Road surface roughness evaluation method, device, computer equipment and storage medium

Abstract

The present application relates to a road surface roughness assessment method, device, computer equipment and storage medium, and belongs to the field of road surface detection technology. The method includes: obtaining and preprocessing the wheel speed of the target vehicle to obtain the first characteristic data; based on the first characteristic data, using a preset function to calculate and obtain the second characteristic data to determine whether to trigger the road surface roughness assessment mechanism; in response to detecting that the road surface roughness assessment mechanism is triggered, based on the second characteristic data, determining the first road surface roughness assessment level; using the first road surface roughness assessment level to verify the preset road surface roughness assessment level, and determining the target road surface roughness assessment level according to the verification result, so as to achieve the evaluation of the road surface roughness. The present application reduces the corresponding cost while ensuring the timely and accurate evaluation of the road surface roughness.

Description

Technical Field

[0001] The present application relates to the technical field of road surface detection, and in particular to a road surface smoothness evaluation method, device, computer equipment and storage medium. Background Art

[0002] Because the road surface is often subjected to the load of large and fast-moving vehicles and is in a natural environment, it is easy to be damaged. Once damaged, the road condition will deteriorate rapidly. Uneven road surface will affect riding comfort and cause vehicle damage, tire wear, increased fuel consumption and increased accident rate. At present, with users' increasing demands for the comfort and power of new energy vehicles, how to conduct timely and accurate evaluation of the road surface flatness so that the vehicle system can make real-time and accurate response to the evaluation results, thereby improving the comfort of new energy vehicles, is an urgent problem to be solved.

[0003] Existing technologies usually collect relevant data by adding multiple sensors to calculate the flatness of the road surface. This method is not universal and has high cost.

[0004] Therefore, there is an urgent need to propose a road surface roughness evaluation method, device, computer equipment and storage medium that can reduce costs while ensuring high accuracy. Summary of the invention

[0005] Based on this, it is necessary to provide a road surface roughness evaluation method, device, computer equipment and storage medium that can reduce costs while ensuring high accuracy in response to the above technical problems.

[0006] In one aspect, a method for evaluating road surface roughness is provided, the method comprising:

[0007] Acquiring the wheel speed of the target vehicle and preprocessing the wheel speed to obtain first characteristic data;

[0008] Based on the first characteristic data, a preset function is used to calculate and obtain second characteristic data to determine whether to trigger a road surface roughness assessment mechanism;

[0009] In response to detecting that the road surface roughness assessment mechanism is triggered, determining a first road surface roughness assessment level based on the second feature data;

[0010] The preset road surface flatness assessment level is verified using the first road surface flatness assessment level, and a target road surface flatness assessment level is determined based on the verification result to achieve the assessment of the road surface flatness.

[0011] Optionally, the wheel speed includes at least a wheel speed, and the wheel speed is preprocessed to obtain the first characteristic data including:

[0012] Obtaining the wheel speed of at least one wheel on the target vehicle;

[0013] Based on a low-pass filtering mechanism, the wheel speed is preprocessed to obtain a target data value;

[0014] The target data value is first-order derived to obtain the wheel acceleration of the target vehicle, and the wheel acceleration is the first characteristic data.

[0015] Optionally, the preset function includes at least a first preset function, a second preset function and a third preset function, the second characteristic data includes at least a root mean square value, a variance value and a peak-to-peak value, and based on the first characteristic data, obtaining the second characteristic data by calculating using the preset function includes:

[0016] Acquire first characteristic data within N operation cycles;

[0017] The root mean square value corresponding to the first characteristic data is calculated using the first preset function, where the first preset function includes:

[0018]

[0019] Among them, X Rms represents the RMS value, N represents the number of operating cycles, X i represents the wheel acceleration corresponding to the i-th operating cycle;

[0020] The variance value corresponding to the first feature data is calculated using the second preset function, where the second preset function includes:

[0021]

[0022] Among them, σ represents the variance value, μ represents the average value of wheel acceleration;

[0023] The peak-to-peak value corresponding to the first characteristic data is calculated using the third preset function, wherein the third preset function includes:

[0024] X Delta =X i max -X i min

[0025] Among them, X Delta represents the peak-to-peak value, X i max Indicates the maximum wheel acceleration, X i min Indicates the minimum wheel acceleration.

[0026] Optionally, based on the second characteristic data, determining whether to trigger a road surface roughness assessment mechanism includes:

[0027] In response to detecting that the RMS value is greater than a first preset value for more than a preset number of times within N operating cycles, and / or the variance value is greater than a second preset value for more than a preset number of times, and / or the peak-to-peak value is greater than a third preset value for more than a preset number of times, the road surface roughness assessment mechanism is triggered.

[0028] Optionally, determining the first road surface roughness assessment grade based on the second characteristic data includes:

[0029] Based on a preset mapping table, respectively obtaining the road surface smoothness assessment grades corresponding to the root mean square value, the variance value, and the peak-to-peak value;

[0030] The first road surface roughness evaluation grade is calculated and determined based on a fourth preset function, wherein the fourth preset function includes:

[0031]

[0032] Among them, τ1, τ2, τ3 are weight coefficients, and the sum is 1, α represents the first road surface roughness evaluation level, μ Rms Indicates the road surface roughness evaluation grade corresponding to the root mean square value, Indicates the road surface roughness evaluation level corresponding to the variance value, μ Delta Indicates the road surface roughness assessment level corresponding to the peak-to-peak value.

