Parameter correction method, system and equipment of vehicle pedal and medium

Abstract

The invention provides a parameter correction method, system and device for a vehicle pedal and a medium, and belongs to the field of vehicle comfort level testing. The method comprises the following steps: acquiring an initial parameter and a man-machine test parameter of a vehicle pedal; determining a user heel drop point position full score value and a corresponding target user heel drop point position during multi-user testing, and determining an included angle between a tangent line of the full score value and the pedal center point and a horizontal line of the target user heel drop point position as a target pedal horizontal included angle; determining each foot length size distribution weight according to a height database; and based on the multi-user test comfort feedback score and the distribution weight, calculating a correction guidance parameter of the vehicle pedal to correct the initial height. By simplifying man-machine analysis, extracting several indexes with the highest comfort degree of association and utilizing test results of multiple testers, data is more persuasive, a method of feeding back multiple subjective opinions is used, the data is more accurate, the feedback is more accurate by program calculation, and the concept of weight is introduced, so that the method is more scientific.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle comfort testing, and particularly relates to a method, system, device and medium for correcting parameters of a vehicle pedal. Background Art

[0002] With the continuous development of technologies such as big data and artificial intelligence, it is expected to realize intelligent evaluation and correction of pedal comfort based on actual operation data in the future, so as to promote the continuous optimization of the humanized design of commercial vehicle cabs and improve the comfort of drivers and driving safety.

[0003] Currently, the evaluation and correction process of the comfort of commercial vehicle cab pedals generally relies on the development of a man-machine verification bench. This traditional method mainly adjusts the distance of the pedal in different axial directions through the subjective feelings of test riders until a generally recognized comfortable axial spacing configuration is reached. However, this method not only takes a long time, but also highly depends on individual subjective judgments and lacks a unified and objective evaluation standard.

[0004] More importantly, this evaluation method requires a large amount of capital investment in advance for the development of molds such as man-machine verification benches, increasing the R & D and production costs. At the same time, since the evaluation process cannot directly use actual operation data for scientific calculation, the accuracy and reliability of the evaluation results also face challenges.

[0005] Therefore, it is urgent to explore a more efficient and scientific evaluation and correction method. An ideal method should be able to quantitatively evaluate pedal comfort based on actual operation data feedback and by using advanced data analysis techniques. This can not only avoid high mold development costs, but also ensure the objectivity and accuracy of the evaluation results. Summary of the Invention

[0006] The purpose of the embodiments of the present invention is to provide a method, system, device and medium for correcting parameters of a vehicle pedal to solve all or at least part of the technical problems existing in the above-mentioned prior art.

[0007] In a first aspect, an embodiment of the present application provides a method for correcting parameters of a vehicle pedal, including: Obtaining the initial parameters of the vehicle pedal and the man-machine test parameters, wherein the initial parameters of the vehicle pedal include the initial pedal height and the pedal horizontal angle, and the man-machine test parameters include the leg angle between the user's thigh and calf, the position of the user's heel landing point, the pedal test height, the pedal test horizontal angle, and the comfort feedback score of multi-user tests; Determine the dense point range of the heel landing position during multi-user testing, and determine the full score value of the user's heel landing position and the corresponding target user's heel landing position through the mode of the dense points. Then, determine the included angle between the tangent line of the target user's heel landing position and the center point of the pedal and the horizontal line of the target user's heel landing position as the target pedal horizontal angle; According to the height database, determine the proportional relationship between the height of the population and the foot length size, and determine the distribution weight of each foot length size based on this proportional relationship; Calculate the correction guidance parameters of the vehicle pedal based on the comfort feedback scores of multi-user testing and the distribution weights of each foot length size, and correct the initial height of the vehicle pedal based on the correction guidance parameters.

[0008] Optionally, obtain the initial parameters and human-machine test parameters of the vehicle pedal, including: Obtain the point cloud data of the vehicle cab, where the point cloud data includes the initial point cloud data when the vehicle cab is empty, the user posture point cloud data during human-machine testing, and the cab point cloud data; Perform rasterization processing on the initial point cloud data, filter out the point cloud data of the vehicle pedal and the point cloud data of the steering wheel in the initial point cloud data, determine the origin of the three-dimensional coordinate system based on the point cloud data of the vehicle pedal, determine the Z-axis of the three-dimensional coordinate system based on the connection line between the point cloud data of the vehicle pedal and the point cloud data of the steering wheel, determine the X-axis based on the horizontal projection of the Z-axis, and determine the Y-axis according to the right-hand rule; Fit the pedal plane equation by the least squares method, calculate the included angle between the normal vector and the horizontal plane, and determine the difference between the included angle between the normal vector and the horizontal plane and 90° as the horizontal angle of the pedal, where the normal vector is the coefficient of the plane equation; Perform rasterization processing on the user posture point cloud data, determine the coordinates of the user's hip joint, knee joint, and ankle joint, determine the thigh vector based on the coordinates of the user's hip joint and knee joint, determine the calf vector based on the coordinates of the knee joint and ankle joint, and determine the included angle between the user's thigh and calf according to the thigh vector and the calf vector; Filter out the pedal surface point set in the cab point cloud data, and determine the coordinate of the heel landing position through contact point detection; Collect the included angle between the thighs and calves of different users, the coordinates of the heel landing positions, the pedal height, the included angle, and the comfort feedback scores of user testing.

