Automobile road surface information identification method and device, vehicle and storage medium

Abstract

This application relates to a method, device, vehicle, and storage medium for identifying vehicle road surface information. The method includes: collecting vehicle driving data and determining the vehicle's current driving condition based on the driving data; if the vehicle's current driving condition is a first preset condition, calculating a first parameter of the road surface based on the vehicle driving data, and calculating a first adhesion coefficient based on the first parameter and a first preset strategy; if the vehicle's current driving condition is a second preset condition, 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 strategy, and filtering the first or second adhesion coefficient to obtain a final adhesion coefficient value; using the final adhesion coefficient value as an index, searching a preset standard road condition adhesion coefficient-road condition information relationship table to obtain the current road condition information, thereby saving hardware costs while effectively improving the adhesion coefficient recognition accuracy and driving condition coverage.

Description

Technical Field

[0001] This application relates to the field of four-wheel drive system development technology, and in particular to a method, device, vehicle, and storage medium for identifying vehicle road surface information. Background Technology

[0002] As a crucial parameter of the four-wheel drive system, the road adhesion coefficient significantly impacts torque distribution, yaw stability control of ABS (Anti-lock Braking System), and other systems that calculate vehicle dynamics. Therefore, research on road adhesion state identification is of great importance. With scholars both domestically and internationally recognizing the importance of vehicle driving state parameters for active vehicle safety control systems, companies and universities worldwide are investing considerable effort in estimating these parameters.

[0003] In the traditional way, the driving status parameters of a car are mainly obtained by measuring sensors. Although this method of measuring with instruments is highly accurate and practical, the high measurement cost limits its widespread use and makes it unsuitable for commercial promotion. Therefore, the technology of using on-board sensors combined with algorithms to identify some key vehicle driving status parameters has become the main research method.

[0004] Currently, most existing methods for identifying adhesion coefficients are limited to specific working conditions, cannot meet the need for real-time data updates, and have low accuracy, which urgently need to be addressed. Summary of the Invention

[0005] This application provides a method, device, vehicle, and storage medium for identifying vehicle road surface information, in order to solve the problems of existing technologies that can only estimate the adhesion coefficient under specific working conditions, cannot meet the needs of real-time data updates, and have low accuracy.

[0006] The first aspect of this application provides a method for identifying vehicle road surface information, comprising the following steps: collecting vehicle driving data and determining the current driving condition of the vehicle based on the vehicle driving data; if the current driving condition of the vehicle is a first preset condition, 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 strategy; if the current driving condition of the vehicle is a second preset condition, 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 strategy, and filtering the first adhesion coefficient or the second adhesion coefficient to obtain a final adhesion coefficient value; using the final adhesion coefficient value as an index, searching a preset standard road condition adhesion coefficient-road condition information relationship table to obtain the current road condition information of the road surface.

[0007] Optionally, in one embodiment of this application, the vehicle's 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 slope signal.

[0008] Optionally, in one embodiment of this application, if the current driving condition of the vehicle is a first preset condition, then calculating the first parameters of the road surface based on the vehicle's driving data, and calculating the first adhesion coefficient based on the first parameters and the first preset strategy, includes: calculating the slip ratio and the adhesion coefficient of the road surface based on the vehicle's driving data; performing fuzzification processing on the slip ratio and the adhesion coefficient, and obtaining six first similarity coefficients based on a preset fuzzy inference table; and calculating the first adhesion coefficient based on the first similarity coefficients.

[0009] Optionally, in one embodiment of this application, if the current driving condition of the vehicle is a second preset condition, then calculating the second parameter of the road surface based on the vehicle's driving data, and obtaining the second adhesion coefficient based on the second parameter and the second preset strategy, includes: calculating the absolute value of the vehicle's lateral acceleration and the absolute value of the difference in yaw rate based on the vehicle's driving data; performing fuzzification processing on the absolute value of the lateral acceleration and the absolute value of the difference in yaw rate, and obtaining the second similarity coefficient based on the preset fuzzy inference table; and obtaining the second adhesion coefficient based on the second similarity coefficient and the preset correction formula.

[0010] Optionally, in one embodiment of this application, the formula for calculating the first adhesion coefficient is as follows:

[0011] λ opt =)k1λ1+k2λ2+k3λ3+k4λ4+k5λ5+k6λ6) / (k1+k2+k3+k4+k5+k6)

[0012] Where, λ opt Let k be the first adhesion coefficient. 1～6 λ is the first similarity coefficient. 1～6 It represents the slip ratio.

