Method, apparatus, vehicle, and storage medium for recognizing road surface information in automobiles

Abstract

This invention discloses a method, apparatus, vehicle, and storage medium for recognizing road surface information for automobiles. [Solution] The method includes: collecting vehicle driving data, determining the vehicle's current driving status based on the vehicle driving data; calculating a first road surface parameter based on the vehicle driving data if the vehicle's current driving status is a first preset status, calculating a first adhesion coefficient based on the first parameter and a first preset policy; calculating a second road surface parameter based on the vehicle driving data if the vehicle's current driving status is a second preset status, obtaining a second adhesion coefficient based on the second parameter and a second preset policy; filtering the first or second adhesion coefficient to obtain a final adhesion coefficient value; and using the final adhesion coefficient value as an index, searching a pre-set standard road condition adhesion coefficient-road condition information relationship table to obtain current road condition information for the road surface. This saves hardware costs while effectively improving the accuracy of adhesion coefficient recognition and driving condition coverage.

Description

Technical Field

[0001] This application relates to the field of four-wheel drive system development technology, and particularly relates to a method and apparatus for recognizing road surface information of a vehicle, a vehicle, and a storage medium.

Background Art

[0002] As one of the important parameters of a four-wheel drive system, the road surface adhesion coefficient has a significant impact on the torque distribution of the four-wheel drive system and the yaw stability control of the ABS (Antilock Brake System), as well as other vehicle dynamics calculation systems. Therefore, it is of great significance to study the recognition of road surface adhesion conditions. As domestic and foreign scholars have recognized that the driving state parameters of a vehicle are important for the vehicle's active safety control system, domestic and foreign enterprises and universities have been vigorously working on the estimation of vehicle driving state parameters.

Summary of the Invention

Means for Solving the Problems

[0003] This application , own provides a method and apparatus for recognizing road surface information of a vehicle, a vehicle, and a storage medium.

[0004] An embodiment of a first aspect of this application relates to a method for recognizing road surface information of an automobile, the method comprising: collecting vehicle driving data; determining the current driving status of the vehicle based on the vehicle driving data; if the current driving status of the vehicle is a first preset status, calculating a first parameter of the road surface based on the vehicle driving data; calculating a first adhesion coefficient based on the first parameter and a first preset policy; if the current driving status of the vehicle is a second preset status, calculating a second parameter of the road surface based on the vehicle driving data; obtaining a second adhesion coefficient based on the second parameter and a second preset policy; filtering the first adhesion coefficient or the second adhesion coefficient to obtain a final adhesion coefficient value; and using the final adhesion coefficient value as an index, searching a preset standard road condition adhesion coefficient-road condition information relationship table to obtain current road condition information of the road surface.

[0005] As one option, in one embodiment of the present application, the vehicle driving data includes at least one of the vehicle speed, front wheel steering angle, yaw rate, lateral / longitudinal acceleration, engine output torque, gear position, and gradient signal.

[0006] As one option, in one embodiment of the present application, when the current driving conditions of the vehicle are a first preset condition, calculating a first parameter of the road surface based on the vehicle's driving data, and calculating a first adhesion coefficient based on the first parameter and a first preset policy, includes calculating the driving slip ratio and the usable adhesion coefficient of the road surface based on the vehicle's driving data, performing fuzzy processing on the driving slip ratio and the usable adhesion coefficient, obtaining six first similarity coefficients based on a preset fuzzy inference table, and calculating the first adhesion coefficient based on the first similarity coefficients.

[0007] As one option, in one embodiment of the present application, when the current driving conditions of the vehicle are those of a second preset condition, calculating a second parameter of the road surface based on the vehicle's driving data and obtaining a second adhesion coefficient based on the second parameter and a second preset policy includes calculating the absolute value of the vehicle's lateral acceleration and the absolute value of the yaw rate difference based on the vehicle's driving data, performing fuzzy logic on the absolute value of the lateral acceleration and the absolute value of the yaw rate difference to obtain a second similarity coefficient based on the preset fuzzy inference table, and obtaining the second adhesion coefficient based on the second similarity coefficient and a preset correction formula.

[0008] As one option, in one embodiment of this application, the formula for calculating the first adhesion coefficient is as follows: The filename is TIFF2026517544000158.tif5151.

[0009] Here, TIFF2026517544000159.tif57 is the first adhesion coefficient, TIFF2026517544000160.tif48 is the first similarity coefficient, TIFF2026517544000161.tif48 is the braking slip ratio.

[0010] As one option, in one embodiment of this application, the preset correction formula is as follows, namely, The filename is TIFF2026517544000162.tif3261.

[0011] Here, TIFF2026517544000163.tif59 is a correction function, TIFF2026517544000164.tif42 is the second similarity coefficient, TIFF2026517544000165.tif49 is a correction function coefficient, TIFF2026517544000166.tif34 is the second adhesion coefficient, TIFF2026517544000167.tif33 has a proportionality constant of 1.19. TIFF2026517544000168.tif44 is the lateral acceleration, TIFF2026517544000169.tif32 represents the acceleration due to gravity.

[0012] As one option, in one embodiment of this application, the final adhesion coefficient value is used as an index to search a pre-set standard road condition adhesion coefficient-road condition information relationship table to obtain current road condition information of the road surface, and then a presentation signal and / or feedback signal is generated based on the road condition information, and the presentation signal and / or feedback signal is received and the current road condition information of the road surface is fed back to the user in acoustic and / or optical form.

[0013] Embodiments of a second aspect of this application relate to a vehicle road surface information recognition device, the device comprising: a collection module used to collect vehicle driving data and determine the current driving status of the vehicle based on the vehicle driving data; a calculation module used to calculate a first parameter of the road surface based on the vehicle driving data and a first adhesion coefficient based on the first parameter and a first preset policy when the current driving status of the vehicle is a first preset status; a calculation module used to calculate a second parameter of the road surface based on the vehicle driving data and a second adhesion coefficient based on the second parameter and a second preset policy when the current driving status of the vehicle is a second preset status and to obtain a final adhesion coefficient value by filtering the first or second adhesion coefficient; and a search module used to obtain current road surface information by searching a preset standard road condition adhesion coefficient-road condition information relationship table using the final adhesion coefficient value as an index.

[0014] A third embodiment of this application relates to a vehicle, the vehicle including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to realize the method for recognizing road surface information of an automobile as described in the above embodiment.