[0009] Optionally, perform rasterization processing on the initial point cloud data, and filter out the point cloud data of the vehicle pedal and the point cloud data of the steering wheel in the initial point cloud data, including: Perform rasterization processing on the initial point cloud data, and respectively obtain the first connected region of the point cloud data of the vehicle pedal in the grid and the second connected region of the point cloud data of the steering wheel in the grid; Traverse the first connected region and the second connected region, calculate the average elevation of the point cloud data in the first connected region and the second connected region, and use the average elevation as the first threshold and the second threshold respectively. Determine whether the elevation of the point cloud data in the first connected region is greater than the first threshold, and whether the elevation of the point cloud data in the second connected region is greater than the second threshold. If so, delete the point cloud data to obtain the point cloud data belonging to the vehicle pedal and the point cloud data of the steering wheel in the initial point cloud data.

[0010] Optionally, calculate the angle between the normal vector and the horizontal plane according to the following formula:

[0011] In the formula, =(0, 0, 1), the normal vector =(a, b, c).

[0012] Optionally, the determination of the normal vector includes: Assume there are N pedal plane point cloud data ( ), construct the objective function: ; For , , Take the partial derivatives of the parameters and set the derivatives to zero to obtain the corresponding system of equations, and organize the system of equations into matrix form; Traverse all the pedal plane point cloud data, calculate the matrix elements, and obtain the corresponding values of the parameters , , by matrix inversion; Based on the corresponding values of the parameters , , , determine the normal vector .

[0013] Optionally, calculate the corrected guidance parameters of the vehicle pedal according to the following formula based on the comfort feedback score of multi-user testing and the weight distribution of each foot length size:

[0014] In the formula, is the height of the corrected vehicle pedal, represents the initial height of the vehicle pedal, represents the comfort feedback score level of multi-user testing, represents the weight distribution of each foot length size, represents the number of test users.

[0015] Optionally, calculate the weight distribution of each foot length size according to the following formula:

[0016] In the formula, a value representing the proportional relationship between the height of the i-th population and the foot length size, and n represents the total number of proportional relationships between the population height and the foot length size, represents the sum of all proportional values.

[0017] In a second aspect, an embodiment of the present application further provides a parameter correction system for a vehicle pedal, including: An acquisition unit for acquiring the initial parameters of the vehicle pedal and the human-machine test parameters, wherein the initial parameters of the vehicle pedal include the initial pedal height and the pedal horizontal angle, and the human-machine test parameters include the leg angle between the user's thigh and calf, the position of the user's heel landing point, the pedal test height, the pedal test horizontal angle, and the comfort feedback score of multi-user tests; A determination unit for determining the dense point range of the heel landing point position during multi-user tests, and determining the full score value of the user's heel landing point position and the corresponding target user's heel landing point position through the dense point mode, and determining the angle between the tangent of the target user's heel landing point position and the pedal center point and the horizontal line of the target user's heel landing point position as the target pedal horizontal angle; An allocation unit for determining the proportional relationship between the population height and the foot length size according to the height database, and determining the allocation weight of each foot length size based on this proportional relationship; A correction unit for calculating the correction guidance parameters of the vehicle pedal based on the comfort feedback score of multi-user tests and the allocation weight of each foot length size, and correcting the initial height of the vehicle pedal based on the correction guidance parameters.

[0018] In a third aspect, an embodiment of the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the parameter correction method for the vehicle pedal described above are implemented.

[0019] In a fourth aspect, an embodiment of the present application further provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the parameter correction method for the vehicle pedal described above are implemented.

