A method, device, medium and equipment for constructing a vehicle collision universal waveform
By constructing a universal waveform for vehicle collisions and utilizing polynomial function weighted summation and mapping relationships, accurate collision waveform data is generated. This solves the problems of high cost and limited coverage of real vehicle testing, and supports efficient and low-cost automotive safety development and virtual evaluation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CATARC AUTOMOTIVE TEST CENT TIANJIN CO LTD
- Filing Date
- 2026-05-22
- Publication Date
- 2026-06-19
AI Technical Summary
In existing technologies, real-vehicle crash tests are costly, time-consuming, and have limited coverage, making it difficult to meet the needs of virtual vehicle safety assessments, resulting in high vehicle safety development costs and poor flexibility.
By acquiring test data from multiple vehicles under the same collision conditions, preprocessing the data, constructing a similarity function through a weighted summation of polynomial functions, calculating the weights, establishing a mapping relationship between vehicle parameters and the similarity function, and generating waveform data of the target vehicle under collision conditions.
It enables the efficient and low-cost generation of standardized collision waveforms that meet specific operating conditions, improving the reliability and flexibility of the data and supporting the implementation of virtual vehicle safety assessments.
Smart Images
Figure CN122241887A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of vehicle collision testing technology, specifically to a method, apparatus, medium, and equipment for constructing a general waveform for vehicle collisions. Background Technology
[0002] In the field of vehicle safety performance development and evaluation, the crash waveform is a key characteristic curve that describes the change of vehicle acceleration over time during a collision. It directly determines the triggering timing, intensity, and overall protection effect of the occupant restraint system, and is therefore a core input parameter for evaluating the vehicle's crash safety and the matching performance of the restraint system.
[0003] For a long time, the authoritative way to obtain real and reliable collision waveforms has been through real-vehicle crash tests. In this traditional model, whether it's mandatory testing for regulatory certification or verification testing for product development, expensive physical tests are required to measure and record vehicle collision response data. The obtained measured waveforms are then used as boundary conditions for computer simulation models (such as finite element models and multibody dynamics models) under corresponding conditions to predict and optimize vehicle crashworthiness and occupant protection effectiveness. This test-driven simulation development model ensures the authenticity and engineering credibility of the data to a certain extent, but its inherent shortcomings are becoming increasingly apparent: High costs and resource consumption: Real-world vehicle crash testing is inherently expensive, encompassing investments in various aspects such as test vehicle and trolley preparation, acceleration and guidance systems, high-speed cameras, data acquisition instruments, dummies and sensors, and test site maintenance. Multiple independent tests are often required for various conditions with different speeds, angles, overlap rates, and obstacle types, resulting in a very high proportion of testing costs in the overall crash safety development cost of a single vehicle model.
[0004] The process is lengthy and inflexible: from test design, prototype preparation, sensor installation and calibration, to formal test execution, data post-processing, and waveform validity verification, the entire process typically takes weeks to months. When vehicle design changes or new test conditions need to be addressed, the inability to quickly generate corresponding collision waveforms severely restricts the implementation of concurrent engineering and agile development models.
[0005] Limited coverage: Real-vehicle testing is constrained by physical boundaries, testing facility capabilities, and safety regulations, making it difficult to cover all potential collision scenarios. For example, collisions at extremely high speeds, collisions with complex angle combinations, collision combinations with vastly different vehicle weights and structures, and special scenarios involving road environment coupling (such as rollovers, pole impacts, and offset collisions) are all difficult to fully cover with a limited number of physical tests.
[0006] This poses a substantial constraint on virtual vehicle safety assessment: Safety evaluation systems, represented by third-party testing organizations, are gradually introducing virtual test conditions not clearly defined in traditional real-vehicle testing procedures. These conditions require companies to provide standardized collision waveforms as simulation inputs without corresponding physical test data. The economic and time costs of conducting real-vehicle tests specifically to obtain waveforms to meet these virtual assessment requirements are prohibitive for most companies. This contradiction severely hinders the effective implementation and widespread adoption of virtual vehicle safety assessment procedures, becoming a key technological bottleneck in the industry's current transition to comprehensive virtual certification.