[0013] Optionally, in one embodiment of this application, the preset correction formula is as follows:

[0014]

[0015]

[0016] Where f(k) is the correction function, k is the similarity coefficient, abcd are the correction function coefficients, μ is the road surface adhesion coefficient, γ is the proportionality coefficient (taken as 1.19), and a yρ is the lateral acceleration, and g is the gravitational acceleration.

[0017] Optionally, in one embodiment of this application, after finding the current road condition information of the road surface by looking up a preset standard road condition adhesion coefficient-road condition information relationship table with the final adhesion coefficient value as an index, the method further includes: generating a prompt signal and / or feedback signal based on the road condition information; receiving the prompt signal and / or feedback signal, and feeding back the current road condition information of the road surface to the user through acoustic and / or optical means.

[0018] A second aspect of this application provides a vehicle road surface information recognition device, comprising: a data acquisition module for acquiring vehicle driving data and determining the current driving condition of the vehicle based on the vehicle driving data; a calculation module for calculating a first parameter of the road surface based on the vehicle driving data if the current driving condition of the vehicle is a first preset condition, calculating a first adhesion coefficient based on the first parameter and a first preset strategy, and calculating a second parameter of the road surface based on the vehicle driving data if the current driving condition of the vehicle is a second preset condition, obtaining a second adhesion coefficient based on the second parameter and a second preset strategy, and filtering the first adhesion coefficient or the second adhesion coefficient to obtain a final adhesion coefficient value; and a lookup module for searching a preset standard road condition adhesion coefficient-road condition information relationship table using the final adhesion coefficient value as an index to obtain the current road condition information of the road surface.

[0019] Optionally, in one embodiment of this application, the vehicle's 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 slope signal.

[0020] Optionally, in one embodiment of this application, the calculation module includes: a first calculation unit, used to calculate the slip ratio and the adhesion coefficient of the road surface based on the vehicle driving data; a first fuzzy processing unit, used to fuzzify the slip ratio and the adhesion coefficient, and obtain six first similarity coefficients based on a preset fuzzy inference table; and a second calculation unit, used to calculate the first adhesion coefficient based on the first similarity coefficients.

[0021] Optionally, in one embodiment of this application, the calculation module further includes: a third calculation unit, configured to calculate the absolute value of the lateral acceleration and the absolute value of the yaw rate difference of the vehicle based on the vehicle driving data; a second fuzzing processing unit, configured to fuzzify the absolute value of the lateral acceleration and the absolute value of the yaw rate difference, and obtain a second similarity coefficient according to the preset fuzzy inference table; and a correction unit, configured to obtain the second adhesion coefficient according to the second similarity coefficient and a preset correction formula.

[0022] Optionally, in one embodiment of this application, the formula for calculating the first adhesion coefficient is as follows:

[0023] λ opt =(k1λ1+k2λ2+k3λ3+k4λ4+k5λ5+k6λ6) / (k1+k2+k3+k4+k5+k6)

[0024] Where, λ opt Let k be the first adhesion coefficient. 1～6 λ is the first similarity coefficient. 1～6 It represents the slip ratio.

[0025] Optionally, in one embodiment of this application, the preset correction formula is as follows:

[0026]

[0027]

[0028] Where f(k) is the correction function, k is the similarity coefficient, abcd are the correction function coefficients, μ is the road surface adhesion coefficient, γ is the proportionality coefficient (taken as 1.19), and a y ρ is the lateral acceleration, and g is the gravitational acceleration.

[0029] Optionally, in one embodiment of this application, it further includes: a generation module, configured to generate a prompt signal and / or feedback signal based on the road surface after looking up a preset standard road condition adhesion coefficient-road condition information relationship table with the final adhesion coefficient value as an index; and a feedback module, configured to receive the prompt signal and / or feedback signal, and provide feedback on the current road condition information of the road surface to the user through acoustic and / or optical means.

[0030] A third aspect of this application provides a 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 implement the vehicle road information recognition method as described in the above embodiments.

[0031] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for recognizing vehicle road information.