[0015] A fourth embodiment of this application relates to a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the program is executed by a processor, the above-described method for recognizing road surface information of an automobile is realized.

[0016] Additional aspects and advantages of this application are, in part, shown in the following description, and in part, will become apparent from the following description or can be acquired by practicing this application. [Brief explanation of the drawing]

[0017] To more clearly explain the technical concepts in the embodiments of this application, the drawings used in the description of the embodiments will be briefly described below. The drawings in the following description are only a few embodiments of this application, and it will be obvious to those skilled in the art that other drawings can be obtained based on these drawings without any creative work.

[0018] [Figure 1] This is a flowchart illustrating a method for recognizing road surface information in an automobile according to an embodiment of this application. [Figure 2] This is a schematic diagram of a hardware interaction according to one embodiment of the present application. [Figure 3] This is a schematic diagram showing the execution logic of a method for recognizing road surface information of an automobile according to one embodiment of this application. [Figure 4] This is a schematic diagram of the fuzzy recognition flow of the road surface adhesion coefficient in linear driving conditions according to one embodiment of this application. [Figure 5] This is a schematic diagram of a part of a fuzzy inference rule according to one embodiment of this application. [Figure 6]It is a schematic diagram of a fuzzy recognition flow of a road surface adhesion coefficient in a non-linear driving situation according to an embodiment of the present application. [Figure 7] It is a schematic diagram showing an execution logic of a filtering algorithm according to an embodiment of the present application. [Figure 8] It is a schematic diagram showing an execution logic of another filtering algorithm according to an embodiment of the present application. [Figure 9] It is a schematic diagram of a recognition device for road surface information of an automobile according to an embodiment of the present application. [Figure 10] It is a schematic diagram of a vehicle configuration according to an embodiment of the present application.

Mode for Carrying Out the Invention

[0019] Technical terms or scientific terms used in the present application should have the ordinary meaning understood by those skilled in the art unless otherwise defined. The terms "first", "second", "third" and similar terms used in the specification and claims of the present application are not for representing any order, number or importance, but merely for distinguishing different components. Similarly, similar terms such as "one" are not for indicating a limitation of number, but for indicating that at least one exists. Similar terms such as "comprising" or "including" mean that the element or object appearing before the term "comprising" or "including" covers the elements or objects listed after the term "comprising" or "including" and their equivalents without excluding other elements or objects. Similar terms such as "connected" or "linked" are not limited to physical or mechanical connections, and can be interpreted to include electrical connections whether direct or indirect. Terms such as "above", "below", "left", "right" are used only for representing relative positional relationships, and when the absolute position of the object described changes, the relative positional relationship may also change accordingly. The term "and / or" describes an association between related objects and may indicate three types of relationships. For example, A and / or B may indicate the following cases: A alone exists, both A and B exist, and B alone exists. The letter " / " indicates an "or" relationship between related objects.

[0020] Conventional methods primarily rely on sensors to measure and obtain vehicle driving state parameters. While these methods utilize measuring instruments, offering high accuracy and practicality, their high measurement costs limit their widespread use and make them unsuitable for commercial adoption. Therefore, the use of technologies combining on-board sensors and algorithms to recognize several key vehicle driving state parameters has become a major research approach.

[0021] Currently, most conventional methods for recognizing adhesion coefficients are limited to specific situations, fail to meet the requirement of updating data in real time, and have low accuracy, making an urgent solution necessary.

[0022] To further clarify the purpose, technical proposal, and advantages of this application, embodiments of this application will be described in more detail below with reference to the drawings.

[0023] The embodiments of this application will be described in detail below, and the examples of such embodiments are shown in the drawings, where the same or similar reference numerals from beginning to end represent the same or similar elements or elements having the same or similar function. The embodiments described below with reference to the drawings are illustrative for the purpose of illustrating this application and should not be construed as limitations thereto.

[0024] Hereinafter, with reference to the drawings, the method, apparatus, vehicle, and storage medium for recognizing road surface information of an embodiment of this application will be described. In response to the problems mentioned in the background art above, this application provides a method for recognizing road surface information of an automobile, which collects vehicle driving data, determines the current driving status of the vehicle based on the vehicle driving data, calculates a first parameter of the road surface based on the vehicle driving data if the current driving status of the vehicle is a first preset status, calculates a first adhesion coefficient based on the first parameter and a first preset policy if the current driving status of the vehicle is a second preset status, calculates a second parameter of the road surface based on the vehicle driving data, obtains a second adhesion coefficient based on the second parameter and a second preset policy, filters the first or second adhesion coefficient to obtain a final adhesion coefficient value, and uses the final adhesion coefficient value as an index to search a preset standard road condition adhesion coefficient-road condition information relationship table to obtain current road condition information of the road surface. This application acquires signals such as vehicle speed and uses fuzzy logic to calculate and recognize the road surface adhesion coefficient in various situations such as steering, braking, and straight-line driving. This provides clear, real-time feedback of current road surface recognition information to the driver, enabling the driver to better select the four-wheel drive driving mode and improving the customer's perceptual experience. This saves hardware costs while effectively improving the accuracy of adhesion coefficient recognition and the coverage of driving conditions. This solves the problems of conventional technology, which could only estimate the adhesion coefficient under specific conditions, could not meet the requirement of updating data in real time, and had low accuracy.

[0025] Specifically, Figure 1 is a flowchart of a method for recognizing road surface information in an automobile according to an embodiment of this application.

[0026] As shown in Figure 1, the method for recognizing road surface information for the vehicle includes the following steps.

[0027] In step S101, vehicle driving data is collected, and the current driving status of the vehicle is determined based on the vehicle driving data.

[0028] The embodiment of this application collects vehicle driving data from a CAN (Controller Area Network) bus via an ESC (Electronic Stability Controller), EMS (Engine Management System), TCU (Transmission Control Unit) system, and SAM (Signal Acquisition / Activation Module). The collected data can be output to the AWD (All-Wheel Drive) system, which is the main four-wheel drive logic control system. As shown in Figure 2, the current vehicle status, such as steering and braking, is determined based on the above data, thereby providing a basis for calculating the subsequent adhesion coefficient.

[0029] As one option, in one embodiment of this application, the vehicle driving data includes at least one of the following: vehicle speed, front wheel steering angle, yaw rate, lateral / longitudinal acceleration, engine output torque, gear position, and gradient signal.