[0033] Optionally, using the first road surface roughness assessment level to verify a preset road surface roughness assessment level, and determining a target road surface roughness assessment level according to the verification result includes:

[0034] In response to detecting that the absolute value of the difference between the first road surface roughness evaluation level and the preset road surface roughness evaluation level is greater than a fourth preset value, determining the first road surface roughness evaluation level as the target road surface roughness evaluation level;

[0035] In response to detecting that the absolute value of the difference between the first road surface roughness evaluation level and the preset road surface roughness evaluation level is less than or equal to a fourth preset value, the preset road surface roughness evaluation level is determined as the target road surface roughness evaluation level.

[0036] Optionally, the method further includes:

[0037] In response to detecting that the root mean square value is less than a first preset value, the variance value is less than a second preset value, and the peak-to-peak value is less than a third preset value within a target time period, the road surface roughness evaluation mechanism is exited.

[0038] In another aspect, a road surface roughness assessment device is provided, the device comprising:

[0039] A preprocessing module, used for acquiring the wheel speed of the target vehicle and preprocessing the wheel speed to obtain first characteristic data;

[0040] A first determination module, configured to obtain second characteristic data by calculating using a preset function based on the first characteristic data, so as to determine whether to trigger a road surface roughness assessment mechanism;

[0041] a second determination module, configured to determine a first road surface roughness assessment level based on the second feature data in response to detecting that the road surface roughness assessment mechanism is triggered;

[0042] The evaluation result verification module is used to use the first road surface flatness evaluation level to verify the preset road surface flatness evaluation level, determine the target road surface flatness evaluation level according to the verification result, and realize the evaluation of road surface flatness.

[0043] In another aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are implemented:

[0044] Acquiring the wheel speed of the target vehicle and preprocessing the wheel speed to obtain first characteristic data;

[0045] Based on the first characteristic data, a preset function is used to calculate and obtain second characteristic data to determine whether to trigger a road surface roughness assessment mechanism;

[0046] In response to detecting that the road surface roughness assessment mechanism is triggered, determining a first road surface roughness assessment level based on the second feature data;

[0047] The preset road surface flatness assessment level is verified using the first road surface flatness assessment level, and a target road surface flatness assessment level is determined based on the verification result to achieve the assessment of the road surface flatness.

[0048] In another aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0049] Acquiring the wheel speed of the target vehicle and preprocessing the wheel speed to obtain first characteristic data;

[0050] Based on the first characteristic data, a preset function is used to calculate and obtain second characteristic data to determine whether to trigger a road surface roughness assessment mechanism;

[0051] In response to detecting that the road surface roughness assessment mechanism is triggered, determining a first road surface roughness assessment level based on the second feature data;

[0052] The preset road surface flatness assessment level is verified using the first road surface flatness assessment level, and a target road surface flatness assessment level is determined based on the verification result to achieve the assessment of the road surface flatness.

[0053] The above-mentioned road surface roughness assessment method, device, computer equipment and storage medium, the method includes: obtaining the wheel speed of the target vehicle, and preprocessing the wheel speed to obtain first characteristic data; based on the first characteristic data, using a preset function to calculate and obtain second characteristic data to determine whether to trigger a road surface roughness assessment mechanism; in response to detecting that the road surface roughness assessment mechanism is triggered, determining a first road surface roughness assessment level based on the second characteristic data; using the first road surface roughness assessment level to verify the preset road surface roughness assessment level, and determining the target road surface roughness assessment level based on the verification result to achieve an assessment of the road surface roughness. The present application determines the road surface roughness assessment level by processing the vehicle-related data without adding additional sensors, thereby reducing the corresponding cost while ensuring timely and accurate assessment of the road surface roughness. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A diagram showing an application environment of a road surface roughness assessment method in one embodiment;

[0055] Figure 2 A schematic diagram of a process of evaluating a road surface flatness in one embodiment;

[0056] Figure 3 is a structural block diagram of a road surface flatness evaluation device in one embodiment;

[0057] Figure 4 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0059] It should be understood that in the description of the present application, unless the context clearly requires otherwise, words such as "include", "comprises", and the like throughout the specification should be interpreted as including rather than being exclusive or exhaustive; that is, as including but not limited to.

[0060] It should also be understood that the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of this application, unless otherwise specified, "plurality" means two or more.

[0061] It should be noted that the terms "S1", "S2", etc. are only used for the purpose of describing the steps, and do not specifically refer to the order or sequence, nor are they used to limit the present application. They are only for the convenience of describing the method of the present application, and cannot be understood as indicating the order of the steps. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present application.