[0032] Therefore, the embodiments of this application have the following beneficial effects:

[0033] The embodiments of this application can collect vehicle driving data and determine the current driving condition of the vehicle based on the driving data. If the current driving condition is a first preset condition, 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 a first preset strategy. If the current driving condition is a second preset condition, a second parameter of the road surface is calculated based on the vehicle driving data, and a second adhesion coefficient is obtained based on the second parameter and a second preset strategy. The first or second adhesion coefficient is then filtered to obtain a final adhesion coefficient value. Using the final adhesion coefficient value as an index, a preset standard road condition adhesion coefficient-road condition information relationship table is looked up to obtain the current road condition information. This saves hardware costs while effectively improving the adhesion coefficient recognition accuracy and driving condition coverage. Therefore, it solves the problems of existing technologies that can only estimate the adhesion coefficient under specific conditions, cannot meet the need for real-time data updates, and have low accuracy.

[0034] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0035] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0036] Figure 1 This is a flowchart illustrating a method for identifying vehicle road surface information according to an embodiment of this application;

[0037] Figure 2 A hardware interaction diagram is provided for one embodiment of this application;

[0038] Figure 3 A schematic diagram illustrating the execution logic of a method for recognizing vehicle road surface information, provided as an embodiment of this application;

[0039] Figure 4 A schematic diagram of a fuzzy identification process for road surface adhesion coefficient under linear driving conditions is provided as an embodiment of this application;

[0040] Figure 5 A schematic diagram of a partially fuzzy inference rule is provided for one embodiment of this application;

[0041] Figure 6 A schematic diagram of a fuzzy identification process for road surface adhesion coefficient under nonlinear driving conditions is provided as an embodiment of this application;

[0042] Figure 7 A schematic diagram illustrating the execution logic of a filtering algorithm provided in one embodiment of this application;

[0043] Figure 8 This is an example diagram of a vehicle road surface information recognition device according to an embodiment of this application;

[0044] Figure 9 This is a schematic diagram of the vehicle structure provided in an embodiment of this application.

[0045] Among them, 10-vehicle road information identification device, 100-acquisition module, 200-computation module, 300-search module, 901-memory, 902-processor, and 903-communication interface. Detailed Implementation

[0046] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0047] The following description, with reference to the accompanying drawings, outlines a method, apparatus, vehicle, and storage medium for identifying vehicle road surface information according to embodiments of this application. Addressing the problems mentioned in the background section, this application provides a method for identifying vehicle road surface information. In this method, vehicle driving data is collected, and the current driving condition of the vehicle is determined based on this data. If the current driving condition is a first preset condition, 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 a first preset strategy. If the current driving condition is a second preset condition, a second parameter of the road surface is calculated based on the vehicle driving data, and a second adhesion coefficient is obtained based on the second parameter and a second preset strategy. The first or second adhesion coefficient is then filtered to obtain a final adhesion coefficient value. Using the final adhesion coefficient value as an index, a preset standard road condition adhesion coefficient-road condition information relationship table is searched to obtain the current road condition information. This application acquires vehicle speed and other signals, and uses fuzzy rules to calculate and identify the road surface adhesion coefficient under various conditions such as steering, braking, and straight-line driving. It then provides real-time, clear feedback of the current road surface identification information to the driver, enabling them to better select the four-wheel drive operating mode and improving the customer's perception experience. This approach saves hardware costs while effectively improving the accuracy of adhesion coefficient identification and the coverage of driving conditions. Therefore, it solves the problems of existing technologies that can only estimate the adhesion coefficient under specific conditions, cannot meet the need for real-time data updates, and have low accuracy.

[0048] Specifically, Figure 1 This is a flowchart illustrating a method for recognizing vehicle road surface information provided in an embodiment of this application.

[0049] like Figure 1As shown, the method for identifying vehicle road information includes the following steps:

[0050] In step S101, vehicle driving data is collected, and the current driving condition of the vehicle is determined based on the vehicle driving data.

[0051] The embodiments of this application can collect vehicle driving data from the CAN (Controller Area Network) bus through the ESC (Electronic Stability Controller), EMS (Engine Management System), and TCU (Transmission Control Unit) systems, and output the collected data to the main four-wheel drive logic control system AWD (All-Wheel Drive). Figure 2 As shown, the current vehicle operating conditions, such as steering and braking, are determined based on the above data, thus providing a basis for the subsequent calculation of the adhesion coefficient.

[0052] Optionally, in one embodiment of this application, the vehicle's 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 slope signal.

[0053] It should be noted that, 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 slope signal, etc., thereby providing a large amount of reliable data support for subsequent operations such as adhesion coefficient calculation and fuzzy recognition processing.