[0030] Here, vehicle speed, lateral / longitudinal acceleration, and gear position can be obtained from the ESC, front wheel steering angle and yaw rate can be obtained from the SAM, engine output torque can be obtained from the EMS, and the gradient signal is calculated based on information such as vehicle speed, front wheel steering angle, yaw rate, lateral / longitudinal acceleration, engine output torque, and gear position. However, these driving data can also be obtained from other systems and can be set according to the actual situation, and the embodiments of this application are not specifically limited to this.

[0031] In the embodiments of this application, the vehicle driving data received from the CAN bus specifically includes vehicle speed, front wheel steering angle, yaw rate, lateral / longitudinal acceleration, engine output torque, gear position, and gradient signal, thereby providing a large amount of reliable data support for subsequent operations such as the calculation of adhesion coefficient and fuzzy recognition processing.

[0032] In step S102, if the vehicle's current driving condition is the first preset condition, a first parameter of the road surface is calculated based on the vehicle's driving data, and a first adhesion coefficient is calculated based on the first parameter and the first preset policy. If the vehicle's current driving condition is the second preset condition, a second parameter of the road surface is calculated based on the vehicle's driving data, and a second adhesion coefficient is obtained based on the second parameter and the second preset policy. The first or second adhesion coefficient is then filtered to obtain the final adhesion coefficient value.

[0033] In the embodiments of this application, the first preset state and the second preset state may be linear driving (i.e., longitudinal dynamics in Figure 3) and nonlinear driving, respectively. Of course, the first preset state and the second preset state may be other driving conditions, and the embodiments of this application are not specifically limited to these.

[0034] After determining the vehicle's current driving status by collecting vehicle driving data, the embodiment of this application further determines the vehicle's current driving status, such as whether it is nonlinear driving, based on the front wheel steering angle and yaw rate signal. By selecting and executing a corresponding algorithm based on the different situations and calculating the adhesion coefficient, accurate calculation of the adhesion coefficient can be achieved in various situations. The specific execution logic for calculating the adhesion coefficient is shown in Figure 3.

[0035] In the embodiments of this application, the method for determining the current driving state of the vehicle based on the front wheel steering angle and yaw rate may be as follows: if the front wheel steering angle is greater than a preset front wheel steering angle threshold and the yaw rate is greater than a preset yaw rate threshold, it is determined that the current driving state of the vehicle is nonlinear; and if the front wheel steering angle is less than or equal to a preset front wheel steering angle threshold, or the yaw rate is less than or equal to a preset yaw rate threshold, it is determined that the current driving state of the vehicle is linear.

[0036] Here, the specific values ​​of the preset front wheel steering angle threshold and the preset yaw rate threshold can be set according to the actual situation and needs, and are not limited to the embodiments of this application.

[0037] Of course, the method for determining the current driving conditions of a vehicle based on the front wheel steering angle and yaw rate may be any other reasonable method, or the current driving conditions of a vehicle may be determined by any other reasonable method in addition to being based on the front wheel steering angle and yaw rate, and the embodiments of this application are not specifically limited to this.

[0038] As one option, in one embodiment of this application, when the current driving conditions of the vehicle are a first preset condition, calculating a first parameter of the road surface based on the vehicle's driving data and calculating a first adhesion coefficient based on the first parameter and the first preset policy includes calculating the driving slip ratio and the usable adhesion coefficient of the road surface based on the vehicle's driving data, performing fuzzy processing on the driving slip ratio and the usable adhesion coefficient to obtain six first similarity coefficients based on a preset fuzzy inference table, and calculating a first adhesion coefficient based on the first similarity coefficients.

[0039] Furthermore, if the current state of the vehicle is linear driving such as straight driving or braking (i.e., the first preset state is linear driving), the embodiment of this application uses a longitudinal dynamics algorithm to calculate the driving slip ratio and the adhesive coefficient used from the received vehicle driving data, performs fuzzy processing on the first parameters (including the driving slip ratio and the adhesive coefficient used), performs inference using a fuzzy inference table, and finally performs defuzzy conversion to obtain a first similarity coefficient for six standard road surfaces, and calculates the first adhesive coefficient for that state accordingly.

[0040] Specifically, if the first preset condition is linear driving, a longitudinal dynamics algorithm is used to calculate the driving slip ratio and the wear adhesion coefficient from the acquired vehicle driving data. By performing a fuzzy logic process on the driving slip ratio and the wear adhesion coefficient, the driving slip ratio and the wear adhesion coefficient after fuzzing are obtained. Furthermore, based on the driving slip ratio after fuzzing, the wear adhesion coefficient after fuzzing, and the preset fuzzy inference table, six first similarity coefficients are obtained, and these six first similarity coefficients can characterize the similarity between the current road surface and the six standard road surfaces. After obtaining the six first similarity coefficients, the first adhesion coefficient can be calculated based on these six first similarity coefficients.

[0041] The following describes the process for calculating the adhesion coefficient using longitudinal dynamics in the embodiments of this application.

[0042] JPEG2026517544000170.jpg31160

[0043] JPEG2026517544000171.jpg36160

[0044] JPEG2026517544000172.jpg39160

[0045] 2. The slip ratio during operation and the coefficient of adhesion used are used as input variables for fuzzy control. TIFF2026517544000173.tif48 is an output variable, and as shown in Figure 4, the final road surface adhesion coefficient value is calculated using the corresponding formula.

[0046] 3. By transforming the output of the fuzzy inference, we obtain a clear control variable output, i.e., a specific numerical value, which is the similarity to six standard road surface curves. Here, a part of the fuzzy inference rule is shown in Figure 5.

[0047] As one option, the processing method for items 2 and 3 above may be as follows: firstly, the drive slip ratio corresponding to the current cycle TIFF2026517544000174.tif54 and the adhesion coefficient used By applying fuzzy logic to TIFF2026517544000175.tif44, the slip ratio during operation after fuzzing is obtained. TIFF2026517544000176.tif54 and the adhesion coefficient used Obtain TIFF2026517544000177.tif44, and then, based on the preset fuzzy inference table, determine the drive slip rate after fuzzing. TIFF2026517544000178.tif54 and the adhesion coefficient used By processing TIFF2026517544000179.tif44, six fuzzy similarity classifications are obtained, which can characterize the degree of similarity between the current road surface and six standard road surfaces. Subsequently, by defuzzying these six fuzzy similarity classifications, six first similarity coefficients are obtained. TIFF2026517544000180.tif44, TIFF2026517544000181.tif44, TIFF2026517544000182.tif44, TIFF2026517544000183.tif44, TIFF2026517544000184.tif44, After obtaining TIFF2026517544000185.tif44, the first adhesion coefficient can finally be calculated based on the six obtained first similarity coefficients and a pre-set formula for the first adhesion coefficient.