[0062] The road surface roughness evaluation method provided in this application can be applied to Figure 1 In the vehicle 100 shown, the vehicle 100 may include an onboard terminal 120. The onboard terminal 120 includes at least one memory and at least one processor, wherein a computer program is stored in the at least one memory, and when the computer program is executed by the at least one processor, the road surface roughness evaluation method according to the exemplary embodiment of the present disclosure is executed. Here, the onboard terminal 120 is not necessarily a single electronic device, but may also be any collection of devices or circuits that can execute the above-mentioned computer program alone or in combination.

[0063] In the vehicle terminal 120, the processor may include a central processing unit (CPU), a graphics processing unit (GPU), a programmable logic device, a dedicated processor system, a microcontroller or a microprocessor. As an example and not a limitation, the processor may also include an analog processor, a digital processor, a microprocessor, a multi-core processor, a processor array, a network processor, etc.; in the vehicle terminal 120, the processor may run a computer program stored in a memory, which may be divided into one or more modules / units (such as computer program 1, computer program 2, ...), and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the terminal device. The memory may be integrated with the processor, for example, RAM or flash memory is arranged within an integrated circuit microprocessor, etc. In addition, the memory may include an independent device, such as an external disk drive, a storage array, or any other storage device that can be used by any database system. The memory and the processor may be coupled in operation, or may communicate with each other, for example, through an I / O port, a network connection, etc., so that the processor can read files stored in the memory.

[0064] In addition, the vehicle terminal 120 may also include a display device (such as a liquid crystal display, etc.) and a user interaction interface (such as a keyboard, a mouse, a touch input device, etc.), and all components of the vehicle terminal 120 may be connected to each other via a bus and / or a network.

[0065] In one embodiment, Figure 2 As shown in FIG. 1 , a road surface roughness evaluation method is provided, and the method is applied to Figure 1 The terminal in is used as an example to illustrate, including the following steps:

[0066] S1: Acquire the wheel speed of the target vehicle, and pre-process the wheel speed to obtain first characteristic data.

[0067] It should be noted that the relevant data of the target vehicle that needs to be obtained includes the vehicle speed in addition to the wheel speed. The wheel speed is obtained by a wheel speed sensor carried by the vehicle itself. The first characteristic data is the wheel acceleration.

[0068] In some specific implementations, the acquiring the wheel speed of the target vehicle and preprocessing the wheel speed to obtain the first characteristic data includes:

[0069] Obtaining the wheel speed of at least one wheel on the target vehicle;

[0070] Based on the low-pass filtering mechanism, the wheel speed is pre-processed to obtain the target data value, that is, the useless high-frequency components in the collected wheel speed data set are eliminated by low-pass filtering to ensure the accuracy of the subsequent road surface roughness evaluation;

[0071] The target data value is first-order derived to obtain the wheel acceleration of the target vehicle, where the wheel acceleration is the first characteristic data, and the target data value is the wheel speed obtained after low-pass filtering.

[0072] In the above implementation, the accuracy of subsequent road surface roughness assessment can be improved by preprocessing the collected relevant data.

[0073] S2: Based on the first characteristic data, a preset function is used to calculate and obtain second characteristic data to determine whether to trigger a road surface roughness assessment mechanism.

[0074] It should be noted that the preset functions include at least a first preset function, a second preset function and a third preset function, and the second characteristic data includes at least a root mean square value, a variance value and a peak-to-peak value, wherein the peak-to-peak value refers to the difference between the highest value and the lowest value of the signal within a cycle, that is, the range between the maximum and the minimum, which describes the size of the signal value variation range.

[0075] In some specific implementations, based on the first feature data, obtaining the second feature data by calculating using a preset function includes:

[0076] Obtaining first characteristic data within N operating cycles, wherein the operating cycle can be set according to actual needs and can be a single time node, and the first characteristic data is the wheel acceleration obtained in the above step;

[0077] The root mean square value corresponding to the first characteristic data is calculated using the first preset function, where the first preset function includes:

[0078]

[0079] Among them, X Rms represents the RMS value, N represents the number of operating cycles, X i represents the wheel acceleration corresponding to the i-th operating cycle;

[0080] Further, the variance value corresponding to the first feature data is calculated using the second preset function, and the second preset function includes:

[0081]

[0082] Among them, σ represents the variance value, μ represents the average value of wheel acceleration;

[0083] The peak-to-peak value corresponding to the first characteristic data is calculated using the third preset function, wherein the third preset function includes:

[0084] X Delta =X i max -X i min

[0085] Among them, X Delta represents the peak-to-peak value, X i max Indicates the maximum wheel acceleration, X i min Indicates the minimum wheel acceleration.

[0086] In some specific implementations, determining whether to trigger a road surface roughness assessment mechanism based on the second characteristic data includes:

[0087] In response to detecting that the number of times the root mean square value is greater than the first preset value within N operating cycles is greater than the preset number of times, and / or the number of times the variance value is greater than the second preset value is greater than the preset number of times, and / or the number of times the peak-to-peak value is greater than the third preset value is greater than the preset number of times, a road surface flatness evaluation mechanism is triggered, wherein the first preset value, the second preset value, the third preset value and the preset number of times can all be set according to actual needs.

