on the use of mechanical formulas to calculate the used adhesion coefficient, the embodiments of the present application use the adhesion coefficient estimation model, which can introduce the tire slip ratio and tire sideslip angle into the estimation of the adhesion coefficient. This model can reflect the influence of the interaction between the ground and the tire on the adhesion coefficient estimation, and at the same time reflects the non-linear factors in the adhesion coefficient estimation, so as to accurately and quickly estimate the adhesion coefficient between the vehicle and the ground, which helps to improve the accuracy and reliability of the by-wire chassis control algorithm. In addition, when the vehicle's dynamics are small, the adhesion coefficient can be accurately calculated using this adhesion coefficient estimation model. When the vehicle changes direction, it is not affected by the zero-crossing point of the tire lateral force.

[0065] In some alternative implementation manners of this embodiment, as Figure 2 shown, step 104 includes:

[0066] Step 1041, using the adhesion coefficient estimation model, estimate the ground potential of the tire slip ratio and the tire sideslip angle to obtain the potential adhesion coefficient.

[0067] Specifically, the calculation of the predicted adhesion coefficient μ is divided into two parts. One part is the used adhesion coefficient μ used , and the other part is the ground potential data representing the influence of the vehicle motion state on the adhesion coefficient. The ground potential data is characterized by the tire sideslip angle α and the tire slip ratio λ, as shown in the following formula:

[0068] μ = μ used + μ p (3)

[0069] Among them, μ p represents the ground potential, and its relationship with α and λ is non-linear and is related to μ used . Therefore, the listed regression model is as follows:

[0070]

[0071] Among them, k0, k1, and k2 are parameters. Linearizing the model gives:

[0072]

[0073] Taking the logarithm again gives:

[0074]

[0075] Among them, β0, β1, and β2 are parameters. β1α + β2λ can be used as the potential adhesion coefficient representing the ground potential.

[0076] Step 1042: Calculate the predicted adhesion coefficient based on the used adhesion coefficient and the potential adhesion coefficient.

[0077] According to the above formula (6), μ used , α, λ, β0, β1, and β2 are known quantities. Substituting these into the formula, the predicted adhesion coefficient μ can be calculated.

[0078] In this embodiment, by calculating the tire slip ratio and the tire sideslip angle, the potential adhesion coefficient is obtained, realizing the quantitative calculation of the influence of the vehicle's motion state on the ground adhesion potential. The predicted adhesion coefficient includes the ground adhesion potential, so that the predicted adhesion coefficient is closer to the actual motion state of the vehicle. Regardless of the vehicle's motion state, the adhesion coefficient can be accurately calculated. At the same time, a linearizable regression model is used, which has high interpretability while ensuring the prediction accuracy.

[0079] In some alternative implementation manners of this embodiment, as Figure 3 shown, step 103 includes:

[0080] Step 1031: Determine the yaw rate of the vehicle.

[0081] Among them, the yaw rate of the vehicle can be obtained by dividing the yaw angle measured by the yaw angle sensor by the measurement period.

[0082] Step 1032: For each tire on the vehicle, determine the distance between the axle where the tire is located and the center of mass of the vehicle as the center-of-mass distance, and determine the distance between the tire and the coaxial tire opposite to it as the inter-wheel distance.

[0083] Specifically, the axles of the vehicle include the front axle and the rear axle, on which two front wheels and two rear wheels are respectively installed. The distances between the two front wheels and between the two rear wheels are the inter-wheel distances.

[0084] Step 1033: Based on the center-of-mass distance, the inter-wheel distance, as well as the lateral acceleration, longitudinal acceleration, and yaw rate, determine the tire lateral acceleration, tire longitudinal acceleration, and tire normal acceleration corresponding to the tire.

[0085] Among them, the tire normal acceleration is the acceleration corresponding to the direction of the normal force of the tire.