[0048] Here, the slip ratio during driving The method for performing fuzzy processing on TIFF2026517544000186.tif54 may be as follows: that is, based on a preset drive slip ratio threshold, the drive slip ratio corresponding to the current cycle Classify TIFF2026517544000187.tif54 and determine the drive slip rate corresponding to the current cycle. If TIFF2026517544000188.tif54 is greater than or equal to the drive slip ratio threshold, the drive slip ratio corresponding to the current cycle TIFF2026517544000189.tif54 can be classified as "Large", and the drive slip rate corresponding to the current cycle If TIFF2026517544000190.tif54 is below the drive slip threshold, the drive slip corresponding to the current cycle TIFF2026517544000191.tif54 can be classified as "small".

[0049] In the embodiments of this application, the slip ratio threshold during driving may be any reasonable value and can be set according to the demand and actual circumstances, and is not limited to this embodiment.

[0050] Adhesion coefficient The method for performing fuzzy processing on TIFF2026517544000192.tif44 may be as follows: based on a preset usage adhesion coefficient threshold and a preset usage adhesion coefficient range corresponding to each standard road surface, the usage adhesion coefficient corresponding to the current cycle By classifying TIFF2026517544000193.tif44, the initial standard road surface corresponding to the wear adhesion coefficient for the current cycle is determined, thereby determining the wear adhesion coefficient for the current cycle. TIFF2026517544000194.tif44 allows for the preliminary determination of standard road surfaces that are relatively similar to the current road surface, i.e., initial standard road surfaces are obtained. Here, the six standard road surfaces include ice, snow, wet cobblestone, wet asphalt, dry concrete, and dry asphalt, and of course, the types and number of standard road surfaces can be reasonably adjusted according to demand and actual conditions, and are not limited thereto in the embodiments of this application.

[0051] In the embodiments of this application, the threshold value of the adhesion coefficient used may be any reasonable value and can be set according to demand and actual circumstances, and is not limited thereto in the embodiments of this application.

[0052] Based on the preset fuzzy inference table, the slip rate during operation after fuzzing is performed. TIFF2026517544000195.tif54 and the adhesion coefficient used The method for obtaining six fuzzy similarity coefficients by processing TIFF2026517544000196.tif44 may be as follows: referring to the preset fuzzy inference table shown in Figure 5 (in the "Input" section shown in Figure 5) "TIFF2026517544000197.tif43" is the above " "TIFF2026517544000198.tif54" is "Input" "TIFF2026517544000199.tif43" is the above " The file is "TIFF2026517544000200.tif44"), and it shows the correspondences in the preset fuzzy inference table, as well as the slip rate during operation after fuzzing. TIFF2026517544000201.tif54 and the adhesion coefficient used Based on TIFF2026517544000202.tif44, six fuzzy similarity classifications can be determined, where the fuzzy similarity classifications may be DS, NS, CS, S, and VS, with DS representing dissimilarity, NS representing general similarity, CS representing relatively similarity, S representing similarity, and VS representing very similarity.

[0053] for example, TIFF2026517544000203.tif42 is large, If TIFF2026517544000204.tif43 is snow, the fuzzy similarity classifications between the current road surface and the six standard road surfaces—ice, snow, wet cobblestone, wet asphalt, dry concrete, and dry asphalt—are S, VS, NS, DS, DS, and DS, respectively.

[0054] Also, for example, TIFF2026517544000205.tif42 is small, If TIFF2026517544000206.tif43 is wet asphalt, the fuzzy similarity classifications between the current road surface and the six standard road surfaces—ice, snow, wet cobblestone, wet asphalt, dry concrete, and dry asphalt—are DS, DS, NS, S, CS, and DS, respectively.

[0055] The method for obtaining six first similarity coefficients by defuzzyizing these six fuzzy similarity classifications may be as follows: that is, based on the correspondence between pre-set fuzzy similarity classifications and similarity coefficients, and the six fuzzy similarity classifications obtained above, similarity coefficients corresponding to the six fuzzy similarity classifications are obtained, i.e., six first similarity coefficients are obtained.

[0056] As a result, the embodiment of this application reduces the cost of calculating the adhesion coefficient and improves the efficiency and accuracy of the calculation by calculating the road surface adhesion coefficient (i.e., the first adhesion coefficient in linear driving conditions) in linear driving conditions of a vehicle using a longitudinal dynamics algorithm.

[0057] As one option, in the embodiment of this application, the formula for calculating the first adhesion coefficient is as follows: The filename is TIFF2026517544000207.tif5151.

[0058] Here, TIFF2026517544000208.tif57 is the first adhesion coefficient, TIFF2026517544000209.tif48 is the six first similarity coefficients obtained above. TIFF2026517544000210.tif49 is the braking slip ratio corresponding to the latest six consecutive cycles.

[0059] In the embodiment of this application, after obtaining parameters such as the braking slip ratio and the usable adhesion coefficient, the final road surface adhesion coefficient value can be calculated using the following formula, with the driving slip ratio and the usable adhesion coefficient as input variables for fuzzy control. TIFF2026517544000211.tif5151

[0060] Here, TIFF2026517544000212.tif57 is the first adhesion coefficient, TIFF2026517544000213.tif48 is the first similarity coefficient, TIFF2026517544000214.tif49 is the braking slip ratio.

[0061] As a result, the embodiments of this application can provide guidance and justification for the subsequent use and presentation of adhesion coefficients by calculating the road surface adhesion coefficient from parameters such as the similarity coefficient and the braking slip ratio.

[0062] As one option, in one embodiment of this application, when the current driving conditions of the vehicle are those of a second preset condition, calculating a second parameter of the road surface based on the vehicle's driving data and obtaining a second adhesion coefficient based on the second parameter and a second preset policy includes calculating the absolute value of the vehicle's lateral acceleration and the absolute value of the yaw rate difference based on the vehicle's driving data, performing fuzzy processing on the absolute value of the lateral acceleration and the absolute value of the yaw rate difference to obtain a second similarity coefficient based on a preset fuzzy inference table, and obtaining a second adhesion coefficient based on the second similarity coefficient and a preset correction formula.