[0054] In step S102, if the current driving condition of the vehicle is the first preset condition, the first parameter of the road surface is calculated based on the vehicle's driving data, and the first adhesion coefficient is calculated based on the first parameter and the first preset strategy. If the current driving condition of the vehicle is the second preset condition, the second parameter of the road surface is calculated based on the vehicle's driving data, and the second adhesion coefficient is obtained based on the second parameter and the second preset strategy. The first adhesion coefficient or the second adhesion coefficient is then filtered to obtain the final adhesion coefficient value.

[0055] After determining the vehicle's current driving condition by collecting vehicle driving data, the embodiments of this application can further determine the current driving condition of the vehicle by using front wheel steering angle and yaw rate signals, such as whether it is non-linear driving, so as to select and execute the corresponding algorithm to calculate the adhesion coefficient according to different driving conditions, thereby achieving accurate calculation of the adhesion coefficient under various driving conditions. The specific execution logic of adhesion coefficient calculation is as follows: Figure 3As shown.

[0056] Optionally, in one embodiment of this application, if the current driving condition of the vehicle is a first preset condition, then the first parameter of the road surface is calculated based on the vehicle's driving data, and the first adhesion coefficient is calculated based on the first parameter and the first preset strategy, including: calculating the slip ratio and the adhesion coefficient of the road surface based on the vehicle's driving data; performing fuzzification processing on the slip ratio and the adhesion coefficient, and obtaining six first similarity coefficients based on a preset fuzzy inference table; and calculating the first adhesion coefficient based on the first similarity coefficients.

[0057] It should be noted that when the vehicle is currently in linear driving condition, such as straight driving or braking, the embodiments of this application can use a longitudinal dynamics algorithm to calculate the slip ratio and the adhesion coefficient using the received vehicle driving data, and then fuzzify the parameters, perform inference through a fuzzy inference table, and finally defuzzify to obtain the similarity coefficients of six standard road surfaces, and then calculate the adhesion coefficient under the condition accordingly.

[0058] Specifically, the process of calculating the adhesion coefficient using longitudinal dynamics in the embodiments of this application is as follows:

[0059] 1. Calculate the slip ratio and the adhesion coefficient using the following formula:

[0060]

[0061]

[0062] Where, λ i v is the slip ratio. 轮 v is the wheel speed of the car. 车 For vehicle speed, μ i To utilize the adhesion coefficient, F xi F is the ground braking force generated on the i-th axis, μ is the adhesion coefficient at the previous moment, and F zi Let be the normal force exerted by the ground on the i-axis.

[0063] 2. Using the slip ratio and adhesion coefficient as input variables for fuzzy control, k 1～6 As the output variable; the final road surface adhesion coefficient value is calculated using the corresponding formula, such as... Figure 4 As shown;

[0064] 3. Transform the output of the fuzzy inference to obtain a clear control output, namely, a numerical value representing the similarity to six standard road surface curves. Some of the fuzzy inference rules are as follows: Figure 5 As shown.

[0065] Therefore, the embodiments of this application calculate the road adhesion coefficient of a vehicle under linear driving conditions using a longitudinal dynamics algorithm, thereby reducing the calculation cost of the adhesion coefficient and improving the efficiency and accuracy of the calculation.

[0066] Optionally, in one embodiment of this application, the formula for calculating the first adhesion coefficient is as follows:

[0067] λ opt =(k1λ1+k2λ2+k3λ3+k4λ4+k5λ5+k6λ6) / (k1+k2+k3+k4+k5+k6)

[0068] Where, λ opt k is the first adhesion coefficient. 1～6 λ is the first similarity coefficient. 1～6 It represents the slip ratio.

[0069] In the embodiments of this application, after obtaining parameters such as slip ratio and coefficient of adhesion, the slip ratio and coefficient of adhesion can be used as input variables for fuzzy control to calculate the final road surface adhesion coefficient value using the following formula:

[0070] λ opt =(k1λ1+k2λ2+k3λ3+k4λ4+k5λ5+k6λ6) / (k1+k2+k3+k4+k5+k6)

[0071] Where, λ opt k is the road surface adhesion coefficient. 1～6 Let λ be the similarity coefficient. 1～6 It represents the slip ratio.