[0086] Specifically, for the left front wheel, the tire lateral acceleration a yFL , and the tire longitudinal acceleration a xFLand the normal acceleration a of the tire nFL The calculation formula is as follows:

[0087] a xFL = a x -(ddψ * l yFa / 2) (7)

[0088] a yFL = a y +(ddψ * l xFa ) (8)

[0089] a nFL = g - a x ρ x - a y ρ y (9)

[0090] For the right front wheel, the lateral acceleration a of the tire yFR , the longitudinal acceleration a of the tire xFR and the normal acceleration a of the tire nFR The calculation formula is as follows:

[0091] a xFR = a x +(ddψ * l yFa / 2) (10)

[0092] a yFR = a y +(ddψ * l xFa ) (11)

[0093] a nFR = g - a x ρ x + a y ρ y (12)

[0094] For the left rear wheel, the lateral acceleration a of the tire yRL , the longitudinal acceleration a of the tire xRL and the normal acceleration a of the tire nRL The calculation formula is as follows:

[0095] a xRL = a x -(ddψ * l yRa / 2) (13)

[0096] a yRL = a y -(ddψ * l xRa ) (14)

[0097] a nRL = g + a x ρx -a y ρ y (15)

[0098] For the right rear wheel, the tire lateral acceleration a yRR , the tire longitudinal acceleration a xRR and the tire normal acceleration a nRR are calculated as follows:

[0099] a xRR = a x +(ddψ * l yRa / 2) (16)

[0100] a yRR = a y -(ddψ * l xRa ) (17)

[0101] a nRR = g + a x ρ x + a y * ρ y (18)

[0102] where l xFa , l-- xRa are the distances from the center of mass to the front axle and the rear axle respectively, i.e., the center of mass distance; l yFa , l yRa are the track widths of the front and rear axles respectively, i.e., the inter-wheel distance; ρ x , ρ y are the influence factors of a x , a y on the normal forces on the front and rear axles and the left and right wheels respectively, and ddψ is the yaw angular acceleration.

[0103] Step 1034: Based on the tire lateral acceleration, the tire longitudinal acceleration, and the tire normal acceleration, determine the utilized adhesion coefficient corresponding to the tire.

[0104] Specifically, for each tire, the formula for calculating the utilized adhesion coefficient of the tire is as follows:

[0105]

[0106] where a x is the tire longitudinal acceleration of any tire, a y is the tire lateral acceleration of any tire, and a n is the tire normal acceleration of any tire.

[0107] It should be understood that the above Step 1032 - Step 1034 are steps for each tire, i.e., the above "the tire" refers to a tire targeted by the current calculation process.

[0108] This embodiment realizes accurately calculating the used adhesion coefficient of each tire, and can calculate the corresponding predicted adhesion coefficient for each tire, so as to more comprehensively detect the adhesion situation between the vehicle and the ground and improve the accuracy of calculating the predicted adhesion coefficient of the vehicle.

[0109] In some alternative implementation manners of this embodiment, as Figure 3 shown, step 104 includes:

[0110] Step 1043: For each tire on the vehicle, use a preset adhesion coefficient estimation model to perform adhesion coefficient prediction on the used adhesion coefficient, tire slip ratio, and tire sideslip angle corresponding to the tire, and obtain the initial predicted adhesion coefficient corresponding to the tire.

[0111] In this embodiment, the tire slip ratio and tire sideslip angle of each tire are obtained in advance, and for each tire, the corresponding initial predicted adhesion coefficient can be calculated.

[0112] Optionally, the above formula (6) can be used to substitute the tire slip ratio λ, tire sideslip angle α, and used adhesion coefficient μ used into the formula to calculate the initial predicted adhesion coefficient μ corresponding to each tire.

[0113] Step 1044: Based on the obtained initial predicted adhesion coefficients, determine the predicted adhesion coefficient of the vehicle.

[0114] Specifically, the predicted adhesion coefficient of the vehicle can be determined according to various set rules. For example, one of them can be selected as the predicted adhesion coefficient of the vehicle from the initial predicted adhesion coefficients according to a certain rule; or, the average of the initial predicted adhesion coefficients can also be taken to obtain the predicted adhesion coefficient of the vehicle.

[0115] This embodiment calculates the initial predicted adhesion coefficient of each tire, and obtains the predicted adhesion coefficient of the vehicle based on the initial predicted adhesion coefficients, which can more comprehensively cover the adhesion situation between the tires at different positions and the ground and improve the accuracy of the predicted adhesion coefficient.

[0116] In some alternative implementation manners of this embodiment, as Figure 4 shown, step 1042 includes:

[0117] Step 10441: Based on the tire slip ratio and tire sideslip angle respectively corresponding to each tire on the vehicle, determine the dynamic coefficient corresponding to each tire.

[0118] Among them, the dynamic coefficient represents the influence degree of the motion state of the tire on the adhesion force. Generally, the dynamic coefficient of each tire can be calculated according to the following formula:

[0119]

[0120] Among them, α and λ are the tire sideslip angle and the tire slip ratio of any tire.