[0088] Specifically, the evaluation of the road surface smoothness by the root mean square value, the variance value and the peak-to-peak value includes: if the root mean square value is larger, it means that the tire is more obviously excited to vibrate and the road surface condition is worse; similarly, if the variance value is larger, it means that the tire is more obviously excited to vibrate and the road surface condition is worse; and the more times the peak-to-peak value is greater than the third preset value, it means that the tire is more obviously excited to vibrate and the road surface condition is worse. Therefore, if the root mean square value is greater than the first preset value within N operating cycles, it means that the road surface is uneven. When the road surface is uneven, the road surface smoothness evaluation mechanism is triggered. If its variance value is greater than the second preset value within N operating cycles, it means that the road surface is uneven. value, it indicates that the road surface is uneven. When the road surface is uneven, the road surface roughness evaluation mechanism is triggered. If the number of times the peak-to-peak value is greater than the third preset value within N operating cycles is greater than the preset number of times, it indicates that the road surface is uneven. When the road surface is uneven, the road surface roughness evaluation mechanism is triggered. In order to distinguish different road surface roughness levels, different thresholds need to be preset. In order to improve the judgment accuracy, the number of times is preset. The road surface roughness evaluation level is determined by combining the two. The larger the peak-to-peak value and the more times, the more uneven the road surface. When any of the above three constraints is met, the road surface roughness evaluation mechanism is triggered.

[0089] In the above implementation, the root mean square value, variance value and peak-to-peak value are obtained by calculating the wheel acceleration, and corresponding constraints are set to determine whether to trigger the road surface roughness assessment mechanism to further improve the accuracy of the road surface roughness assessment.

[0090] S3: In response to detecting that the road surface roughness assessment mechanism is triggered, determining a first road surface roughness assessment level based on the second feature data.

[0091] It should be noted that this step specifically includes:

[0092] Based on a preset mapping table, obtaining the road surface smoothness assessment grade corresponding to the root mean square value, the variance value, and the peak-to-peak value, respectively, wherein the preset mapping table includes at least one mapping relationship between the root mean square value and the road surface smoothness assessment grade, the variance value and the road surface smoothness assessment grade, and the peak-to-peak value and the road surface smoothness assessment grade, wherein the road surface smoothness assessment grade is a value obtained through multiple tests combined with expert scoring;

[0093] The first road surface roughness evaluation grade is calculated and determined based on a fourth preset function, wherein the fourth preset function includes:

[0094]

[0095] Among them, τ1, τ2, τ3 are weight coefficients, and the sum is 1, α represents the first road surface roughness evaluation level, μ Rms Indicates the road surface roughness evaluation grade corresponding to the root mean square value, Indicates the road surface roughness evaluation level corresponding to the variance value, μ Delta represents the road surface roughness evaluation level corresponding to the peak-to-peak value, wherein a higher first road surface roughness evaluation level indicates a more uneven road surface. The weight coefficient is obtained by multiple tests combined with expert scoring to obtain the value;

[0096] In the above implementation, the importance of the root mean square value, the variance value, and the peak-to-peak value is used to determine the corresponding weight coefficient, and then the final road surface roughness assessment level is calculated and determined, thereby further improving the accuracy of the road surface roughness assessment.

[0097] S4: using the first road surface roughness assessment level to verify the preset road surface roughness assessment level, and determining the target road surface roughness assessment level according to the verification result, so as to implement the road surface roughness assessment.

[0098] It should be noted that the preset road surface flatness is determined by the camera preview information to determine the road surface flatness level, which is determined by image processing results. The image processing and flatness level determination process is a common method and will not be repeated here.

[0099] In some specific implementations, using the first road surface roughness assessment level to verify a preset road surface roughness assessment level, and determining a target road surface roughness assessment level according to the verification result includes:

[0100] In response to detecting that the absolute value of the difference between the first road surface roughness evaluation level and the preset road surface roughness evaluation level is greater than a fourth preset value, determining the first road surface roughness evaluation level as the target road surface roughness evaluation level, wherein the fourth preset value can be set according to actual needs;

[0101] In response to detecting that the absolute value of the difference between the first road surface roughness evaluation level and the preset road surface roughness evaluation level is less than or equal to a fourth preset value, determining the preset road surface roughness evaluation level as the target road surface roughness evaluation level;

[0102] Specifically, the road surface roughness level is judged based on the calculated road surface roughness and the camera preview information, that is, the camera preview information is used to perform feedforward judgment on the road surface roughness, and then the road surface roughness is calculated and verified in combination with the wheel acceleration. If the level difference between the road surface roughness calculated by the wheel acceleration and the preview road surface roughness is less than or equal to a fourth preset value, the first road surface roughness assessment level is used as the final result. If the level difference is greater than the fourth preset value, the preset road surface roughness assessment level is used as the final result.