[0007] Therefore, there is an urgent need for a method that can efficiently and cost-effectively generate standardized collision waveforms that meet the requirements of specific working conditions and has good generalization ability. Summary of the Invention
[0008] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide a method, apparatus, medium, and device for constructing a universal waveform for vehicle collisions.
[0009] According to one aspect of this application, a method for constructing a universal waveform for vehicle collisions is provided, comprising: acquiring test data of multiple vehicles under the same collision condition; wherein the test data includes vehicle parameters and waveform data; preprocessing the test data to obtain preprocessed data; constructing a similarity function of the waveform data in the preprocessed data by weighted summation of multiple polynomial functions; calculating the weight of the similarity function based on the waveform data in the preprocessed data and the corresponding similarity function; determining a mapping relationship between the vehicle parameters and the weight of the similarity function based on the weight of the similarity function and the vehicle parameters in the preprocessed data; and generating waveform data of the target vehicle under the collision condition based on the mapping relationship and the vehicle parameters of the target vehicle.
[0010] In one embodiment, the preprocessing of the test data to obtain preprocessed data includes: normalizing the vehicle parameters to obtain normalized vehicle parameters; and filtering the waveform data to obtain filtered waveform data.
[0011] In one embodiment, constructing a similarity function for the waveform data in the preprocessed data by weighted summation of multiple polynomial functions includes: the calculation formula for the similarity function is: ;in, The independent variable in the waveform data. For similarity functions, For the first A polynomial function, , For the first The weights of a polynomial function, The number of polynomial functions.
[0012] In one embodiment, calculating the weight of the similarity function based on the waveform data and the corresponding similarity function in the preprocessed data includes: finding the weight of the similarity function when the weighted squared error between the waveform data and the corresponding similarity function in the preprocessed data is minimized.
[0013] In one embodiment, the formula for calculating the weighted squared error is: ;in, For weighted squared error, For the first The weights of a polynomial function, For the waveform data, the first One independent variable, For the corresponding The waveform amplitude, For the first Each polynomial function corresponds to The function value.
[0014] In one embodiment, determining the mapping relationship between the vehicle parameters and the weights of the similarity function based on the weights of the similarity function and the vehicle parameters in the preprocessed data includes: determining the mapping relationship between the vehicle parameters and the weights of the similarity function based on the number of polynomial functions in the similarity function and their corresponding weights, and the vehicle parameters in the preprocessed data.
[0015] In one embodiment, the number of polynomial functions in the similarity function is determined by: calculating the correlation coefficient between similar functions containing different numbers of polynomial functions and the waveform data in the preprocessed data; and selecting the minimum number of correlation coefficients greater than a preset threshold as the number of polynomial functions in the similarity function.
[0016] According to another aspect of this application, a device for constructing a universal waveform for vehicle collisions is provided, comprising: a test data acquisition module for acquiring test data of multiple vehicles under the same collision condition; wherein the test data includes vehicle parameters and waveform data; a preprocessing module for preprocessing the test data to obtain preprocessed data; a similarity function construction module for constructing a similarity function of the waveform data in the preprocessed data by weighted summation of multiple polynomial functions; a similarity weight calculation module for calculating the weight of the similarity function based on the waveform data in the preprocessed data and the corresponding similarity function; a mapping relationship determination module for determining the mapping relationship between the vehicle parameters and the weight of the similarity function based on the weight of the similarity function and the vehicle parameters in the preprocessed data; and a waveform data generation module for generating waveform data of the target vehicle under the collision condition based on the mapping relationship and the vehicle parameters of the target vehicle.
[0017] According to another aspect of this application, a computer-readable storage medium is provided, the storage medium storing a computer program for performing any of the methods described above.
[0018] According to another aspect of this application, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; the processor being configured to perform any of the methods described above.