[0072] Therefore, the embodiments of this application can calculate the road surface adhesion coefficient through parameters such as similarity coefficient and slip ratio, thereby providing guidance and basis for the subsequent use and prompts of the adhesion coefficient.

[0073] Optionally, in one embodiment of this application, if the current driving condition of the vehicle is a second preset condition, then the second parameters of the road surface are calculated based on the vehicle's driving data, and the second adhesion coefficient is obtained based on the second parameters and the second preset strategy, including: calculating the absolute value of the vehicle's lateral acceleration and the absolute value of the difference in yaw rate based on the vehicle's driving data; performing fuzzification processing on the absolute value of the lateral acceleration and the absolute value of the difference in yaw rate, and obtaining a second similarity coefficient based on a preset fuzzy inference table; and obtaining the second adhesion coefficient based on the second similarity coefficient and a preset correction formula.

[0074] When the vehicle is currently in a nonlinear driving condition, the embodiments of this application can calculate the road adhesion coefficient under this condition using a nonlinear dynamics algorithm.

[0075] Specifically, the process for calculating the road surface adhesion coefficient under nonlinear driving conditions in the embodiments of this application is as follows:

[0076] 1. Calculate the absolute value of the yaw rate deviation:

[0077]

[0078]

[0079] |dω|=|ω t -ω|

[0080] Where, ω t,s For steady-state yaw rate, ω t To correct the yaw angle, |dω| is the yaw angular velocity deviation, and v x l is the vehicle speed, l is the wheelbase, and m is the vehicle mass. f l r Where C is the wheelbase of the front and rear axles, ω is the actual yaw rate, and C is the yaw rate of the rear axle. f C r For the front and rear axle lateral stiffness;

[0081] 2. The lateral acceleration a y The absolute values ​​of the values ​​and the absolute values ​​of the yaw rate difference are used as input variables for the fuzzy rule, and k is the output variable, i.e., the similarity coefficient. The adhesion coefficient is then calculated using the modified formula, such as... Figure 6 As shown.

[0082] Therefore, the embodiments of this application calculate the absolute value of the lateral acceleration and the absolute value of the difference in yaw rate by receiving parameters, fuzzify the above parameters, and obtain the similarity coefficient by reasoning through a fuzzy inference table. Then, the adhesion coefficient is calculated by a correction formula, thereby realizing the accurate calculation of the road adhesion coefficient under nonlinear driving conditions.

[0083] Optionally, in one embodiment of this application, the preset correction formula is as follows:

[0084]

[0085]

[0086] Where f(k) is the correction function, k is the similarity coefficient, abcd are the correction function coefficients, μ is the road surface adhesion coefficient, γ is the proportionality coefficient (taken as 1.19), and a y ρ is the lateral acceleration, and g is the gravitational acceleration.

[0087] It should be noted that the formula for calculating the road surface adhesion coefficient under nonlinear driving conditions in this embodiment is as follows:

[0088]

[0089]

[0090] Where f(k) is the correction function, k is the similarity coefficient, abcd are the correction function coefficients (-2.2, 3.2, -2.1, 2 respectively), μ is the road surface adhesion coefficient, γ is the proportionality coefficient (taken as 1.19), and a y ρ is the lateral acceleration, and g is the gravitational acceleration.

[0091] Therefore, the embodiments of this application identify similarity factors and nonlinear factors under different driving conditions through the fuzzy rules of the AWD system calculation module, so as to calculate the estimated value of the adhesion coefficient under the current corresponding driving condition of the vehicle. Thus, the embodiments of this application do not require additional hardware devices such as sensors, effectively reducing the cost of adhesion coefficient identification and improving the accuracy of adhesion coefficient calculation and identification.

[0092] After obtaining the road surface adhesion coefficient under the corresponding vehicle operating conditions, embodiments of this application can further employ corresponding filtering algorithms to filter the road surface adhesion coefficient under different operating conditions, in order to obtain the final adhesion coefficient values ​​under different operating conditions, such as... Figure 7 As shown, by changing the magnitude of the corresponding coefficients, the sensitivity of the filtering algorithm can be adjusted, thereby effectively improving the accuracy and anti-interference ability of road surface adhesion coefficient estimation.

[0093] In step S103, the final adhesion coefficient value is used as an index to look up the preset standard road condition adhesion coefficient-road condition information relationship table to obtain the current road condition information of the road surface.