[0103] In some specific implementations, in order to prevent the vehicle from being on an uneven road again and affecting the control of the entire vehicle, its root mean square value, variance value, and peak-to-peak value are also required to determine whether to exit the road surface roughness evaluation mechanism, that is, when it is detected that the root mean square value is less than the first preset value, the variance value is less than the second preset value, and the peak-to-peak value is less than the third preset value within the target time period, the road surface roughness evaluation mechanism is exited, wherein the evaluation mechanism is not avoided to avoid frequent jumps, and the root mean square value, variance value, and peak-to-peak value here are respectively kept at a certain interval with the corresponding value of the above-mentioned triggering road surface roughness evaluation mechanism, such as the interval value is A The absolute value of the difference between the root mean square value when the mechanism is triggered and the root mean square value when the mechanism is exited is A. The target time period can be set according to actual needs. The start time node and the time node that triggers the road surface roughness evaluation mechanism are kept at a time interval. The above interval value is related to the road surface roughness grade and the vehicle speed. If the road surface roughness grade is higher and the vehicle speed is higher, the interval value and the target time period are longer. If the root mean square value, variance value, and peak-to-peak value all meet the above constraints within the target time period, it is considered that the road surface tends to be flat, and the road surface roughness grade can be restored to the default state step by step.

[0104] In the above embodiment, the final road surface roughness assessment level is determined by the above verification method, which further improves the accuracy of the road surface roughness assessment, and sets the road surface roughness assessment mechanism exit rules to avoid the vehicle being on an uneven road surface again and affecting the control of the entire vehicle, thereby improving the vehicle user experience.

[0105] In some specific embodiments, when the road surface roughness assessment mechanism is triggered, it is necessary to adjust the vehicle operating parameters according to the road surface roughness assessment level, such as determining the damping coefficient adjustment value corresponding to the road surface roughness assessment level based on a mapping relationship table between the road surface roughness assessment level and the damping coefficient adjustment value, so as to adjust the current damping coefficient of the vehicle downward, thereby improving the comfort of the entire vehicle. For example, the current damping coefficient is X, and the damping coefficient adjustment value is Y. Because the higher the road surface roughness level is, the more uneven the road surface is, the value obtained by XY needs to be used as the target damping coefficient. The mapping relationship table includes at least one mapping relationship generated by a road surface roughness assessment level and a damping coefficient adjustment value, and the damping coefficient adjustment value is a value obtained through multiple tests and combined with expert scores. Furthermore, when the vehicle is on a flat road, the adaptive adjustment of the vehicle is stopped, thereby realizing intelligent control of vehicle comfort and improving the user's vehicle experience.

[0106] In the above-mentioned road surface roughness assessment method, the method includes: obtaining the wheel speed of the target vehicle and preprocessing the wheel speed to obtain first characteristic data; based on the first characteristic data, obtaining second characteristic data using a preset function calculation to determine whether to trigger a road surface roughness assessment mechanism; in response to detecting that the road surface roughness assessment mechanism is triggered, determining a first road surface roughness assessment level based on the second characteristic data; using the first road surface roughness assessment level to verify the preset road surface roughness assessment level, and determining the target road surface roughness assessment level based on the verification result to achieve an assessment of the road surface roughness. The present application determines the road surface roughness assessment level by processing the vehicle-related data without adding additional sensors, thereby reducing the corresponding costs and improving the user's vehicle experience while ensuring timely and accurate assessment of the road surface roughness.

[0107] It should be understood that although Figure 2 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 2 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0108] In one embodiment, Figure 3 As shown, a road surface flatness evaluation device is provided, comprising: a preprocessing module, a first determination module, a second determination module and an evaluation result verification module, wherein:

[0109] A preprocessing module, used for acquiring the wheel speed of the target vehicle and preprocessing the wheel speed to obtain first characteristic data;

[0110] A first determination module, configured to obtain second characteristic data by calculating using a preset function based on the first characteristic data, so as to determine whether to trigger a road surface roughness assessment mechanism;

[0111] a second determination module, configured to determine a first road surface roughness assessment level based on the second feature data in response to detecting that the road surface roughness assessment mechanism is triggered;

[0112] The evaluation result verification module is used to use the first road surface flatness evaluation level to verify the preset road surface flatness evaluation level, determine the target road surface flatness evaluation level according to the verification result, and realize the evaluation of road surface flatness.

[0113] As a preferred implementation, in the embodiment of the present invention, the preprocessing module is specifically used for:

[0114] Obtaining the wheel speed of at least one wheel on the target vehicle;

[0115] Based on a low-pass filtering mechanism, the wheel speed is preprocessed to obtain a target data value;

[0116] The target data value is first-order derived to obtain the wheel acceleration of the target vehicle, and the wheel acceleration is the first characteristic data.

[0117] As a preferred implementation manner, in the embodiment of the present invention, the first determination module is specifically used for:

[0118] Defining the preset function to include at least a first preset function, a second preset function and a third preset function, the second characteristic data to include at least a root mean square value, a variance value and a peak-to-peak value, and based on the first characteristic data, using the preset function to calculate and obtain the second characteristic data includes:

[0119] Acquire first characteristic data within N operation cycles;

[0120] The root mean square value corresponding to the first characteristic data is calculated using the first preset function, where the first preset function includes:

[0121]

[0122] Among them, X Rms represents the RMS value, N represents the number of operating cycles, X i represents the wheel acceleration corresponding to the i-th operating cycle;

[0123] The variance value corresponding to the first feature data is calculated using the second preset function, where the second preset function includes:

[0124]

[0125] Among them, σ represents the variance value, μ represents the average value of wheel acceleration;

[0126] The peak-to-peak value corresponding to the first characteristic data is calculated using the third preset function, wherein the third preset function includes:

[0127] X Delta =X i max -X i min

[0128] Among them, X Delta represents the peak-to-peak value, X i max Indicates the maximum wheel acceleration, X i min Indicates the minimum wheel acceleration.