[0019] This application provides a method, apparatus, medium, and device for constructing a universal waveform for vehicle collisions. It acquires test data from multiple vehicles under the same collision conditions. The test data includes vehicle parameters and waveform data. The test data is preprocessed to obtain preprocessed data. A similarity function is constructed from the waveform data in the preprocessed data using a weighted summation of multiple polynomial functions. Based on the waveform data in the preprocessed data and the corresponding similarity function, the weights of the similarity functions are calculated. Based on the weights of the similarity functions and the vehicle parameters in the preprocessed data, a mapping relationship between the vehicle parameters and the weights of the similarity functions is determined. Based on the mapping relationship and the vehicle parameters of the target vehicle, waveform data of the target vehicle under the collision conditions is generated. By acquiring actual collision test data and preprocessing it to improve data reliability, and by using a weighted summation of polynomial functions to construct a similarity function for the waveform data, and calculating the weights of the similarity functions based on the actual waveform data, the mapping relationship between the vehicle parameters and the weights of the similarity functions is determined. This allows for the direct generation of waveform data based on different vehicle parameters, thereby efficiently obtaining universal and accurate collision waveform data, providing accurate waveform data for subsequent collision simulations. Attached Figure Description
[0020] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.
[0021] Figure 1 This is a flowchart illustrating a method for constructing a general waveform for vehicle collisions provided in an exemplary embodiment of this application.
[0022] Figure 2 This is a schematic diagram of the structure of a device for constructing a universal waveform for vehicle collision provided in an exemplary embodiment of this application.
[0023] Figure 3 This is a structural diagram of an electronic device provided in an exemplary embodiment of this application. Detailed Implementation
[0024] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.
[0025] Figure 1 This is a flowchart illustrating a method for constructing a general waveform for vehicle collisions provided in an exemplary embodiment of this application. Figure 1 As shown, the method for constructing the universal waveform for vehicle collisions includes the following steps: Step 110: Obtain test data for multiple vehicles under the same collision conditions.
[0026] The test data includes vehicle parameters and waveform data. This application collects test data from multiple vehicles under the same collision conditions. This test data includes vehicle power type (pure electric vehicle, hybrid electric vehicle, gasoline vehicle), vehicle curb weight, internal energy absorbed by the vehicle during the collision, and acceleration and velocity waveform data (translational acceleration in the direction of travel), etc.
[0027] Step 120: Preprocess the experimental data to obtain preprocessed data.
[0028] After obtaining test data from multiple vehicles under the same collision conditions, this application preprocesses the test data, removes invalid and interfering data, and normalizes the data format to improve the accuracy of the test data and the convenience of subsequent data processing.
[0029] Step 130: Construct a similarity function for the waveform data in the preprocessed data by weighted summation of multiple polynomial functions.
[0030] This application employs a weighted summation of multiple polynomial functions to construct a similarity function for the waveform data in the preprocessed data, thereby obtaining an approximate function for the waveform data and providing a function that can approximate the representation of other waveforms in the future.
[0031] Step 140: Calculate the weights of the similarity functions based on the waveform data and corresponding similarity functions in the preprocessed data.
[0032] This application calculates the weights of the similarity functions based on the waveform data and corresponding similarity functions in the preprocessed data, that is, obtains the weight of each polynomial in the similarity function, thereby improving the similarity between the approximation function and the waveform data.
[0033] Step 150: Based on the weights of the similarity function and the vehicle parameters in the preprocessed data, determine the mapping relationship between the vehicle parameters and the weights of the similarity function.
[0034] This application establishes a mapping relationship between vehicle parameters and the weights of similarity functions based on the weights of similarity functions and vehicle parameters in preprocessed data. That is, it uses the actual collected test data to obtain the similarity function of waveform data, and then constructs a mapping relationship (i.e., a prediction model) between vehicle parameters and similarity functions (corresponding weight information).
[0035] Step 160: Based on the mapping relationship and the vehicle parameters of the target vehicle, generate waveform data of the target vehicle under collision conditions.
[0036] This application directly generates waveform data of the target vehicle under collision conditions based on the mapping relationship between vehicle parameters and similarity functions, as well as the vehicle parameters of the target vehicle to be predicted, thereby obtaining waveform data applicable to various vehicles and providing relatively accurate waveform data for subsequent collision tests and inspections.