[0094] After obtaining the estimated final road surface adhesion coefficient under different road conditions, the embodiments of this application can further compare the final road surface adhesion coefficient with the standard road condition adhesion coefficient-road condition information relationship table to obtain the current road condition information of the road surface, and send the obtained currently identified road condition information to the IHU (Infotainment Head Unit), and prompt the driver through the central control screen to switch to AWD mode or select automatic switching, thereby improving the reliability and user-friendliness of the vehicle.

[0095] Optionally, in one embodiment of this application, after finding the relationship table between the preset standard road condition adhesion coefficient and road condition information by using the final adhesion coefficient value as an index to obtain the current road condition information of the road surface, the method further includes: generating a prompt signal and / or feedback signal based on the road condition information; receiving the prompt signal and / or feedback signal, and feeding back the current road condition information of the road surface to the user through acoustic and / or optical means.

[0096] It is understood that after obtaining the current road condition information, the embodiments of this application can also generate corresponding prompts and feedback signals based on the obtained current road condition information. When the vehicle terminal receives the signal, the four-wheel drive system receives the signal and uses it for front and rear wheel torque distribution to determine whether the current driving mode corresponds to the identified road condition. If it does not match, a pop-up window can be sent to the IHU central control or instrument panel with a prompt message "suggest switching driving mode or turning on automatic mode switching". The driver can click the button on the central control screen to choose whether to turn on automatic mode switching, switch manually, or ignore this reminder. In addition, the embodiments of this application can also send relevant voice prompt feedback information through a voice device.

[0097] Therefore, the embodiments of this application can meet the requirements of four-wheel drive torque distribution for road surface adhesion coefficient identification by identifying the road surface adhesion coefficient, and can also be used as the road surface adhesion coefficient input required by other systems; in addition, the embodiments of this application can also clearly feed back the current road surface identification information to the driver in real time, so that the driver can better select the four-wheel drive operation mode and improve the user's driving experience.

[0098] According to the vehicle road surface information recognition method proposed in this application, the vehicle's driving data is collected, and the current driving condition of the vehicle is determined based on the driving data. If the current driving condition of the vehicle is a first preset condition, the first parameter of the road surface is calculated based on the driving data of the vehicle, and the first adhesion coefficient is calculated based on the first parameter and the first preset strategy. If the current driving condition of the vehicle is a second preset condition, the second parameter of the road surface is calculated based on the driving data of the vehicle, and the second adhesion coefficient is obtained based on the second parameter and the second preset strategy. The first adhesion coefficient or the second adhesion coefficient is filtered to obtain the final adhesion coefficient value. The final adhesion coefficient value is used as an index to look up the relationship table of preset standard road condition adhesion coefficient and road condition information to obtain the current road condition information of the road surface. This method saves hardware costs and effectively improves the adhesion coefficient recognition accuracy and driving condition coverage.

[0099] Secondly, the vehicle road surface information recognition device according to the embodiments of this application is described with reference to the accompanying drawings.

[0100] Figure 8 This is a block diagram of a vehicle road information recognition device according to an embodiment of this application.

[0101] like Figure 8 As shown, the vehicle road information recognition device 10 includes: a data acquisition module 100, a calculation module 200, and a search module 300.

[0102] The acquisition module 100 is used to collect vehicle driving data and determine the current driving condition of the vehicle based on the vehicle driving data.

[0103] The calculation module 200 is used to calculate the first parameters of the road surface based on the vehicle's driving data if the current driving condition of the vehicle is a first preset condition, and calculate the first adhesion coefficient based on the first parameters and the first preset strategy. If the current driving condition of the vehicle is a second preset condition, the calculation module 200 is used to calculate the second parameters of the road surface based on the vehicle's driving data, obtain the second adhesion coefficient based on the second parameters and the second preset strategy, and filter the first adhesion coefficient or the second adhesion coefficient to obtain the final adhesion coefficient value.

[0104] The lookup module 300 is used to look up the relationship table of preset standard road condition adhesion coefficient and road condition information using the final adhesion coefficient value as an index, so as to obtain the current road condition information of the road surface.

[0105] Optionally, in one embodiment of this application, the vehicle's 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 slope signal.

[0106] Optionally, in one embodiment of this application, the calculation module 200 includes: a first calculation unit, a first fuzzy processing unit, and a second calculation unit.

[0107] The first calculation unit is used to calculate the slip ratio of the road surface and the adhesion coefficient based on vehicle driving data.