[0129] As a preferred implementation manner, in the embodiment of the present invention, the first determination module is further configured to:

[0130] In response to detecting that the RMS value is greater than a first preset value for more than a preset number of times within N operating cycles, and / or the variance value is greater than a second preset value for more than a preset number of times, and / or the peak-to-peak value is greater than a third preset value for more than a preset number of times, the road surface roughness assessment mechanism is triggered.

[0131] As a preferred implementation manner, in the embodiment of the present invention, the second determination module is specifically used for:

[0132] Based on a preset mapping table, respectively obtaining the road surface smoothness assessment grades corresponding to the root mean square value, the variance value, and the peak-to-peak value;

[0133] The first road surface roughness evaluation grade is calculated and determined based on a fourth preset function, wherein the fourth preset function includes:

[0134]

[0135] Among them, τ1, τ2, τ3 are weight coefficients, and the sum is 1, α represents the first road surface roughness evaluation level, μ Rms Indicates the road surface roughness evaluation grade corresponding to the root mean square value, Indicates the road surface roughness evaluation level corresponding to the variance value, μ Delta Indicates the road surface roughness assessment level corresponding to the peak-to-peak value.

[0136] As a preferred implementation, in the embodiment of the present invention, the evaluation result verification module is specifically used for:

[0137] In response to detecting that the absolute value of the difference between the first road surface roughness evaluation level and the preset road surface roughness evaluation level is greater than a fourth preset value, determining the first road surface roughness evaluation level as the target road surface roughness evaluation level;

[0138] In response to detecting that the absolute value of the difference between the first road surface roughness evaluation level and the preset road surface roughness evaluation level is less than or equal to a fourth preset value, the preset road surface roughness evaluation level is determined as the target road surface roughness evaluation level.

[0139] As a preferred implementation, in the embodiment of the present invention, the device further includes an exit module, and the exit module is specifically used to:

[0140] In response to detecting that the root mean square value is less than a first preset value, the variance value is less than a second preset value, and the peak-to-peak value is less than a third preset value within a target time period, the road surface roughness evaluation mechanism is exited.

[0141] The specific definition of the road surface roughness evaluation device can be found in the definition of the road surface roughness evaluation method above, which will not be repeated here. Each module in the above-mentioned road surface roughness evaluation device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0142] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for evaluating the flatness of a road surface is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0143] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0144] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0145] S1: acquiring the wheel speed of the target vehicle, and preprocessing the wheel speed to obtain first characteristic data;

[0146] S2: Based on the first characteristic data, a preset function is used to calculate and obtain second characteristic data to determine whether to trigger a road surface roughness assessment mechanism;

[0147] S3: in response to detecting that the road surface roughness assessment mechanism is triggered, determining a first road surface roughness assessment level based on the second feature data;

[0148] S4: using the first road surface roughness assessment level to verify the preset road surface roughness assessment level, and determining the target road surface roughness assessment level according to the verification result, so as to implement the road surface roughness assessment.

[0149] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0150] Obtaining the wheel speed of at least one wheel on the target vehicle;

[0151] Based on a low-pass filtering mechanism, the wheel speed is preprocessed to obtain a target data value;

[0152] The target data value is first-order derived to obtain the wheel acceleration of the target vehicle, and the wheel acceleration is the first characteristic data.

[0153] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0154] Defining the preset function to include at least a first preset function, a second preset function and a third preset function, the second characteristic data to include at least a root mean square value, a variance value and a peak-to-peak value, and based on the first characteristic data, using the preset function to calculate and obtain the second characteristic data includes:

[0155] Acquire first characteristic data within N operation cycles;

[0156] The root mean square value corresponding to the first characteristic data is calculated using the first preset function, where the first preset function includes:

[0157]

[0158] Among them, X Rms represents the RMS value, N represents the number of operating cycles, X i represents the wheel acceleration corresponding to the i-th operating cycle;

[0159] The variance value corresponding to the first feature data is calculated using the second preset function, where the second preset function includes:

[0160]

[0161] Among them, σ represents the variance value, μ represents the average value of wheel acceleration;

[0162] The peak-to-peak value corresponding to the first characteristic data is calculated using the third preset function, wherein the third preset function includes:

[0163] X Delta =X i max -X i min

[0164] Among them, X Delta represents the peak-to-peak value, X i max Indicates the maximum wheel acceleration, X i min Indicates the minimum wheel acceleration.

[0165] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0166] In response to detecting that the RMS value is greater than a first preset value for more than a preset number of times within N operating cycles, and / or the variance value is greater than a second preset value for more than a preset number of times, and / or the peak-to-peak value is greater than a third preset value for more than a preset number of times, the road surface roughness assessment mechanism is triggered.