[0037] This application provides a method for constructing a universal waveform for vehicle collisions. The method involves acquiring test data from multiple vehicles under the same collision conditions. The test data includes vehicle parameters and waveform data. The test data is preprocessed to obtain preprocessed data. A similarity function is constructed from the waveform data in the preprocessed data using a weighted summation of multiple polynomial functions. Based on the waveform data in the preprocessed data and the corresponding similarity function, the weights of the similarity functions are calculated. Based on the weights of the similarity functions and the vehicle parameters in the preprocessed data, a mapping relationship between the vehicle parameters and the weights of the similarity functions is determined. Based on the mapping relationship and the vehicle parameters of the target vehicle, waveform data of the target vehicle under the collision conditions is generated. By acquiring actual collision test data and preprocessing it to improve data reliability, and by using a weighted summation of polynomial functions to construct a similarity function for the waveform data, and calculating the weights of the similarity functions based on the actual waveform data, the mapping relationship between the vehicle parameters and the weights of the similarity functions is determined. This allows for the direct generation of waveform data based on different vehicle parameters, thereby efficiently obtaining universal and accurate collision waveform data, providing accurate waveform data for subsequent collision simulations.
[0038] In one embodiment, step 120 can be implemented by: normalizing the vehicle parameters to obtain normalized vehicle parameters; and filtering the waveform data to obtain filtered waveform data.
[0039] This application obtains normalized vehicle parameters by normalizing the vehicle parameters. Specifically, data such as vehicle power type, vehicle curb weight, and internal energy absorbed by the vehicle during a collision are standardized. The specific processing method is as follows: first, for each continuous feature... x i Calculate its mean and standard deviation, where the formulas for calculating the mean and standard deviation are: , , The mean, Continuous features x i The number of possible values, Let the standard deviation be denoted as , and then the normalized data are calculated using the following formula: ,in, For the data before normalization, To normalize the data, this application performs normalization processing to standardize the vehicle parameters to the [-1,1] interval, eliminating differences in the dimensions of the data. Simultaneously, the waveform data (including velocity waveforms and acceleration waveforms, etc.) is filtered to obtain filtered waveform data, thereby improving data quality.
[0040] In one embodiment, step 130 can be specifically implemented as follows: the formula for calculating the similarity function is: ;in, The independent variable in the waveform data. For similarity functions, For the first A polynomial function, , For the first The weights of a polynomial function, The number of polynomial functions.
[0041] This application uses a weighted summation of multiple polynomial functions to obtain a similar function to the waveform function, that is, it uses polynomial functions to approximate the waveform function to obtain a similar function to the waveform function, wherein... express factorial, express of The first derivative, and due to the independent variable Within the interval [-1, 1], the above polynomial functions form an orthogonal function system, which can approximate any waveform function by weighted summation of multiple polynomial functions.
[0042] In one embodiment, step 140 can be implemented by finding the weight that minimizes the weighted squared error between the waveform data and the corresponding similar function in the preprocessed data, and using this weight as the weight of the similar function.
[0043] This application calculates the weighted squared error between the waveform data in the preprocessed data and the corresponding similarity function, and solves the problem of minimizing the weighted squared error to obtain the corresponding weight as the weight of the similarity function. That is, it solves the weight value that minimizes the weighted squared error between the waveform data in the preprocessed data and the corresponding similarity function.
[0044] In one embodiment, step 140 can be specifically implemented as follows: the formula for calculating the weighted squared error is: ;in, For weighted squared error, For the first The weights of a polynomial function, For the waveform data, the first One independent variable, For the corresponding The waveform amplitude, For the first Each polynomial function corresponds to The function value.
[0045] This application obtains the weights of the corresponding polynomial function by solving the optimization problem of minimizing the weighted squared error, thereby obtaining the similarity function that is most similar to the waveform data.