[0020] It can be seen from the above technical solutions that the present invention has the following advantages: In the parameter correction method, system, device, and medium for the vehicle pedal provided by the present application, by simplifying the human-machine analysis, extracting several indicators with the highest correlation to comfort, using the test results of multiple testers, the data is more persuasive, and using the method of multiple subjective opinion feedbacks is more accurate, and the feedback for program calculation is more accurate. Moreover, the concept of weight is introduced, and the method is more scientific. Brief Description of the Drawings

[0021] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for the description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0022] Figure 1 It is a flowchart of a method for correcting parameters of a vehicle pedal provided by an embodiment of the present invention; Figure 2 It is a flowchart of a process for obtaining initial parameters and human-machine test parameters of a vehicle pedal provided by an embodiment of the present invention; Figure 3 It is a schematic diagram of a human-machine test provided by an embodiment of the present invention; Figure 4 It is a schematic structural diagram of a parameter correction system for a vehicle pedal provided by an embodiment of the present invention; Figure 5 It is a schematic hardware structure diagram of an electronic device provided by an embodiment of the present invention. Detailed Description of the Embodiments

[0023] In the following detailed description, various embodiments of the present disclosure will be described more fully. The present disclosure may have various embodiments and adjustments and changes can be made therein. However, it should be understood that there is no intention to limit the various embodiments of the present disclosure to the specific embodiments disclosed herein. Instead, the present disclosure should be understood to cover all adjustments, equivalents, and / or alternative solutions that fall within the spirit and scope of the various embodiments of the present disclosure.

[0024] In the following text, the term "comprising" or "may comprise" that may be used in various embodiments of the present disclosure indicates the presence of the disclosed functions or operations, and does not limit the addition of one or more functions or operations. In addition, as used in various embodiments of the present disclosure, the terms "comprising", "having" and their cognates are only intended to indicate a specific feature, number, step, operation or combination of the foregoing items, and should not be construed as precluding the existence or addition of one or more other features, numbers, steps, operations or combinations of the foregoing items.

[0025] In various embodiments of the present disclosure, the expression "or" or "at least one of A or / and B" includes any combination or all combinations of the recited words. For example, the expression "A or B" or "at least one of A or / and B" may include A, may include B, or may include both A and B.

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts fall within the protection scope of the present invention.

[0027] Refer to Figure 1 The figure shows a flowchart of a method for parameter correction of a vehicle pedal in a specific embodiment, including the following execution steps: Step 100: Obtain the initial parameters of the vehicle pedal and the human-machine test parameters. Among them, the initial parameters of the vehicle pedal include the initial pedal height and the pedal horizontal angle, and the human-machine test parameters include the leg angle between the user's thigh and calf, the position of the user's heel landing point, the pedal test height, the pedal test horizontal angle, and the comfort feedback score of multi-user tests.

[0028] It should be understood that the vehicle pedal includes an accelerator, a clutch, and a brake pedal.

[0029] Specifically, refer to Figure 2 As shown in the figure, when performing step 100, the following steps can be specifically executed: S1000: Obtain the point cloud data of the vehicle cab, where the point cloud data includes the initial point cloud data when the vehicle cab is empty, the user posture point cloud data during the human-machine test, and the cab point cloud data.

[0030] Exemplarily, the point cloud data of the vehicle cab can be obtained by installing a lidar device at a suitable position outside or inside the vehicle cab to ensure that the internal space of the cab can be comprehensively scanned. Start the lidar device and perform a full-range scan of the cab. The lidar measures the distance between the object and the sensor by emitting laser pulses and receiving the reflected signals, thereby generating three-dimensional point cloud data. It should be noted that during the scanning process, adjust the scanning angle and speed of the lidar to ensure the uniformity and integrity of data collection.

[0031] S1001: Perform rasterization processing on the initial point cloud data, screen out the point cloud data of the vehicle pedal and the point cloud data of the steering wheel in the initial point cloud data, determine the origin of the three-dimensional coordinate system based on the point cloud data of the vehicle pedal, determine the Z-axis of the three-dimensional coordinate system based on the connection line between the point cloud data of the vehicle pedal and the point cloud data of the steering wheel, determine the X-axis based on the horizontal projection of the Z-axis, and determine the Y-axis according to the right-hand rule.

[0032] Specifically, rasterize the initial point cloud data, and filter out the point cloud data of the vehicle pedal and the point cloud data of the steering wheel in the initial point cloud data, including: rasterize the initial point cloud data, and respectively obtain the first connected region of the point cloud data belonging to the vehicle pedal in the grid and the second connected region of the point cloud data of the steering wheel in the grid; traverse the first connected region and the second connected region, calculate the average elevation of the point cloud data in the first connected region and the second connected region, and use the average elevation as the first threshold and the second threshold respectively, and judge whether the elevation of the point cloud data in the first connected region is greater than the first threshold, and whether the elevation of the point cloud data in the second connected region is greater than the second threshold. If so, delete the point cloud data to obtain the point cloud data of the vehicle pedal and the point cloud data of the steering wheel in the initial point cloud data.