[0121] Step 10442: Based on the obtained various dynamic coefficients, determine the target initial predicted adhesion coefficient as the predicted adhesion coefficient of the vehicle from the initial predicted adhesion coefficients corresponding to each tire respectively.

[0122] Generally, the initial predicted adhesion coefficient corresponding to the maximum dynamic coefficient can be used as the predicted adhesion coefficient of the vehicle.

[0123] As Figure 5 shown, it shows a flowchart for calculating the predicted adhesion coefficient of the vehicle for four tires. Among them, FL, FR, RL, and RR respectively represent the left front wheel, right front wheel, left rear wheel, and right rear wheel of the vehicle. Four sets of used adhesion coefficients, tire slip ratios, and tire sideslip angles are input into the adhesion coefficient estimation model to obtain four initial predicted adhesion coefficients and four dynamic coefficients, and the maximum dynamic coefficient is determined as the final predicted adhesion coefficient μ.

[0124] In this embodiment, by calculating the dynamic coefficient of each tire, the finally obtained predicted adhesion coefficient can reflect the motion state of the tire to the greatest extent, thereby further improving the accuracy of the predicted adhesion coefficient.

[0125] In some alternative implementation manners of this embodiment, as Figure 6 shown, step 102 includes:

[0126] Step 1021: Calculate the absolute value of the difference between the tire rotation speed and the relative moving speed.

[0127] Step 1022: If the tire rotation speed is greater than or equal to the relative moving speed, divide the absolute value by the tire rotation speed to obtain the tire slip ratio.

[0128] Step 1023: If the tire rotation speed is less than the relative moving speed, divide the absolute value by the relative moving speed to obtain the tire slip ratio.

[0129] Specifically, for any tire, let V1 be the tire rotation speed and V2 be the relative moving speed of the tire and the ground. The tire slip ratio λ is as shown in the following formula:

[0130]

[0131] In the traditional slip rate calculation method, the divisor in the formula is fixed as V1 or V1, resulting in the numerical range of the slip rate being (-∞, 1). In this embodiment, the numerical range of the slip rate can be limited to (0, 1), making the calculated numerical range more concentrated and avoiding the reduction of prediction accuracy caused by excessive offset between numerical values.

[0132] In some alternative implementation manners of this embodiment, as Figure 7 shown, the adhesion coefficient estimation model is pre-trained according to the following steps:

[0133] Step 701, under preset normal conditions and extreme conditions, obtain a sample data set collected when the vehicle travels on at least one road surface.

[0134] Each group of sample data in the sample data set includes a sample tire slip angle, a sample tire rotation speed, a sample relative movement speed, a sample lateral acceleration, and a sample longitudinal acceleration.

[0135] As an example, the at least one road surface may include, but is not limited to, at least one of the following: dry asphalt, wet asphalt, tiles, wet tiles, snow surface, ice surface, etc. The normal conditions include, but are not limited to, at least one of the following: acceleration, braking, single lane change, double lane change, circular turning, etc. The ground extreme conditions include, but are not limited to, at least one of the following: full throttle acceleration, ABS braking, double lane change, etc.

[0136] Step 702, based on the sample data set, calculate the sample used adhesion coefficient and the sample tire slip rate corresponding to each road surface in the at least one road surface.

[0137] The method for calculating the sample used adhesion coefficient and the sample tire slip rate may be the same as the method for calculating the used adhesion coefficient and the tire slip rate in the above embodiments.

[0138] Step 703, for each road surface in the at least one road surface, determine, from the obtained various used adhesion coefficients, the sample used adhesion coefficient corresponding to this road surface and under extreme conditions as the sample predicted adhesion coefficient corresponding to this road surface.

[0139] That is, for one road surface, under extreme conditions, the used adhesion coefficient μ can be calculated according to the mechanical formula shown in the above formula (2) used , and this used adhesion coefficient is determined as the sample predicted adhesion coefficient μ corresponding to this road surface. Specifically, under extreme conditions, the vehicle moves violently, and the relationship between the driving state of the vehicle and the adhesion force with the ground is more obvious. At this time, the adhesion coefficient calculated by the mechanical formula is more accurate. Therefore, the sample used adhesion coefficient under extreme conditions can be used as the labeled data for training.