[0046] In one embodiment, step 150 can be implemented by determining the mapping relationship between vehicle parameters and the weights of similar functions based on the number of polynomial functions and their corresponding weights in the similarity function and the vehicle parameters in the preprocessed data.
[0047] This application determines the corresponding similarity function expression based on the number of polynomial functions and their corresponding weights in the similarity function, and constructs a mapping relationship (i.e., a prediction model) between the vehicle parameters and the weights of the similarity function by combining the vehicle parameters in the preprocessed data. Specifically, the prediction model of this application can be set as a multiple linear regression model: ; in, For the weight vector, For vehicle parameter matrix, For the regression coefficient vector, This is the error vector.
[0048] The regression coefficient vector of the above multiple linear regression model is solved by the least squares method to establish the mapping relationship between vehicle parameters and the weights of similarity functions.
[0049] After establishing the mapping relationship between vehicle parameters and the weights of similarity functions, this application can use validation data (including actual vehicle parameters and measured waveform data) to validate the prediction model. Specifically, the model can be evaluated from aspects such as channel, phase, amplitude, and slope to calculate the prediction accuracy. The specific calculation formula is as follows: ;in, These are evaluation values for four aspects: channel, phase, amplitude, and slope.
[0050] In one embodiment, the specific implementation of step 150 above may be as follows: the number of polynomial functions in the similarity function is determined by: calculating the correlation coefficient between the similarity function containing different numbers of polynomial functions and the waveform data in the preprocessed data; selecting the minimum number of similarity functions with a correlation coefficient greater than a preset threshold as the number of polynomial functions in the similarity function.
[0051] This application calculates the correlation coefficient between similar functions containing different numbers of polynomial functions and waveform data in preprocessed data, and selects the minimum number of similar functions with a correlation coefficient greater than a preset threshold (e.g., 0.99) as the number of polynomial functions in the similar function. Specifically, the formula for calculating the correlation coefficient is: ;in, For inclusion NThis application calculates the correlation coefficient between similar functions containing different numbers of polynomial functions and waveform data in preprocessed data. It then selects similar functions with correlation coefficients exceeding a preset threshold, choosing the one with the fewest polynomial functions as the similar function for the waveform data. In other words, it selects the similar function with the fewest polynomial functions that meets the correlation coefficient requirement.
[0052] Figure 2 This is a schematic diagram of the structure of a device for constructing a universal waveform for vehicle collisions provided in an exemplary embodiment of this application. Figure 2 As shown, the vehicle collision universal waveform construction device 20 includes: a test data acquisition module 21, used to acquire test data of multiple vehicles under the same collision condition; wherein, the test data includes vehicle parameters and waveform data; a preprocessing module 22, used to preprocess the test data to obtain preprocessed data; a similarity function construction module 23, used to construct a similarity function of the waveform data in the preprocessed data by weighted summation of multiple polynomial functions; a similarity weight calculation module 24, used to calculate the weight of the similarity function based on the waveform data in the preprocessed data and the corresponding similarity function; a mapping relationship determination module 25, used to determine the mapping relationship between the vehicle parameters and the weight of the similarity function based on the weight of the similarity function and the vehicle parameters in the preprocessed data; and a waveform data generation module 26, used to generate waveform data of the target vehicle under the collision condition based on the mapping relationship and the vehicle parameters of the target vehicle.
[0053] This application provides a device for constructing a universal waveform for vehicle collisions. A test data acquisition module 21 acquires test data from multiple vehicles under the same collision conditions; the test data includes vehicle parameters and waveform data. A preprocessing module 22 preprocesses the test data to obtain preprocessed data. A similarity function construction module 23 constructs a similarity function for the waveform data in the preprocessed data using a weighted sum of multiple polynomial functions. A similarity weight calculation module 24 calculates the weights of the similarity functions based on the waveform data in the preprocessed data and the corresponding similarity functions. A mapping relationship determination module 25 determines the vehicle parameters based on the weights of the similarity functions and the vehicle parameters in the preprocessed data. The mapping relationship between the similarity function weights and the target vehicle parameters is established. The waveform data generation module 26 generates waveform data of the target vehicle under collision conditions based on the mapping relationship and the vehicle parameters of the target vehicle. By acquiring actual collision test data and preprocessing the test data to improve the reliability of the data, and using polynomial function weighted summation to construct the similarity function of the waveform data, the weights of the similarity function are calculated based on the actual waveform data, and then the mapping relationship between the vehicle parameters and the weights of the similarity function is determined. This allows waveform data to be generated directly according to different vehicle parameters, thereby efficiently obtaining general and accurate collision waveform data and providing accurate waveform data for subsequent collision simulation.