[0033] S1002: Fit the pedal plane equation by the least squares method, calculate the angle between the normal vector and the horizontal plane, and determine the horizontal angle of the pedal as the difference between the angle between the normal vector and the horizontal plane and 90°, where the normal vector is the coefficient of the plane equation.

[0034] Specifically, calculate the angle between the normal vector and the horizontal plane according to the following formula:

[0035] In the formula, =(0, 0, 1), normal vector =(a, b, c).

[0036] In some embodiments, the determination of the normal vector includes the following steps: Assume there are N pedal plane point cloud data ( ), and construct the objective function: ; For , , parameters, take the partial derivatives and set the derivatives to zero to obtain the corresponding system of equations, and organize the system of equations into matrix form; Traverse all the pedal plane point cloud data, calculate the matrix elements, and obtain the corresponding values of the parameters , , by matrix inversion; Based on the corresponding values of the parameters , , , determine the normal vector .

[0037] S1003: Perform rasterization processing on the user's pose point cloud data to determine the coordinates of the user's hip joint, knee joint, and ankle joint. Based on the coordinates of the user's hip joint and knee joint, determine the thigh vector. Based on the coordinates of the knee joint and ankle joint, determine the calf vector. And determine the angle between the user's thigh and calf according to the thigh vector and the calf vector.

[0038] S1004: Screen out the pedal surface point set from the cab point cloud data, and determine the coordinate of the shoe heel landing position through contact point detection.

[0039] Specifically, based on the overall layout of the cab and the spatial distribution of the point cloud data, preliminarily determine the approximate position of the pedal. Define the pedal area by setting a spatial range or geometric shape (such as a rectangle, polygon, etc.). Within the determined pedal area, screen according to the height information (Z-axis coordinate) of the point cloud data. Set a height threshold range, and regard the points within this range as the point set of the pedal surface. The screening result can be further refined through methods such as statistical analysis or morphological processing to improve accuracy.

[0040] S1005: Collect the angle between the thighs of different users, the coordinate of the shoe heel landing position, the pedal height, the angle, and the user's test comfort feedback score.

[0041] Step 101: Determine the dense point range of the shoe heel landing position during multi-user testing, and determine the full score value of the user's shoe heel landing position and the corresponding target user's shoe heel landing position through the mode of the dense points. And determine the angle between the tangent of the line connecting the target user's shoe heel landing position and the pedal center point and the horizontal line of the target user's shoe heel landing position as the target pedal horizontal angle.

[0042] Step 102: According to the height database, determine the proportional relationship between the height of the population and the foot length size, and determine the distribution weight of each foot length size based on this proportional relationship.

[0043] In some embodiments, through the global human height database, count relevant data and calculate the proportion of each height population, and then convert the relationship between height and foot length size. The population proportion is shown in Table 1 below: Table 1 Population proportion

[0044] According to the proportion of the five sizes in Table 1, use the "proportion allocation method" to calculate the allocated weights, as shown in Table 2 specifically. The basic principle of the proportion allocation method is to divide the value of each part by the sum of the values of all parts to determine the weight or proportion of each part.

[0045] Table 2 Allocated weights

[0046] Step 103: Calculate the correction guidance parameters of the vehicle pedal based on the comfort feedback scores of multi-user tests and the weight distribution of each foot length size, and correct the initial height of the vehicle pedal based on the correction guidance parameters.

[0047] Specifically, calculate the correction guidance parameters of the vehicle pedal based on the comfort feedback scores of multi-user tests and the weight distribution of each foot length size according to the following formula:

[0048] In the formula, is the height of the corrected vehicle pedal, represents the initial height of the vehicle pedal, represents the comfort feedback score level of multi-user tests, represents the weight distribution of each foot length size, represents the number of test users.

[0049] In a specific embodiment, calculate the weight distribution of each foot length size according to the following formula:

[0050] In the formula, represents the value of the ratio relationship between the height and foot length size of the i-th population, n represents the total number of ratio relationships between population height and foot length size, represents the sum of all ratio values.

[0051] In a specific embodiment, referring to Figure 3 as shown, the most critical man-machine parameters for comfort are h, α, and x. The reasons are as follows: According to the actual tests of multiple people with different heights, each person adjusts the seat to the most comfortable position, and the a3 included angle value basically does not change much. The landing points of the front soles of the test personnel on the pedal are basically the same, but due to the difference in shoe sizes, the landing point x positions vary greatly, so the horizontal included angle α of each person is also different. And the designed height of h directly affects α, so the key man-machine parameters are h, α, and x. The following is the design of the scheme for calculating the optimal values of the parameters.