[0140] Step 704: Adjust the parameters of the preset initial adhesion coefficient estimation model by using the sample used adhesion coefficient, the sample predicted adhesion coefficient, the sample tire sideslip angle, and the sample tire slip ratio, and use the initial adhesion coefficient estimation model with adjusted parameters as the trained adhesion coefficient estimation model.

[0141] Specifically, the parameters of the initial adhesion coefficient estimation model can be adjusted according to the machine learning method to minimize the error between the predicted adhesion coefficient output by the model and the sample predicted adhesion coefficient. When the training end condition is met (such as error convergence, the number of training times reaches the preset number, etc.), the training is ended, and the model after the last parameter adjustment is used as the trained adhesion coefficient estimation model.

[0142] For the linearized model shown in the above formula (6), μ used is the sample used adhesion coefficient calculated by using the sample data, and μ is the sample predicted adhesion coefficient under a certain road surface. The sample tire slip ratio λ and the sample tire sideslip angle α are both known. After training, the parameters β0, β1, and β2 can be determined, and then the model can be used for prediction.

[0143] In this embodiment, by collecting training sample data in advance under different road surfaces and different working conditions, and using the used adhesion coefficient under extreme working conditions as the labeled data during training, the trained adhesion coefficient estimation model can have higher generalization ability and prediction accuracy.

[0144] Figure 8 It is a schematic structural diagram of a vehicle adhesion coefficient estimation device provided by an embodiment of the present application. Specifically, it includes:

[0145] An acquisition module 801, configured to acquire the tire sideslip angle, tire rotation speed, relative moving speed between the tire and the ground collected for the tire, and the lateral acceleration and longitudinal acceleration collected for the vehicle during the vehicle driving process;

[0146] A first determination module 802, configured to determine the tire slip ratio based on the tire rotation speed and the relative moving speed;

[0147] A second determination module 803, configured to determine the used adhesion coefficient between the tire of the vehicle and the ground based on the lateral acceleration and the longitudinal acceleration;

[0148] A prediction module 804, configured to use a preset adhesion coefficient estimation model to perform adhesion coefficient prediction on the tire slip ratio, tire sideslip angle, and used adhesion coefficient to obtain the predicted adhesion coefficient of the vehicle.

[0149] In a possible implementation, the prediction module includes: a first calculation unit configured to estimate the ground potential of the tire slip ratio and the tire sideslip angle by using an adhesion coefficient estimation model to obtain the potential adhesion coefficient; a second calculation unit configured to calculate a predicted adhesion coefficient based on the used adhesion coefficient and the potential adhesion coefficient.

[0150] In a possible implementation, the second determination module includes: a first determination unit configured to determine the yaw rate of the vehicle; a second determination unit configured to, for each tire on the vehicle, determine the distance between the axle where the tire is located and the center of mass of the vehicle as the center-of-mass distance, and determine the distance between the tire and the opposite coaxial tire as the inter-wheel distance; a third determination unit configured to determine the corresponding tire lateral acceleration, tire longitudinal acceleration, and tire normal acceleration of the tire based on the center-of-mass distance, the inter-wheel distance, as well as the lateral acceleration, longitudinal acceleration, and yaw rate, where the tire normal acceleration is the acceleration corresponding to the normal force direction of the tire; a fourth determination unit configured to determine the used adhesion coefficient corresponding to the tire based on the tire lateral acceleration, tire longitudinal acceleration, and tire normal acceleration.

[0151] In a possible implementation, the prediction module includes: a prediction unit configured to, for each tire on the vehicle, predict the adhesion coefficient of the used adhesion coefficient, tire slip ratio, and tire sideslip angle corresponding to the tire by using a preset adhesion coefficient estimation model to obtain the initial predicted adhesion coefficient corresponding to the tire; a fourth determination unit configured to determine the predicted adhesion coefficient of the vehicle based on the obtained initial predicted adhesion coefficients.

[0152] In a possible implementation, the fourth determination unit includes: a first determination subunit configured to determine the dynamic coefficient corresponding to each tire based on the tire slip ratio and the tire sideslip angle respectively corresponding to each tire on the vehicle; a second determination subunit configured to determine the target initial predicted adhesion coefficient as the predicted adhesion coefficient of the vehicle from the initial predicted adhesion coefficients respectively corresponding to each tire based on the obtained dynamic coefficients.