[0167] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0168] Based on a preset mapping table, respectively obtaining the road surface smoothness assessment grades corresponding to the root mean square value, the variance value, and the peak-to-peak value;

[0169] The first road surface roughness evaluation grade is calculated and determined based on a fourth preset function, wherein the fourth preset function includes:

[0170]

[0171] Among them, τ1, τ2, τ3 are weight coefficients, and the sum is 1, α represents the first road surface roughness evaluation level, μ Rms Indicates the road surface roughness evaluation grade corresponding to the root mean square value, Indicates the road surface roughness evaluation level corresponding to the variance value, μ Delta Indicates the road surface roughness assessment level corresponding to the peak-to-peak value.

[0172] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0173] In response to detecting that the absolute value of the difference between the first road surface roughness evaluation level and the preset road surface roughness evaluation level is greater than a fourth preset value, determining the first road surface roughness evaluation level as the target road surface roughness evaluation level;

[0174] In response to detecting that the absolute value of the difference between the first road surface roughness evaluation level and the preset road surface roughness evaluation level is less than or equal to a fourth preset value, the preset road surface roughness evaluation level is determined as the target road surface roughness evaluation level.

[0175] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0176] In response to detecting that the root mean square value is less than a first preset value, the variance value is less than a second preset value, and the peak-to-peak value is less than a third preset value within a target time period, the road surface roughness evaluation mechanism is exited.

[0177] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0178] S1: acquiring the wheel speed of the target vehicle, and preprocessing the wheel speed to obtain first characteristic data;

[0179] S2: Based on the first characteristic data, a preset function is used to calculate and obtain second characteristic data to determine whether to trigger a road surface roughness assessment mechanism;

[0180] S3: in response to detecting that the road surface roughness assessment mechanism is triggered, determining a first road surface roughness assessment level based on the second feature data;

[0181] S4: using the first road surface roughness assessment level to verify the preset road surface roughness assessment level, and determining the target road surface roughness assessment level according to the verification result, so as to achieve the assessment of the road surface roughness.

[0182] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0183] Obtaining the wheel speed of at least one wheel on the target vehicle;

[0184] Based on a low-pass filtering mechanism, the wheel speed is preprocessed to obtain a target data value;

[0185] The target data value is first-order derived to obtain the wheel acceleration of the target vehicle, and the wheel acceleration is the first characteristic data.

[0186] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0187] Defining the preset function to include at least a first preset function, a second preset function and a third preset function, the second characteristic data to include at least a root mean square value, a variance value and a peak-to-peak value, and based on the first characteristic data, using the preset function to calculate and obtain the second characteristic data includes:

[0188] Acquire first characteristic data within N operation cycles;

[0189] The root mean square value corresponding to the first characteristic data is calculated using the first preset function, where the first preset function includes:

[0190]

[0191] Among them, X Rms represents the RMS value, N represents the number of operating cycles, X i represents the wheel acceleration corresponding to the i-th operating cycle;

[0192] The variance value corresponding to the first feature data is calculated using the second preset function, where the second preset function includes:

[0193]

[0194] Among them, σ represents the variance value, μ represents the average value of wheel acceleration;

[0195] The peak-to-peak value corresponding to the first characteristic data is calculated using the third preset function, wherein the third preset function includes:

[0196] X Delta =X i max -X i min

[0197] Among them, X Delta represents the peak-to-peak value, X i max Indicates the maximum wheel acceleration, X i min Indicates the minimum wheel acceleration.

[0198] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0199] In response to detecting that the RMS value is greater than a first preset value for more than a preset number of times within N operating cycles, and / or the variance value is greater than a second preset value for more than a preset number of times, and / or the peak-to-peak value is greater than a third preset value for more than a preset number of times, the road surface roughness assessment mechanism is triggered.

[0200] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0201] Based on a preset mapping table, respectively obtaining the road surface smoothness assessment grades corresponding to the root mean square value, the variance value, and the peak-to-peak value;

[0202] The first road surface roughness evaluation grade is calculated and determined based on a fourth preset function, wherein the fourth preset function includes:

[0203]

[0204] Among them, τ1, τ2, τ3 are weight coefficients, and the sum is 1, α represents the first road surface roughness evaluation level, μ Rms Indicates the road surface roughness evaluation grade corresponding to the root mean square value, Indicates the road surface roughness evaluation level corresponding to the variance value, μ Delta Indicates the road surface roughness assessment level corresponding to the peak-to-peak value.

[0205] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0206] In response to detecting that the absolute value of the difference between the first road surface roughness evaluation level and the preset road surface roughness evaluation level is greater than a fourth preset value, determining the first road surface roughness evaluation level as the target road surface roughness evaluation level;

[0207] In response to detecting that the absolute value of the difference between the first road surface roughness evaluation level and the preset road surface roughness evaluation level is less than or equal to a fourth preset value, the preset road surface roughness evaluation level is determined as the target road surface roughness evaluation level.

[0208] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0209] In response to detecting that the root mean square value is less than a first preset value, the variance value is less than a second preset value, and the peak-to-peak value is less than a third preset value within a target time period, the road surface roughness evaluation mechanism is exited.