[0063] If the current state of the vehicle is a nonlinear driving condition, the embodiment of this application calculates the road surface adhesion coefficient in that condition using a nonlinear dynamics algorithm. That is, if the second preset condition is a nonlinear driving condition, the second adhesion coefficient can be calculated using a nonlinear dynamics algorithm.

[0064] Specifically, in the embodiment of this application, the process for calculating the road surface adhesion coefficient under nonlinear driving conditions is as follows:

[0065] 1. The absolute value of the yaw rate difference can be calculated using the following formula: TIFF2026517544000215.tif1975 TIFF2026517544000216.tif1249 TIFF2026517544000217.tif637

[0066] Here, TIFF2026517544000218.tif47 is a steady-state yaw rate, TIFF2026517544000219.tif44 is the corrected yaw rate. TIFF2026517544000220.tif58 is the absolute value of the yaw rate difference, TIFF2026517544000221.tif44 is vehicle speed, TIFF2026517544000222.tif42 is the wheelbase, TIFF2026517544000223.tif34 is the gross vehicle mass, TIFF2026517544000224.tif56 is the front and rear tread (that is TIFF2026517544000225.tif53 is the front tread, TIFF2026517544000226.tif54 is the rear tread. TIFF2026517544000227.tif34 is the actual yaw rate, TIFF2026517544000228.tif511 is the cornering stiffness of the front and rear axles (i.e.) TIFF2026517544000229.tif55 is the front axle cornering stiffness, TIFF2026517544000230.tif55 represents the rear axle cornering stiffness.

[0067] 2, lateral acceleration Absolute value of TIFF2026517544000231.tif44 and absolute value of yaw rate difference TIFF2026517544000232.tif58 is used as the input variable for the fuzzy rule. TIFF2026517544000233.tif42 is used as the output variable (i.e., the second similarity coefficient), and the second adhesion coefficient is calculated using a preset correction formula, as shown in Figure 6.

[0068] One option is lateral acceleration Absolute value of TIFF2026517544000234.tif44 and absolute value of yaw rate difference After obtaining TIFF2026517544000235.tif58, the method for calculating the second similarity coefficient may be as follows, that is, referring to Figure 6, first, the lateral acceleration Absolute value of TIFF2026517544000236.tif44 and absolute value of yaw rate difference By applying fuzzy logic to each of the TIFF2026517544000237.tif58 files, the lateral acceleration after fuzzing is obtained. The absolute value of TIFF2026517544000238.tif44 and the absolute value of the yaw rate difference after fuzzing. After obtaining TIFF2026517544000239.tif58, the lateral acceleration after fuzzing is calculated based on a pre-configured fuzzy inference table. The absolute value of TIFF2026517544000240.tif44 and the absolute value of the yaw rate difference after fuzzing. By processing TIFF2026517544000241.tif58, a fuzzy similarity classification is obtained, and then, by defuzzying the fuzzy similarity classification, a second similarity coefficient is obtained. After obtaining TIFF2026517544000242.tif42, the second adhesion coefficient can finally be obtained based on the obtained second similarity coefficient and preset correction formula.

[0069] As a result, the embodiment of this application calculates the absolute value of lateral acceleration and the absolute value of yaw rate difference from the received parameters, performs fuzzy logic on the above parameters, obtains a second similarity coefficient by inference using a fuzzy inference table, and further calculates a second adhesion coefficient using a correction formula, thereby achieving accurate calculation of the road surface adhesion coefficient in nonlinear driving conditions.

[0070] As one option, in the embodiment of this application, the preset correction formula is as follows, namely, It is TIFF2026517544000243.tif3361,

[0071] Here, TIFF2026517544000244.tif59 is a correction function, TIFF2026517544000245.tif42 is a similarity coefficient, TIFF2026517544000246.tif425 consists of correction function coefficients, TIFF2026517544000247.tif43 is the required road surface adhesion coefficient (i.e., the second adhesion coefficient mentioned above), TIFF2026517544000248.tif42 has a proportionality constant of 1.19. TIFF2026517544000249.tif44 is the lateral acceleration, TIFF2026517544000250.tif42 represents the acceleration due to gravity.

[0072] In the embodiment of this application, the formula for calculating the road surface adhesion coefficient under nonlinear driving conditions is as follows: The filename is TIFF2026517544000251.tif3361.

[0073] Here, TIFF2026517544000252.tif59 is a correction function, TIFF2026517544000253.tif42 is a similarity coefficient, TIFF2026517544000254.tif425 contains the correction function coefficients, which may be -2.2, 3.2, -2.1, and 2 respectively. TIFF2026517544000255.tif43 is the required road surface adhesion coefficient. TIFF2026517544000256.tif42 has a proportionality constant of 1.19. TIFF2026517544000257.tif44 is the lateral acceleration, TIFF2026517544000258.tif42 represents the acceleration due to gravity.

[0074] Here, The values ​​of TIFF2026517544000259.tif425 and The values ​​of TIFF2026517544000260.tif42 can be reasonably set according to the actual circumstances, and are not limited thereto in the embodiments of this application.

[0075] As a result, the embodiment of this application calculates an estimated value of the adhesion coefficient in the current corresponding situation of the vehicle by recognizing similar and nonlinear factors in different driving conditions using fuzzy rules of the calculation module of the AWD system. Therefore, the embodiment of this application does not require the addition of hardware devices such as sensors, effectively reduces the recognition cost of the adhesion coefficient, and improves the calculation and recognition accuracy of the adhesion coefficient.

[0076] As one option, after obtaining the road surface adhesion coefficient for a vehicle in a corresponding situation, the embodiment of this application can also filter the road surface adhesion coefficient for different situations using a corresponding filtering algorithm, as shown in Figure 7, in order to obtain the final adhesion coefficient value for different situations. Therefore, by changing the magnitude of the corresponding coefficient and adjusting the sensitivity of the filtering algorithm, the accuracy and interference resistance of the road surface adhesion coefficient estimation can be effectively improved.

[0077] As one option, the method for filtering the obtained first or second adhesion coefficient to obtain the final adhesion coefficient value may be as follows:

[0078] As shown in Figure 8, first Enter TIFF2026517544000261.tif54, and here, TIFF2026517544000262.tif54 is the road surface adhesion coefficient corresponding to the current cycle, and may be either the first adhesion coefficient corresponding to the current cycle calculated above or the second adhesion coefficient corresponding to the current cycle.