[0108] The first fuzzy processing unit is used to fuzzify the slip ratio and the adhesion coefficient, and obtain six first similarity coefficients based on a preset fuzzy inference table.

[0109] The second calculation unit is used to calculate the first adhesion coefficient based on the first similarity coefficient.

[0110] Optionally, in one embodiment of this application, the calculation module 200 further includes: a third calculation unit, a second fuzzy processing unit, and a correction unit.

[0111] The third calculation unit is used to calculate the absolute value of the vehicle's lateral acceleration and the absolute value of the difference in yaw rate based on the vehicle's driving data.

[0112] The second fuzzy processing unit is used to fuzzify the absolute value of the lateral acceleration and the absolute value of the difference in yaw rate, and obtain the second similarity coefficient according to the preset fuzzy inference table.

[0113] The correction unit is used to obtain the second adhesion coefficient based on the second similarity coefficient and the preset correction formula.

[0114] Optionally, in one embodiment of this application, the formula for calculating the first adhesion coefficient is as follows:

[0115] λ opt=(k1λ1+k2λ2+k3λ3+k4λ4+k5λ5+k6λ6) / (k1+k2+k3+k4+k5+k6)

[0116] Where, λ opt k is the first adhesion coefficient. 1～6 λ is the first similarity coefficient. 1～6 It represents the slip ratio.

[0117] Optionally, in one embodiment of this application, the preset correction formula is as follows:

[0118]

[0119]

[0120] Where f(k) is the correction function, k is the similarity coefficient, abcd are the correction function coefficients, μ is the road surface adhesion coefficient, γ is the proportionality coefficient (taken as 1.19), and a y ρ is the lateral acceleration, and g is the gravitational acceleration.

[0121] Optionally, in one embodiment of this application, the vehicle road surface information recognition device 10 of this application embodiment further includes: a generation module and a feedback module.

[0122] The generation module is used to look up a preset standard road condition adhesion coefficient-road condition information relationship table with the final adhesion coefficient value as an index, obtain the current road condition information of the road surface, and then generate prompt signals and / or feedback signals based on the road condition information.

[0123] The feedback module is used to receive prompt signals and / or feedback signals, and to provide the user with information on the current road conditions through acoustic and / or optical means.

[0124] It should be noted that the explanation of the above-described method for recognizing vehicle road surface information also applies to the vehicle road surface information recognition device of this embodiment, and will not be repeated here.

[0125] The vehicle road surface information recognition device proposed in this application collects vehicle driving data and determines the current driving condition of the vehicle based on the driving data. If the current driving condition of the vehicle is a first preset condition, the device calculates a first parameter of the road surface based on the vehicle driving data, and calculates a first adhesion coefficient based on the first parameter and a first preset strategy. If the current driving condition of the vehicle is a second preset condition, the device 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 strategy, and filters the first or second adhesion coefficient to obtain a final adhesion coefficient value. Using the final adhesion coefficient value as an index, the device searches a preset standard road condition adhesion coefficient-road condition information relationship table to obtain the current road condition information. This saves hardware costs while effectively improving the adhesion coefficient recognition accuracy and driving condition coverage.

[0126] Figure 9 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include:

[0127] The memory 901, the processor 902, and the computer program stored on the memory 901 and capable of running on the processor 902.

[0128] When the processor 902 executes the program, it implements the vehicle road information recognition method provided in the above embodiments.

[0129] Furthermore, the vehicle also includes:

[0130] Communication interface 903 is used for communication between memory 901 and processor 902.

[0131] The memory 901 is used to store computer programs that can run on the processor 902.

[0132] The memory 901 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0133] If the memory 901, processor 902, and communication interface 903 are implemented independently, then the communication interface 903, memory 901, and processor 902 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 9 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0134] Optionally, in a specific implementation, if the memory 901, processor 902, and communication interface 903 are integrated on a single chip, then the memory 901, processor 902, and communication interface 903 can communicate with each other through an internal interface.

[0135] The processor 902 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0136] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for recognizing vehicle road information.