[0052] 1. Determine the optimal x value: Conduct tests with multiple testers, record the dense point range of the most frequent heel landing position x, and find the mode of the densest points, which is the full score value of x (10 points). 2. Determine the optimal α value: Draw a tangent line from the calculated full score point of x to the center point of the pedal, and the horizontal angle α formed is the value corresponding to the evaluation score of 10 points. 3. Determine the optimal value of the design height h: According to the feedback from the testers on their feelings about h, create a feedback record form for the suggestions of each person to raise or lower. Input the values in our record form into our Python calculation program. The program will fine-tune the height h according to the weight of the corresponding shoe size, and finally calculate the final value of h corresponding to the evaluation score of 10 points.

[0053] The specific implementation plan is as follows: 1. Record the original test parameters of the vehicle (such as the original design height: pedal height h0 = 122, horizontal angle α0 = 43); 2. Multiple testers conduct in-vehicle experiences to feel the comfort of the pedal. The test process is as follows: 2.1 Tester 1 (foot size 41) adjusts the seat position until the angle between the legs feels the most comfortable; 2.2 Tester 1 (foot size 41) places the front sole of the foot on the center point of the pedal; 2.3 Record the position of the heel landing x of Tester 1 (foot size 41), such as 124 mm; 2.4 Record the horizontal angle α formed by the tangent line between the heel landing x of Tester 1 (foot size 41) and the center point of the pedal, such as 42°; 2.5 Ask Tester 1 (foot size 41) for feedback, and feedback on the improvement direction of the design height h, such as choosing a slight reduction.

[0054] 2.6 Replace the tester and repeat this process (covering foot sizes 37 / 39 / 41 / 43 / 45, with no less than 3 people for each foot size); Record the feedback record form 3. The feedback level is divided into four grades: slight (1 to 2 mm), medium (3 to 5 mm), slightly large (5 to 7 mm), large (>7 mm).

[0055] Table 3 Tester Feedback Record

[0056] After all testers complete the above process, calculate the mode of the densest points of the x points, which is the x value corresponding to the full score of 10 points, and the corresponding angle α is the angle value corresponding to the full score of 10 points. For example: the optimal value x = 125 mm, α = 41°.

[0057] 4. Calculate the optimal value h. Input the values in the record table into the Python calculation program. The program will fine-tune the height h according to the weights of its corresponding size. The adjusted h is the height value with a full score of 10 points. For example, calculate the optimal value h as 122 - 0.56 + 0.384 + 0.384 - 0.351 = 121.857. Calculate the correction guidance parameters of the pedal (calculated by the Python program). Among them, the weights set in Python corresponding to the sizes are shown in Table 4, and the weights set in Python corresponding to the feedback levels are shown in Table 5. The Python calculation program is as follows: import pandas as pd from collections import Counter # Define the weight table size_weights = { 41: 0.297, 43: 0.241, 39: 0.215, 45: 0.128, 37: 0.117 } # Feedback level corresponding values feedback_values = { 'Slight': 2, 'Medium': 5, 'Slightly large': 7, 'Large': 10, 'None': 0 } # Initialize the data container data = [] # Collect testers' data while True: try: print('Press the Enter key or enter non-numeric characters to end the input') size = int(input('Please enter the size of the tester (37 / 39 / 41 / 43 / 45): ')) x_point = float(input('Please enter the measured x value of the tester's heel landing point: ')) alpha_value = float(input('Please enter the measured foot angle α of the tester: ')) opinion = input('Please enter the feedback from the tester (raise = + / lower = - / keep = 0):') opinion_grade = input('Please enter the feedback level from the tester (slight / moderate / slightly large / large / none):') # Convert the opinion to a numerical value if opinion == '+': opinion_num = feedback_values[opinion_grade] elif opinion == '-': opinion_num = -feedback_values[opinion_grade] else:# Remain unchanged opinion_num = 0 data.append({ 'Size': size, 'Landing point x': x_point, 'α': alpha_value, 'Feedback': opinion, 'Feedback level': opinion_grade, 'Feedback value': opinion_num }) except ValueError: print("Input ended.") break # Convert the data into a DataFrame df = pd.DataFrame(data) # Calculate the weighted corrected opinion for each tester df['Weighted corrected opinion'] = df.apply(lambda row: row['Feedback value'] * size_weights.get(row['Size'], 0), axis = 1) # Statistically find the most concentrated point (mode) of the landing point x most_common_x, _ = Counter(df['Landing point x']).most_common(1)[0] alpha_at_most_common_x = df[df['Landing Point x'] == most_common_x]['α'].mode().iloc[0] if not df df['Landing Point x'] == most_common_x].empty else None # Initial value alpha_0 = 43 h_0 = 122 # Calculate the optimal height h h_adjustment = df['Weight Correction Opinion'].sum() best_h = h_0 + h_adjustment # Output result print(f"High-frequency point of landing point: {most_common_x}mm") print(f"Corresponding α value: {alpha_at_most_common_x}°") print(f"Calculated optimal value h: {best_h:.3f}mm") # Print the data table with correction opinions print("\nFinal data table:") print(df[['Size', 'Landing Point x', 'α', 'Feedback', 'Feedback Level', 'Weight Correction Opinion']])