[0079] JPEG2026517544000263.jpg18160

[0080] JPEG2026517544000264.jpg26160

[0081] JPEG2026517544000265.jpg61160

[0082] JPEG2026517544000266.jpg54160

[0083] JPEG2026517544000267.jpg60160

[0084] The above filtering process avoids the problem of large errors occurring in the road surface adhesion coefficient due to inaccuracies in the acquired driving data or calculation errors. As a result, the accuracy of the final adhesion coefficient value obtained can be improved, and the accuracy of the vehicle's recognition of road surface information can be improved.

[0085] Of course, the method for filtering the first or second adhesion coefficient in the embodiments of this application may be any other reasonable method, and the embodiments of this application are not specifically limited thereto.

[0086] In step S103, the final adhesion coefficient value is used as an index to search a pre-configured standard road surface adhesion coefficient-road surface information relationship table to obtain the current road surface condition information.

[0087] After obtaining an estimated final road surface adhesion coefficient under different road conditions, the embodiment of this application further obtains current road condition information by comparing the final adhesion coefficient value with a standard road condition adhesion coefficient-road condition information relationship table, transmits the obtained currently recognized road condition information to the IHU (Infotainment Head Unit), and presents to the driver via the central control display that it is now necessary to switch to AWD mode or to switch automatically, thereby improving vehicle reliability and usability.

[0088] As one option, in one embodiment of this application, the final adhesion coefficient value is used as an index to search a pre-set standard road condition adhesion coefficient-road condition information relationship table to obtain current road condition information of the road surface, and then a presentation signal and / or feedback signal is generated based on the road condition information, and the presentation signal and / or feedback signal is received and the current road condition information of the road surface is fed back to the user in acoustic and / or optical form.

[0089] After obtaining current road surface information, the embodiment of this application can generate a notification signal and a feedback signal based on the obtained road surface information. When the vehicle receives such a signal, the four-wheel drive system uses the signal to distribute torque between the front and rear wheels and determines whether the current driving mode corresponds to the recognized road conditions. If it does not correspond, it sends notification information to the IHU center console / meter in the form of a pop-up, recommending that the driver switch driving modes or activate automatic switching mode. The driver can then choose to activate automatic switching mode, switch manually, or ignore the reminder by clicking a button on the central control display. Furthermore, the embodiment of this application can transmit relevant voice reminder feedback information via an audio device.

[0090] As a result, the embodiment of this application can satisfy the need to recognize the road surface adhesion coefficient based on the torque distribution of four-wheel drive by recognizing the road surface adhesion coefficient, and can also be input as the road surface adhesion coefficient required by other systems. Furthermore, the embodiment of this application can provide clear real-time feedback of the current road surface recognition information to the driver, enabling the driver to better select the four-wheel drive driving mode and improving the user's driving experience.

[0091] According to the vehicle road surface information recognition method of the embodiment of this application, vehicle driving data is collected, the current driving status of the vehicle is determined based on the vehicle driving data, if the current driving status of the vehicle is a first preset status, a first parameter of the road surface is calculated based on the vehicle driving data, and a first adhesion coefficient is calculated based on the first parameter and the first preset policy, if the current driving status of the vehicle is a second preset status, a second parameter of the road surface is calculated based on the vehicle driving data, a second adhesion coefficient is obtained based on the second parameter and the second preset policy, the first adhesion coefficient or the second adhesion coefficient is filtered to obtain a final adhesion coefficient value, and the current road surface information is obtained by searching a preset standard road condition adhesion coefficient-road condition information relationship table using the final adhesion coefficient value as an index.

[0092] The method for recognizing road surface information by an automobile according to the embodiment of this application eliminates the need to install additional sensors. Instead, the automobile's inherent system can acquire driving data and calculate the final adhesion coefficient value, resulting in reduced hardware costs.

[0093] Furthermore, if the vehicle's current driving conditions are different (i.e., the vehicle's current driving conditions may be the first preset condition or the second preset condition), the method for obtaining the corresponding first or second adhesion coefficient in a different way, and calculating the first and second adhesion coefficients, is intended to improve the accuracy of the obtained first or second adhesion coefficients and also improve the coverage of driving conditions.

[0094] As described above, this method saves on hardware costs while effectively improving the accuracy of adhesion coefficient recognition and the coverage of driving conditions.

[0095] Next, with reference to the drawings, an automobile road surface information recognition device according to an embodiment of this application will be described.

[0096] Figure 9 is a schematic block diagram of a road surface information recognition device for an automobile according to an embodiment of this application.

[0097] As shown in Figure 9, the road surface information recognition device 10 of the vehicle includes a collection module 100, a calculation module 200, and a search module 300.

[0098] Here, the collection module 100 is used to collect vehicle driving data and to determine the vehicle's current driving status based on the vehicle driving data.

[0099] The calculation module 200 is used to calculate a first parameter of the road surface based on the vehicle's driving data when the vehicle's current driving condition is a first preset condition, and to calculate a first adhesion coefficient based on the first parameter and the first preset policy. If the vehicle's current driving condition is a second preset condition, it calculates a second parameter of the road surface based on the vehicle's driving data, obtains a second adhesion coefficient based on the second parameter and the second preset policy, and filters the first or second adhesion coefficient to obtain a final adhesion coefficient value.

[0100] The search module 300 is used to obtain current road surface condition information by searching a pre-configured standard road condition adhesion coefficient-road condition information relationship table using the final adhesion coefficient value as an index.

[0101] As one option, in one embodiment of this application, the vehicle driving data includes at least one of the following: vehicle speed, front wheel steering angle, yaw rate, lateral / longitudinal acceleration, engine output torque, gear position, and gradient signal.

[0102] As one option, in one embodiment of this application, the calculation module 200 includes a first calculation unit, a first fuzzing unit, and a second calculation unit.

[0103] Here, the first calculation unit is used to calculate the road surface slip ratio and the coefficient of adhesion used based on the vehicle's driving data.

[0104] The first fuzzing unit is used to perform fuzzy processing on the drive slip ratio and the adhesion coefficient used, and to obtain six first similarity coefficients based on a preset fuzzy inference table.

[0105] The second calculation unit is used to calculate the first adhesion coefficient based on the first similarity coefficient.

[0106] As one option, in one embodiment of this application, the calculation module 200 further includes a third calculation unit, a second fuzzing unit, and a correction unit.

[0107] Here, the third calculation unit is used to calculate the absolute value of the vehicle's lateral acceleration and the absolute value of the yaw rate difference based on the vehicle's driving data.