[0153] In a possible implementation, the first determination module includes: a third calculation unit configured to calculate the absolute value of the difference between the tire rotation speed and the relative moving speed; a fourth calculation unit configured to divide the absolute value by the tire rotation speed to obtain the tire slip ratio if the tire rotation speed is greater than or equal to the relative moving speed; a fifth calculation unit configured to divide the absolute value by the relative moving speed to obtain the tire slip ratio if the tire rotation speed is less than the relative moving speed.

[0154] In a possible implementation, the adhesion coefficient estimation model is pre-trained according to the following steps: Under preset normal operating conditions and extreme operating conditions, obtain a sample data set collected when the vehicle is driving on at least one road surface. Each set of sample data in the sample data set includes a sample tire slip angle, a sample tire rotation speed, a sample relative movement speed, a sample lateral acceleration, a sample longitudinal acceleration, and a sample yaw rate; Based on the sample data set, calculate the sample used adhesion coefficient and the sample tire slip ratio corresponding to each road surface in at least one road surface; For each road surface in at least one road surface, determine, from the obtained various used adhesion coefficients, the sample used adhesion coefficient corresponding to this road surface and under extreme operating conditions as the sample predicted adhesion coefficient corresponding to this road surface; Use the sample used adhesion coefficient, the sample predicted adhesion coefficient, the sample tire slip angle, and the sample tire slip ratio to adjust the parameters of the preset initial adhesion coefficient estimation model, and use the initial adhesion coefficient estimation model with adjusted parameters as the trained adhesion coefficient estimation model.

[0155] The vehicle adhesion coefficient estimation device provided in this embodiment may be the vehicle adhesion coefficient estimation device shown in Figure 8 and can execute all steps of the above vehicle adhesion coefficient estimation methods, thereby achieving the technical effects of the above vehicle adhesion coefficient estimation methods. For specific reference, please refer to the above relevant descriptions. For the sake of concise description, it will not be elaborated here.

[0156] Figure 9 It is a schematic structural diagram of an electronic device provided in an embodiment of the present application. Figure 9 The electronic device 900 shown includes: at least one processor 901, a memory 902, at least one network interface 904, and other user interfaces 903. Each component in the electronic device 900 is coupled together through a bus system 905. It can be understood that the bus system 905 is used to realize the connection and communication between these components. In addition to including a data bus, the bus system 905 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clear illustration, in Figure 9 all kinds of buses are labeled as the bus system 905.

[0157] Among them, the user interface 903 may include a display, a keyboard, or a pointing device (such as a mouse, a trackball, a touchpad, or a touch screen, etc.).

[0158] It can be understood that the memory 902 in the embodiments of the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synch link dynamic random access memory (SLDRAM), and direct rambus random access memory (DRRAM). The memory 902 described herein is intended to include but not be limited to these and any other suitable types of memory.

[0159] In some embodiments, the memory 902 stores the following elements, executable units or data structures, or subsets or supersets thereof: the operating system 9021 and the application programs 9022.

[0160] Among them, the operating system 9021 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application programs 9022 include various application programs, such as a media player and a browser, etc., for implementing various application services. The program for implementing the method of the embodiments of the present application can be included in the application programs 9022.

[0161] In this embodiment, by invoking the programs or instructions stored in the memory 902, specifically, the programs or instructions stored in the application programs 9022, the processor 901 is used to execute the method steps provided in each method embodiment, for example, including:

[0162] Obtain the tire sideslip angle, tire rotation speed, relative moving speed between the tire and the ground collected for the tire during vehicle driving, and the lateral acceleration and longitudinal acceleration collected for the vehicle; determine the tire slip ratio based on the tire rotation speed and the relative moving speed; determine the utilized adhesion coefficient between the vehicle's tire and the ground based on the lateral acceleration and longitudinal acceleration; use a preset adhesion coefficient estimation model to perform adhesion coefficient prediction on the tire slip ratio, tire sideslip angle, and utilized adhesion coefficient to obtain the predicted adhesion coefficient of the vehicle.

[0163] The method disclosed in the embodiments of the present application above can be applied to or implemented by the processor 901. The processor 901 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by the integrated logic circuit in hardware or instructions in software form in the processor 901. The above-mentioned processor 901 may be a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute each method, step, and logic block diagram disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed and completed by a hardware decoding processor, or by a combination of hardware and software units in the decoding processor. The software unit may be located in a mature storage medium in the art such as a random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, register, etc. This storage medium is located in the memory 902, and the processor 901 reads the information in the memory 902 and combines its hardware to complete the steps of the above method.