[0058] Table 4 Weight settings in Python

[0059] Table 5 Weight settings in Python corresponding to feedback levels

[0060] Finally, multiply the weight corresponding to the person's size by the feedback value to calculate the correction value. Finally, the corrected height for full marks of pedal comfort is shown in Table 6, e.g., 121.772cm.

[0061] Table 6 Corrected height for full marks of pedal comfort

[0062] In this embodiment, by simplifying the human-machine analysis, several indicators with the highest correlation to comfort are extracted. Using the test results of multiple testers makes the data more persuasive. And with the method of multiple subjective opinion feedbacks, it is more accurate, and the feedback for program calculation is more accurate. Moreover, by introducing the concept of weight, the method is more scientific.

[0063] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not imply the order of execution. The order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0064] As Figure 4 shown, the following is an embodiment of a parameter correction system for a vehicle pedal provided by an embodiment of the present disclosure. It belongs to the same inventive concept as the parameter correction method for the vehicle pedal in the above embodiments. Details not described in detail in the embodiment of the parameter correction system for the vehicle pedal can refer to the embodiment of the parameter correction method for the vehicle pedal.

[0065] An acquisition unit, configured to acquire the initial parameters of the vehicle pedal and the human-machine test parameters. Among them, the initial parameters of the vehicle pedal include the initial pedal height and the pedal horizontal angle, and the human-machine test parameters include the leg angle between the user's thigh and calf, the position of the user's heel landing point, the test height of the pedal, the test horizontal angle of the pedal, and the comfort feedback score of multi-user tests. A determination unit, configured to determine the dense point range of the heel landing point position during multi-user tests, and determine the full score value of the user's heel landing point position and the corresponding target user's heel landing point position through the dense point mode, and determine the angle between the tangent line of the target user's heel landing point position and the pedal center point and the horizontal line of the target user's heel landing point position as the target pedal horizontal angle. An allocation unit, configured to determine the proportional relationship between the population height and the foot length size according to the height database, and determine the allocation weight of each foot length size based on this proportional relationship. A correction unit, configured to calculate the correction guidance parameters of the vehicle pedal based on the comfort feedback score of multi-user tests and the allocation weights of each foot length size, and correct the initial height of the vehicle pedal based on the correction guidance parameters.

[0066] Figure 5 It is a schematic hardware structure diagram of an electronic device for implementing various embodiments of the present invention.

[0067] The parameter correction method for the vehicle pedal provided by the embodiment of the present application can be applied to an electronic device. Those skilled in the art can understand that the structure of the electronic device involved in the embodiment of the present invention does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements. In the embodiment of the present invention, the electronic device includes, but is not limited to, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital processor, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiment of the present application described herein and / or claimed.

[0068] The electronic device may include a processor, an external memory interface, an internal memory, a universal serial bus (USB) interface, a charging management module, a power management module, a battery, a wireless communication module, an audio module, a speaker, a microphone, a sensor module, a key, a camera, a display screen, and a SIM card interface, etc.

[0069] It can be understood that the structure schematically shown in the embodiment of the present application does not constitute a specific limitation on the electronic device. In other embodiments of the present application, the electronic device may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or have different component arrangements. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0070] The processor may include one or more processing units. For example, the processor may include a central processing unit (CPU), etc., an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Among them, different processing units may be independent devices or integrated in one or more processors.

[0071] Among them, the processor can be the nerve center and command center of the electronic device. The controller can generate operation control signals according to the instruction operation code and timing signal to complete the control of fetching and executing instructions.

[0072] A memory can also be set in the processor to store instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can save the instructions or data that the processor has just used or recycled. If the processor needs to use the instruction or data again, it can directly call it from this memory. This avoids repeated accesses, reduces the waiting time of the processor, and thus improves the system efficiency.