[0108] The second fuzzing unit is used to perform fuzzy processing on the absolute values ​​of the lateral acceleration and the yaw rate difference, and to obtain a second similarity coefficient based on a preset fuzzy inference table.

[0109] The correction unit is used to obtain a second adhesion coefficient based on a second similarity coefficient and a preset correction formula.

[0110] As one option, in one embodiment of this application, the formula for calculating the first adhesion coefficient is as follows: The filename is TIFF2026517544000268.tif5151.

[0111] Here, TIFF2026517544000269.tif57 is the first adhesion coefficient, TIFF2026517544000270.tif47 is the first similarity coefficient, TIFF2026517544000271.tif48 is the braking slip ratio.

[0112] As one option, in one embodiment of this application, the preset correction formula is as follows, namely, The filename is TIFF2026517544000272.tif3361.

[0113] Here, TIFF2026517544000273.tif59 is a correction function, TIFF2026517544000274.tif42 is a similarity coefficient, TIFF2026517544000275.tif49 is a correction function coefficient, TIFF2026517544000276.tif43 is the required road surface adhesion coefficient. TIFF2026517544000277.tif32 has a proportionality constant of 1.19. TIFF2026517544000278.tif44 is the lateral acceleration, TIFF2026517544000279.tif32 represents the acceleration due to gravity.

[0114] As one option, in one embodiment of this application, the road surface information recognition device 10 for the automobile in the embodiment of this application further includes a generation module and a feedback module.

[0115] Here, the generation module uses the final adhesion coefficient value as an index to search a pre-configured standard road condition adhesion coefficient-road condition information relationship table to obtain current road condition information for the road surface, and then generates presentation signals and / or feedback signals based on the road condition information.

[0116] The feedback module receives presentation signals and / or feedback signals and is used to provide the user with information about the current road conditions on the road surface in acoustic and / or optical form.

[0117] Furthermore, the description of the above-mentioned embodiment of the method for recognizing road surface information for automobiles also applies to the road surface information recognition device for automobiles in that embodiment, and will not be explained further here.

[0118] According to the road surface information recognition device for automobiles according to the embodiment of this application, vehicle driving data is collected, the current driving status of the vehicle is determined based on the vehicle driving data, if the current driving status of the vehicle is a first preset status, a first parameter of the road surface is calculated based on the vehicle driving data, and a first adhesion coefficient is calculated based on the first parameter and the first preset policy, if the current driving status of the vehicle is a second preset status, a second parameter of the road surface is calculated based on the vehicle driving data, a second adhesion coefficient is obtained based on the second parameter and the second preset policy, the first adhesion coefficient or the second adhesion coefficient is filtered to obtain a final adhesion coefficient value, and the current road surface information is obtained by searching a preset standard road condition adhesion coefficient-road condition information relationship table using the final adhesion coefficient value as an index.

[0119] The method for recognizing road surface information by an automobile according to the embodiment of this application eliminates the need to install additional sensors. Instead, the automobile's inherent system can acquire driving data and calculate the final adhesion coefficient value, resulting in reduced hardware costs.

[0120] Furthermore, if the vehicle's current driving conditions are different (i.e., the vehicle's current driving conditions may be the first preset condition or the second preset condition), the method for obtaining the corresponding first or second adhesion coefficient in a different way, and calculating the first and second adhesion coefficients, is intended to improve the accuracy of the obtained first or second adhesion coefficients and also improve the coverage of driving conditions.

[0121] As described above, this method saves on hardware costs while effectively improving the accuracy of adhesion coefficient recognition and the coverage of driving conditions.

[0122] Figure 10 is a schematic diagram of the vehicle configuration according to the embodiment of this application.

[0123] The vehicle may include a memory 901, a processor 902, and a computer program stored in the memory 901 and executable on the processor 902.

[0124] When the processor 902 executes the program, the method for recognizing road surface information of the automobile according to the above embodiment is realized.

[0125] Furthermore, the vehicle further includes a communication interface 903 used for communication between the memory 901 and the processor 902.

[0126] Memory 901 is used to store computer programs that can be executed on processor 902.

[0127] Memory 901 may include high-speed RAM memory and may further include non-volatile memory, such as at least one disk memory.

[0128] When the memory 901, processor 902, and communication interface 903 are implemented independently, the communication interface 903, memory 901, and processor 902 can be interconnected via a bus to enable communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be classified into address buses, data buses, control buses, etc. For simplicity of representation, Figure 9 shows a single thick line, but this does not mean that there is only one bus or only one type of bus.

[0129] As one option, in a specific implementation, if the memory 901, processor 902, and communication interface 903 are integrated onto a single chip, the memory 901, processor 902, and communication interface 903 can communicate with each other via an internal interface.

[0130] The processor 902 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to carry out the embodiments of this application.

[0131] In the embodiments of this application, a computer-readable storage medium is further provided, and when a computer program is stored in the computer-readable storage medium and the program is executed by a processor, the above-described method for recognizing road surface information of an automobile is realized.

[0132] In this specification, reference terms such as “one embodiment,” “several embodiments,” “example,” “specific example,” or “several examples” mean that the specific features, structures, materials, or characteristics described with reference to that embodiment or example are included in at least one embodiment or example of this application. In this specification, a general expression for the above terms does not necessarily correspond to the same embodiment or example. The specific features, structures, materials, or characteristics described may be combined in an appropriate manner in any or N embodiments or examples. Furthermore, a person skilled in the art can combine different embodiments or examples and features of different embodiments or examples described herein, provided that they do not conflict with each other.

[0133] Furthermore, the terms “first” and “second” are used solely to describe the purpose and should not be understood as indicating or implying relative importance or implicitly pointing to the number of specified technical features. Accordingly, features limited by “first” and “second” may explicitly or implicitly include at least one such feature. In the description of this application, “N” means at least two, for example, two, three, etc., unless otherwise clearly and specifically limited.

[0134] Descriptions of processes or methods described in flowcharts or otherwise in this specification are understood to represent modules, segments, or portions of code containing one or N executable instructions for implementing a given logic function or step in a process, and the scope of preferred embodiments of this application includes other embodiments, and the functions do not have to be performed in the order shown or discussed, for example, depending on the function, the functions may be performed basically in parallel or in reverse order, as should be understood by those skilled in the art.