[0164] It can be understood that the embodiments described herein can be implemented by hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the above functions of the present application, or a combination thereof.

[0165] For software implementation, the above-described technologies herein can be implemented by units that execute the above functions herein. The software code can be stored in a memory and executed by a processor. The memory can be implemented within the processor or external to the processor.

[0166] The electronic device provided in this embodiment can be an electronic device as shown in Figure 9 which can execute all the steps of the above-described vehicle adhesion coefficient estimation methods, and thus achieve the technical effects of the above-described vehicle adhesion coefficient estimation methods. For specific details, please refer to the above relevant descriptions. For the sake of brevity, it will not be elaborated herein.

[0167] The embodiments of the present application further provide a storage medium (computer-readable storage medium). The storage medium stores one or more programs. Among them, the storage medium can include volatile memory, such as random access memory; the memory can also include non-volatile memory, such as read-only memory, flash memory, hard disk, or solid-state drive; the memory can also include a combination of the above types of memory.

[0168] When one or more programs in the storage medium can be executed by one or more processors to implement the vehicle adhesion coefficient estimation method executed on the electronic device side as described above.

[0169] The above processor is used to execute the program stored in the memory to implement the following steps of the vehicle adhesion coefficient estimation method executed on the electronic device side:

[0170] During the vehicle's driving, obtain the tire sideslip angle, tire rotation speed, relative moving speed between the tire and the ground collected for the tire, as well as the lateral acceleration and longitudinal acceleration collected for the vehicle; determine the tire slip ratio based on the tire rotation speed and relative moving speed; determine the utilized adhesion coefficient between the vehicle's tire and the ground based on the lateral acceleration and longitudinal acceleration; use a preset adhesion coefficient estimation model to perform adhesion coefficient prediction on the tire slip ratio, tire sideslip angle, and utilized adhesion coefficient, and obtain the predicted adhesion coefficient of the vehicle.

[0171] Those skilled in the art should also be able to further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0172] The steps of the methods or algorithms described in combination with the embodiments disclosed in this article can be implemented by hardware, software modules executed by a processor, or a combination of the two. The software modules can be placed in a random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium well-known in the technical field.

[0173] It should be understood that the terms used in this article are only for the purpose of describing specific example embodiments and are not intended to be restrictive. Unless otherwise clearly indicated in the context, the singular forms "a", "an", and "the" as used in this article may also represent the plural form. The terms "include", "comprise", "contain", and "have" are inclusive and thus specify the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or their combinations. The method steps, processes, and operations described in this article are not to be construed as necessarily requiring them to be executed in the specific order described or illustrated, unless the execution order is clearly indicated. It should also be understood that additional or alternative steps can be used.

[0174] The above are only specific embodiments of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but rather will be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for estimating the vehicle adhesion coefficient, characterized in that, The method includes: Obtaining, during the vehicle driving, the tire sideslip angle, tire rotation speed, relative moving speed between the tire and the ground collected for the tire, and the lateral acceleration and longitudinal acceleration collected for the vehicle; Determining the tire slip ratio based on the tire rotation speed and the relative moving speed; Determining the utilized adhesion coefficient between the tire of the vehicle and the ground based on the lateral acceleration and the longitudinal acceleration; Performing adhesion coefficient prediction on the tire slip ratio, the tire sideslip angle, and the utilized adhesion coefficient by using a preset adhesion coefficient estimation model to obtain the predicted adhesion coefficient of the vehicle.

2. The method according to claim 1, characterized in that, The performing adhesion coefficient prediction on the tire slip ratio, the tire sideslip angle, and the utilized adhesion coefficient by using a preset adhesion coefficient estimation model to obtain the predicted adhesion coefficient of the vehicle includes: Performing ground potential estimation on the tire slip ratio and the tire sideslip angle by using the adhesion coefficient estimation model to obtain the potential adhesion coefficient; Calculating the predicted adhesion coefficient based on the utilized adhesion coefficient and the potential adhesion coefficient.