[0210] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0211] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0212] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A road surface roughness assessment method, characterized in that: The method comprises: Acquire the wheel speed of the target vehicle, and pre-process the wheel speed to obtain first characteristic data, where the first characteristic data is wheel acceleration; Based on the first characteristic data, a preset function is used to calculate and obtain second characteristic data to determine whether to trigger a road surface roughness evaluation mechanism, wherein the preset function at least includes a first preset function for calculating a root mean square value, a second preset function for calculating a variance value, and a third preset function for calculating a peak-to-peak value; In response to detecting that the road surface roughness assessment mechanism is triggered, determining a first road surface roughness assessment level based on the second feature data, including: Based on a preset mapping table, respectively obtaining the road surface smoothness assessment grade corresponding to the root mean square value, the variance value, and the peak-to-peak value; The first road surface roughness evaluation grade is calculated and determined based on a fourth preset function, wherein the fourth preset function includes: Among them, τ1, τ2, τ3 are weight coefficients, and the sum is 1, α represents the first road surface roughness evaluation level, μ Rms Indicates the road surface roughness evaluation grade corresponding to the root mean square value, Indicates the road surface roughness evaluation level corresponding to the variance value, μ Delta Indicates the road surface roughness assessment level corresponding to the peak-to-peak value; The preset road surface flatness assessment level is verified using the first road surface flatness assessment level, and a target road surface flatness assessment level is determined based on the verification result to achieve the assessment of the road surface flatness.

2. The road surface roughness evaluation method according to claim 1, characterized in that: The step of acquiring the wheel speed of the target vehicle and preprocessing the wheel speed to obtain the first characteristic data includes: Obtaining the wheel speed of at least one wheel on the target vehicle; Based on a low-pass filtering mechanism, the wheel speed is preprocessed to obtain a target data value; The target data value is first-order derived to obtain the wheel acceleration of the target vehicle, and the wheel acceleration is the first characteristic data.

3. The road surface roughness evaluation method according to claim 1, characterized in that: The second characteristic data at least includes a root mean square value, a variance value, and a peak-to-peak value. Based on the first characteristic data, obtaining the second characteristic data by calculating using a preset function includes: Acquire first characteristic data within N operation cycles; The root mean square value corresponding to the first characteristic data is calculated using the first preset function, where the first preset function includes: Among them, X Rms represents the RMS value, N represents the number of operating cycles, X i represents the wheel acceleration corresponding to the i-th operating cycle; The variance value corresponding to the first feature data is calculated using the second preset function, where the second preset function includes: Among them, σ represents the variance value, and μ represents the average value of wheel acceleration; The peak-to-peak value corresponding to the first characteristic data is calculated using the third preset function, wherein the third preset function includes: X Delta =X imax -X imin Among them, X Delta represents the peak-to-peak value, X imax Indicates the maximum wheel acceleration, X imin Indicates the minimum wheel acceleration.

4. The road surface flatness evaluation method according to claim 3, characterized in that: Based on the second characteristic data, determining whether to trigger a road surface roughness assessment mechanism includes: In response to detecting that the RMS value is greater than a first preset value for more than a preset number of times within N operating cycles, and / or the variance value is greater than a second preset value for more than a preset number of times, and / or the peak-to-peak value is greater than a third preset value for more than a preset number of times, the road surface roughness assessment mechanism is triggered.

5. The road surface roughness evaluation method according to claim 1, characterized in that: Verifying a preset road surface roughness evaluation level using the first road surface roughness evaluation level, and determining a target road surface roughness evaluation level according to the verification result includes: In response to detecting that the absolute value of the difference between the first road surface roughness evaluation level and the preset road surface roughness evaluation level is greater than a fourth preset value, determining the first road surface roughness evaluation level as the target road surface roughness evaluation level; In response to detecting that the absolute value of the difference between the first road surface roughness evaluation level and the preset road surface roughness evaluation level is less than or equal to a fourth preset value, the preset road surface roughness evaluation level is determined as the target road surface roughness evaluation level.

6. The road surface roughness evaluation method according to claim 3, characterized in that: The method further comprises: In response to detecting that the root mean square value is less than a first preset value, the variance value is less than a second preset value, and the peak-to-peak value is less than a third preset value within a target time period, the road surface roughness evaluation mechanism is exited.

7. A road surface roughness assessment device for implementing the road surface roughness assessment method according to any one of claims 1 to 6, characterized in that: The device comprises: A preprocessing module, used for acquiring the wheel speed of the target vehicle and preprocessing the wheel speed to obtain first characteristic data; A first determination module, configured to obtain second characteristic data by calculating using a preset function based on the first characteristic data, so as to determine whether to trigger a road surface roughness assessment mechanism; a second determination module, configured to determine a first road surface roughness assessment level based on the second feature data in response to detecting that the road surface roughness assessment mechanism is triggered; The evaluation result verification module is used to use the first road surface flatness evaluation level to verify the preset road surface flatness evaluation level, determine the target road surface flatness evaluation level according to the verification result, and realize the evaluation of road surface flatness.

8. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

Images