[0135] The logic and / or steps shown in the flowchart or described otherwise herein can be thought of, for example, as a sequence of executable instructions for realizing a logic function and can be specifically realized on a computer-readable storage medium for use in or in combination with instruction execution systems, apparatuses, or devices (e.g., a system that operates using a computer, a system that includes a processor, or other system that can obtain and execute instructions from an instruction execution system, apparatus, or device). In this specification, “computer-readable storage medium” may be any device capable of containing, storing, communicating with, propagating, or transmitting a program for use in or in combination with such instruction execution systems, apparatuses, or devices. More specific examples (a non-exclusive list) of computer-readable storage media include electrical connectors with one or N wires (electronic devices), portable computer disk cartridges (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber devices, and read-only optical discs (CDROM). Computer-readable storage media may also be paper or other suitable media on which the program can be printed, since the program can be electronically obtained and stored in computer memory by optically scanning the paper or other medium, then editing and translating it, or processing it in any other suitable way as necessary.

[0136] Each part of this application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, N steps or methods can be implemented by software or firmware stored in memory and executed by a suitable instruction execution system. When implemented by hardware, as in another embodiment, it can be implemented by any or a combination of technologies known in the art, such as discrete logic circuits having logic gate circuits for realizing logic functions for data signals, dedicated integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0137] Those skilled in the art will understand that all or part of the steps of the above embodiment can be implemented by instructing the relevant hardware by program, the program may be stored on a computer-readable storage medium, and the program may, at runtime, include one or a combination thereof of the steps of the embodiment of the method.

[0138] In each embodiment of this application, each functional unit may be integrated into a single processing module, each unit may exist individually physically, or two or more units may be integrated into a single module. The integrated module may be implemented in hardware form or in software form. If the integrated module is implemented in software form and sold or used as a separate product, it may also be stored on a computer-readable storage medium.

[0139] The above-mentioned storage medium may be a read-only memory, a magnetic disk, or an optical disk. Although embodiments of this application have been described above, these embodiments are illustrative and not limiting to this application, and those skilled in the art can modify, alter, replace, or adapt these embodiments within the scope of this application. [Explanation of symbols]

[0140] 10...Vehicle road surface information recognition device, 100...Collection module, 200...Calculation module, 300...Search module, 901...Memory, 902...Processor, 903...Communication interface

Claims

1. A method for recognizing road surface information in an automobile, Collect vehicle driving data and determine the current driving status of the vehicle based on said vehicle driving data, If the current driving conditions of the vehicle are those of a first preset condition, a first parameter of the road surface is calculated based on the vehicle's driving data, and a first adhesion coefficient is calculated based on the first parameter and the first preset policy. If the current driving conditions of the vehicle are those of a second preset condition, a second parameter of the road surface is calculated based on the vehicle's driving data, and a second adhesion coefficient is obtained based on the second parameter and the second preset policy. The first adhesion coefficient or the second adhesion coefficient is then filtered to obtain a final adhesion coefficient value. Using the aforementioned final adhesion coefficient value as an index, the current road condition information of the road surface is obtained by searching a pre-set standard road condition adhesion coefficient-road condition information relationship table. including, A method characterized by the following:

2. The method according to claim 1, characterized in that the vehicle driving data includes at least one of the following: vehicle speed, front wheel steering angle, yaw rate, lateral / longitudinal acceleration, engine output torque, shift position, and gradient signal.

3. If the current driving conditions of the vehicle are those of a first preset condition, then a first parameter of the road surface is calculated based on the vehicle's driving data, and a first adhesion coefficient is calculated based on the first parameter and the first preset policy. Based on the driving data of the aforementioned vehicle, the slip ratio and adhesion coefficient of the road surface during driving are calculated, The aforementioned slip ratio during operation and the adhesion coefficient used are subjected to fuzzy logic processing, and six first similarity coefficients are obtained based on a preset fuzzy inference table. The first adhesion coefficient is calculated based on the first similarity coefficient, including, The method according to feature 1.

4. If the current driving conditions of the vehicle are those of a second preset condition, then a second parameter of the road surface is calculated based on the vehicle's driving data, and a second adhesion coefficient is obtained based on the second parameter and the second preset policy. Based on the vehicle's driving data, the absolute value of the vehicle's lateral acceleration and the absolute value of the yaw rate difference are calculated. The absolute values ​​of the lateral acceleration and the yaw rate difference are subjected to fuzzy processing to obtain a second similarity coefficient based on the preset fuzzy inference table. The second adhesion coefficient is obtained based on the second similarity coefficient and the preset correction formula, including, The method according to feature 1.

5. The formula for calculating the first adhesion coefficient is as follows: And, Here, This is the first adhesion coefficient, This is the first similarity coefficient, This is the slip ratio during braking. The method according to feature 3.

6. The preset correction formula is as follows: And, Here, This is a correction function, This is the second similarity coefficient, This is the correction function coefficient, This is the second adhesion coefficient, The proportionality constant is 1.

19. This is the lateral acceleration, This is the acceleration due to gravity. The method according to feature 4.

7. Using the aforementioned final adhesion coefficient value as an index, the relationship table between the pre-set standard road surface adhesion coefficient and road surface information is searched to obtain the current road surface information. To generate a presentation signal and / or a feedback signal based on the aforementioned road condition information, The system receives the aforementioned presentation signal and / or feedback signal, and provides the user with current road surface condition information in acoustic and / or optical format. Further including, The method according to feature 1.

8. A collection module used to collect vehicle driving data and determine the current driving status of the vehicle based on said vehicle driving data, A calculation module used to obtain a final adhesion coefficient value by filtering the first adhesion coefficient or the second adhesion coefficient, if the current driving conditions of the vehicle are those of a first preset condition, by calculating a first parameter of the road surface based on the vehicle's driving data, and by calculating a first adhesion coefficient based on the first parameter and the first preset policy, if the current driving conditions of the vehicle are those of a second preset condition, by calculating a second parameter of the road surface based on the vehicle's driving data, and by obtaining a second adhesion coefficient based on the second parameter and the second preset policy, A search module is used to obtain the current road surface information by searching a pre-set standard road surface adhesion coefficient-road surface information relationship table using the aforementioned final adhesion coefficient value as an index. including, A vehicle road surface information recognition device characterized by the following features.

9. A vehicle comprising memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to realize the method for recognizing road surface information of an automobile as described in any one of claims 1 to 7.

10. A computer-readable storage medium in which a computer program is stored, characterized in that the method for recognizing road surface information of an automobile as described in any one of claims 1 to 7 is realized when the program is executed by a processor.

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