[0073] The external memory interface can be used to connect to an external memory card, such as a MicroSD card, to implement the storage capacity expansion of the electronic device. The external memory card communicates with the processor through the external memory interface to achieve the data storage function. For example, files such as music and videos are saved in the external memory card.

[0074] The internal memory can be used to store computer-executable program codes, and the computer-executable program codes include instructions. The processor executes various functional applications and data processing of the electronic device by running the instructions stored in the internal memory. The internal memory can include a program storage area and a data storage area. The internal memory can include a high-speed random access memory and can also include non-volatile memories, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), etc.

[0075] The wireless communication function of the electronic device can be implemented through an antenna, a wireless communication module, a modulation and demodulation processor, a baseband processor, etc.

[0076] The wireless communication module can provide wireless communication solutions applied to the electronic device, including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared technology (IR), etc.

[0077] The electronic device can implement audio functions, etc. through an audio module, a speaker, a receiver, a microphone, a headphone interface, an application processor, etc.

[0078] An electronic device can implement a shooting function through an ISP, a camera, a video codec, a GPU, a display screen, an application processor, etc.

[0079] An electronic device can implement a display function through a GPU, a display screen, an application processor, etc.

[0080] The GPU is a microprocessor for image processing, connecting the display screen and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processor may include one or more GPUs that execute program instructions to generate or change display information.

[0081] The display screen is used to display images, videos, etc. The display screen includes a display panel.

[0082] In the storage medium provided by this application, there is a program product that can implement the parameter correction method of the vehicle pedal.

[0083] The parameter correction method of the vehicle pedal includes: obtaining the initial parameters of the vehicle pedal and the human-machine test parameters, where the initial parameters of the vehicle pedal include the initial pedal height and the pedal horizontal angle, and the human-machine test parameters include the leg angle between the user's thigh and calf, the position of the user's heel landing point, the pedal test height, the pedal test horizontal angle, and the multi-user test comfort feedback score; determining the dense point range of the heel landing point position during multi-user testing, and determining the full score value of the user's heel landing point position and the corresponding target user's heel landing point position through the dense point mode, and determining the angle between the tangent of the line connecting the target user's heel landing point position and the pedal center point and the horizontal line of the target user's heel landing point position as the target pedal horizontal angle; determining the proportional relationship between the population height and the foot length size according to the height database, and determining the weight distribution of each foot length size based on this proportional relationship; calculating the correction guidance parameters of the vehicle pedal based on the multi-user test comfort feedback score and the weight distribution of each foot length size, and correcting the initial height of the vehicle pedal based on the correction guidance parameters.

[0084] In some possible implementation manners, the subject name of the present disclosure, the parameter correction method and system of the vehicle pedal, can be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary embodiments of the present disclosure described in the "Exemplary Method" section of this specification.

[0085] The storage medium of the present disclosure may employ any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0086] The foregoing description of the disclosed embodiments enables those skilled in the art to practice or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for correcting parameters of a vehicle pedal, characterized in that: include: Obtaining initial parameters of the vehicle pedal and human-machine test parameters, wherein the initial parameters of the vehicle pedal include an initial height of the pedal and a horizontal angle of the pedal, and the human-machine test parameters include an inter-leg angle between a user's thigh and calf, a user's heel landing point, a pedal test height, a pedal test horizontal angle, and a multi-user test comfort feedback score; Determine the dense point range of the heel landing position during the multi-user test, and determine the full score of the user's heel landing position and the corresponding target user's heel landing position through the mode of the dense points, and determine the angle between the tangent line of the target user's heel landing position and the center point of the pedal and the horizontal line of the target user's heel landing position as the target pedal horizontal angle; According to the height database, determine the proportional relationship between the height of the group and the foot length, and determine the weight of each foot length based on the proportional relationship; Based on the comfort feedback scores of multiple users and the weights assigned to each foot length size, the revised guidance parameters of the vehicle pedal are calculated, and the initial height of the vehicle pedal is revised based on the revised guidance parameters.