[0137] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0138] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0139] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0140] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0141] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0142] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0143] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0144] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for identifying vehicle road surface information, characterized in that, Includes the following steps: Collect vehicle driving data and determine the vehicle's current driving condition based on the vehicle driving data; If the vehicle's current driving condition is 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 a first preset strategy. If the vehicle's current driving condition is 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 a second preset strategy. The first or second adhesion coefficient is then filtered to obtain a final adhesion coefficient value. Here, the first preset condition is a linear driving condition, and the second preset condition is a non-linear driving condition. Using the final adhesion coefficient value as an index, the relationship table between the preset standard road condition adhesion coefficient and road condition information is searched to obtain the current road condition information of the road surface; Wherein, if the current driving condition of the vehicle is a second preset condition, then calculating the second parameter of the road surface based on the vehicle's driving data, and obtaining the second adhesion coefficient based on the second parameter and the second preset strategy, includes: The steady-state yaw rate of the vehicle is calculated based on the vehicle driving data, and the corresponding corrected yaw rate is calculated using the steady-state yaw rate. The difference between the corrected yaw rate and the actual yaw rate is calculated to obtain the yaw rate difference. The absolute value of the vehicle's lateral acceleration and the absolute value of the yaw rate difference are calculated based on the vehicle driving data. The absolute values ​​of the lateral acceleration and the absolute value of the difference in yaw rate are fuzzified, and a second similarity coefficient is obtained based on a preset fuzzy inference table. The second adhesion coefficient is obtained based on the second similarity coefficient and the preset correction formula.

2. The method according to claim 1, characterized in that, The vehicle's 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.

3. The method according to claim 1, characterized in that, If the current driving condition of the vehicle is a first preset condition, then the first parameter of the road surface is calculated based on the vehicle's driving data, and the first adhesion coefficient is calculated based on the first parameter and the first preset strategy, including: The slip ratio and adhesion coefficient of the road surface are calculated based on the vehicle driving data. The slip ratio and the adhesion coefficient are fuzzified, 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.

4. The method according to claim 3, characterized in that, The formula for calculating the first adhesion coefficient is as follows: Where, λ opt The first adhesion coefficient, k 1~6 The first similarity coefficient, λ 1~6 The adhesion coefficient is used as described above.

5. The method according to claim 1, characterized in that, The preset correction formula is as follows: in, f ( k ) is the correction function. k The second similarity coefficient, a, b, c, d To correct the coefficients of the function, μ The desired road surface adhesion coefficient is... γ The scaling factor is set to 1.

19. a y It is lateral acceleration. g This is the acceleration due to gravity.

6. The method according to claim 1, characterized in that, After using the final adhesion coefficient value as an index to look up the relationship table of preset standard road condition adhesion coefficient and road condition information to obtain the current road condition information of the road surface, the method further includes: Generate prompt signals and / or feedback signals based on the road condition information; The system receives the prompt signal and / or feedback signal, and provides the user with the current road condition information of the road surface through acoustic and / or optical means.

7. A vehicle road surface information recognition device, characterized in that, include: The data acquisition module is used to collect vehicle driving data and determine the current driving condition of the vehicle based on the vehicle driving data. The calculation module is configured to: if the vehicle's current driving condition is a first preset condition, calculate a first parameter of the road surface based on the vehicle's driving data; calculate a first adhesion coefficient based on the first parameter and a first preset strategy; if the vehicle's current driving condition is a second preset condition, calculate a second parameter of the road surface based on the vehicle's driving data; obtain a second adhesion coefficient based on the second parameter and a second preset strategy; and filter either the first or second adhesion coefficient to obtain a final adhesion coefficient value. The first preset condition is a linear driving condition, and the second preset condition is a non-linear driving condition. The lookup module is used to look up a preset standard road condition adhesion coefficient-road condition information relationship table using the final adhesion coefficient value as an index, and obtain the current road condition information of the road surface. The calculation module includes: The third calculation unit is used to calculate the steady-state yaw rate of the vehicle based on the vehicle driving data, and to calculate the corresponding corrected yaw rate using the steady-state yaw rate. It also calculates the difference between the corrected yaw rate and the actual yaw rate to obtain the yaw rate difference, and calculates the absolute value of the vehicle's lateral acceleration and the absolute value of the yaw rate difference based on the vehicle driving data. The second fuzzy processing unit is used to fuzzify the absolute value of the lateral acceleration and the absolute value of the difference in yaw rate, and obtain the second similarity coefficient according to the preset fuzzy inference table. The correction unit is used to obtain the second adhesion coefficient based on the second similarity coefficient and the preset correction formula.

8. A vehicle, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the method for identifying vehicle road information as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the method for recognizing vehicle road surface information as described in any one of claims 1-6.

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