3. The method according to claim 1, characterized in that The determining the utilized adhesion coefficient between the tire of the vehicle and the ground based on the lateral acceleration and the longitudinal acceleration includes: Determining the yaw angular velocity of the vehicle; For each tire on the vehicle, determining the distance between the axle where the tire is located and the center of mass of the vehicle as the center-of-mass distance, and determining the distance between the tire and the coaxial tire opposite thereto as the inter-wheel distance; Determining the tire lateral acceleration, tire longitudinal acceleration, and tire normal acceleration corresponding to the tire based on the center-of-mass distance, the inter-wheel distance, the lateral acceleration, the longitudinal acceleration, and the yaw angular velocity, where the tire normal acceleration is the acceleration corresponding to the direction of the normal force of the tire; Determining the utilized adhesion coefficient corresponding to the tire based on the tire lateral acceleration, the tire longitudinal acceleration, and the tire normal acceleration.

4. The method according to claim 3, characterized in that, The performing adhesion coefficient prediction on the tire slip ratio, the tire sideslip angle, and the utilized adhesion coefficient by using a preset adhesion coefficient estimation model to obtain the predicted adhesion coefficient of the vehicle includes: For each tire on the vehicle, performing adhesion coefficient prediction on the utilized adhesion coefficient, tire slip ratio, and tire sideslip angle corresponding to the tire by using a preset adhesion coefficient estimation model to obtain the initial predicted adhesion coefficient corresponding to the tire; Determining the predicted adhesion coefficient of the vehicle based on the obtained initial predicted adhesion coefficients.

5. The method according to claim 4, characterized in that, The determining the predicted adhesion coefficient of the vehicle based on the obtained initial predicted adhesion coefficients includes: Determining the dynamic coefficient corresponding to each tire based on the tire slip ratio and the tire sideslip angle respectively corresponding to each tire on the vehicle; Determining the target initial predicted adhesion coefficient as the predicted adhesion coefficient of the vehicle from the initial predicted adhesion coefficients respectively corresponding to each tire based on the obtained dynamic coefficients.

6. The method according to claim 1, wherein The determining the tire slip ratio based on the tire rotation speed and the relative moving speed includes: Calculating the absolute value of the difference between the tire rotation speed and the relative moving speed; If the rotational speed of the tire is greater than or equal to the relative movement speed, divide the absolute value by the rotational speed of the tire to obtain the tire slip ratio; If the rotational speed of the tire is less than the relative movement speed, divide the absolute value by the relative movement speed to obtain the tire slip ratio.

7. The method according to any one of claims 1 to 6, characterized in that, The adhesion coefficient estimation model is pre-trained according to the following steps: Under preset normal conditions and extreme conditions, obtain a set of sample data collected during the vehicle traveling on at least one type of road surface. Each set of sample data in the set of sample data includes a sample tire side slip angle, a sample tire rotational speed, a sample relative movement speed, a sample lateral acceleration, a sample longitudinal acceleration, and a sample yaw rate; Based on the set of sample data, calculate the sample used adhesion coefficient and the sample tire slip ratio corresponding to each road surface in the at least one type of road surface; For each road surface in the at least one type of road surface, among the obtained various used adhesion coefficients, determine the sample used adhesion coefficient corresponding to this road surface and under extreme conditions as the sample predicted adhesion coefficient corresponding to this road surface; Use the sample used adhesion coefficient, the sample predicted adhesion coefficient, the sample tire side slip angle, and the sample tire slip ratio to adjust the parameters of a preset initial adhesion coefficient estimation model, and use the initial adhesion coefficient estimation model with adjusted parameters as the trained adhesion coefficient estimation model.

8. A vehicle adhesion coefficient estimation device, characterized in that, The device includes: An acquisition module, configured to acquire the tire side slip angle, the tire rotational speed, the relative movement speed between the tire and the ground collected for the tire during the vehicle traveling, and the lateral acceleration and the longitudinal acceleration collected for the vehicle; A first determination module, configured to determine the tire slip ratio based on the tire rotational speed and the relative movement speed; A second determination module, configured to determine the used adhesion coefficient between the tire of the vehicle and the ground based on the lateral acceleration and the longitudinal acceleration; A prediction module, configured to use a preset adhesion coefficient estimation model to perform adhesion coefficient prediction on the tire slip ratio, the tire side slip angle, and the used adhesion coefficient to obtain the predicted adhesion coefficient of the vehicle.

9. An electronic device, characterized in that, Including: A memory, configured to store a computer program; A processor, configured to execute the computer program stored in the memory, and when the computer program is executed, implement the vehicle adhesion coefficient estimation method according to any one of claims 1-7 above.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, implement the vehicle adhesion coefficient estimation method according to any one of claims 1-7 above.

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