2. The method for correcting the parameters of a vehicle pedal according to claim 1, characterized in that: Obtain the initial parameters of the vehicle pedal and the human-machine test parameters, including: Acquire point cloud data of the vehicle cab, wherein the point cloud data includes initial point cloud data when the vehicle cab is unmanned, user posture point cloud data during human-machine testing, and cab point cloud data; rasterizing the initial point cloud data, filtering out point cloud data belonging to a vehicle pedal and point cloud data of a steering wheel from the initial point cloud data, determining an origin of a three-dimensional coordinate system based on the point cloud data of the vehicle pedal, determining a Z axis of the three-dimensional coordinate system based on a line connecting the point cloud data of the vehicle pedal and the point cloud data of the steering wheel, determining an X axis by a horizontal projection of the Z axis, and determining a Y axis according to the right-hand rule; The pedal plane equation is fitted by the least square method, and the angle between the normal vector and the horizontal plane is calculated, and the difference between the angle between the normal vector and the horizontal plane and 90° is determined as the horizontal angle of the pedal, wherein the normal vector is the coefficient of the plane equation; rasterizing the user's posture point cloud data to determine the coordinates of the user's hip joint, knee joint, and ankle joint, determining a thigh vector based on the coordinates of the user's hip joint and knee joint, determining a calf vector based on the coordinates of the knee joint and ankle joint, and determining an inter-leg angle between the user's thigh and calf based on the thigh vector and the calf vector; The pedal surface point set is selected from the cab point cloud data, and the heel landing point coordinates are determined through contact point detection; Collect the leg angle, heel landing point coordinates, pedal height, angle and user test comfort feedback scores of different users.

3. The method for correcting the parameters of a vehicle pedal according to claim 2, characterized in that: The initial point cloud data is subjected to rasterization processing, and point cloud data belonging to the vehicle pedal and the point cloud data of the steering wheel in the initial point cloud data are screened out, including: Performing rasterization processing on the initial point cloud data to obtain a first connected region of the point cloud data belonging to the vehicle pedal in the grid and a second connected region of the point cloud data belonging to the steering wheel in the grid; The first connected area and the second connected area are traversed, the average elevation of the point cloud data in the first connected area and the second connected area are calculated, and the average elevation is used as the first threshold and the second threshold respectively, to determine whether the elevation of the point cloud data in the first connected area is greater than the first threshold, and whether the elevation of the point cloud data in the second connected area is greater than the second threshold. If so, the point cloud data is deleted to obtain the point cloud data of the vehicle pedal and the point cloud data of the steering wheel in the initial point cloud data.

4. The method for correcting the parameters of a vehicle pedal according to claim 2, characterized in that: The angle between the normal vector and the horizontal plane is calculated according to the following formula: In the formula, = (0,0,1), normal vector =(a,b,c).

5. The method for correcting the parameters of a vehicle pedal according to claim 4, characterized in that: The determination of the normal vector includes: Assume that there are N pedal plane point cloud data ( ), construct the objective function: ; against , , Take partial derivatives of the parameters and set the derivatives to zero to obtain the corresponding system of equations, and organize the system of equations into a matrix form; Traverse all pedal plane point cloud data, calculate matrix elements, and obtain parameters by matrix inversion , , The corresponding value; Based on parameters , , Corresponding value, determine the normal vector .

6. The method for correcting the parameters of a vehicle pedal according to claim 1, characterized in that: The modified guidance parameters for vehicle pedals are calculated based on the multi-user test comfort feedback score and the weights assigned to each foot length size according to the following formula: In the formula, is the height of the vehicle pedal after correction, represents the initial height of the vehicle pedal, Indicates the comfort feedback rating level of multi-user testing, Indicates the weight assigned to each foot length size, Indicates the number of test users.

7. The method for correcting the parameters of a vehicle pedal according to claim 6, characterized in that: The weights for each foot length are calculated using the following formula: In the formula, represents the value of the ratio between the height and foot length of the i-th group of people, n represents the total number of ratios between the height and foot length of the group of people, Represents the sum of all scale values.

8. A vehicle pedal parameter correction system, characterized in that: include: An acquisition unit, used to acquire initial parameters of a vehicle pedal and human-machine test parameters, wherein the initial parameters of the vehicle pedal include an initial height of the pedal and a horizontal angle of the pedal, and the human-machine test parameters include an inter-leg angle between a user's thigh and calf, a user's heel landing point, a pedal test height, a pedal test horizontal angle, and a multi-user test comfort feedback score; a determination unit, used to determine the dense point range of the heel landing position during the multi-user test, and determine the full score of the user's heel landing position and the corresponding target user's heel landing position through the mode of the dense points, and determine the angle between the tangent line of the target user's heel landing position and the pedal center point and the horizontal line of the target user's heel landing position as the target pedal horizontal angle; An allocation unit, used to determine the proportional relationship between the height of a group of people and the foot length size according to the height database, and determine the allocation weight of each foot length size based on the proportional relationship; The correction unit is used to calculate the correction guidance parameters of the vehicle pedal based on the comfort feedback score of the multi-user test and the weight assigned to each foot length size, and to correct the initial height of the vehicle pedal based on the correction guidance parameters.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the vehicle pedal parameter correction method according to any one of claims 1 to 7 are implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the vehicle pedal parameter correction method according to any one of claims 1 to 7 are implemented.

Images