[0054] In one embodiment, the preprocessing module 22 can be further configured to: normalize the vehicle parameters to obtain normalized vehicle parameters; and filter the waveform data to obtain filtered waveform data.
[0055] In one embodiment, the similarity function construction module 23 can be further configured such that the calculation formula for the similarity function is: ;in, The independent variable in the waveform data. For similarity functions, For the first A polynomial function, , For the first The weights of a polynomial function, The number of polynomial functions.
[0056] In one embodiment, the similarity weight calculation module 24 can be further configured to: calculate the weight of the similarity function when the weighted square error between the waveform data and the corresponding similarity function in the preprocessed data is minimized.
[0057] In one embodiment, the similarity weight calculation module 24 can be further configured such that the weighted squared error is calculated using the following formula: ;in, For weighted squared error, For the first The weights of a polynomial function, For the waveform data, the first One independent variable, For the corresponding The waveform amplitude, For the first Each polynomial function corresponds to The function value.
[0058] In one embodiment, the mapping relationship determination module 25 can be further configured to: determine the mapping relationship between vehicle parameters and the weights of similar functions based on the number of polynomial functions and their corresponding weights in the similarity function and the vehicle parameters in the preprocessed data.
[0059] In one embodiment, the mapping relationship determination module 25 can be further configured as follows: the number of polynomial functions in the similar function is determined by: calculating the correlation coefficient between the similar function containing different numbers of polynomial functions and the waveform data in the preprocessed data; and selecting the minimum number of similar functions whose correlation coefficient is greater than a preset threshold as the number of polynomial functions in the similar function.
[0060] Below, for reference Figure 3This application describes an electronic device according to embodiments thereof. The electronic device may be either or both of a first device and a second device, or a standalone device independent of them, which may communicate with the first device and the second device to receive acquired input signals from them.
[0061] Figure 3 A block diagram of an electronic device according to an embodiment of this application is illustrated.
[0062] like Figure 3 As shown, the electronic device 10 includes one or more processors 11 and memory 12.
[0063] The processor 11 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.
[0064] The memory 12 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 11 may execute the program instructions to implement the methods of the various embodiments of this application described above and / or other desired functions. Various contents such as input signals, signal components, and noise components may also be stored in the computer-readable storage medium.
[0065] In one example, the electronic device 10 may also include an input device 13 and an output device 14, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).
[0066] When the electronic device is a standalone device, the input device 13 can be a communication network connector for receiving the collected input signals from the first device and the second device.
[0067] In addition, the input device 13 may also include, for example, a keyboard, a mouse, etc.
[0068] The output device 14 can output various information to the outside, including determined distance information, direction information, etc. The output device 14 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.
[0069] Of course, for the sake of simplicity, Figure 3Only some of the components of the electronic device 10 relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the electronic device 10 may include any other suitable components depending on the specific application.
[0070] In addition to the methods and apparatus described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of this application described in the "Exemplary Methods" section above.
[0071] The computer program product can be written in any combination of one or more programming languages to perform the operations of the embodiments of this application. The programming languages include object-oriented programming languages such as Java and C++, as well as conventional procedural programming languages such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.
[0072] Furthermore, embodiments of this application may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of this application described in the "Exemplary Methods" section above.
[0073] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.
[0074] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.
[0075] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.
[0076] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.
[0077] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.
[0078] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.
Claims
1. A method of constructing a vehicle crash universal waveform, characterized by, include: Acquire test data of multiple vehicles under the same collision conditions; wherein, the test data includes vehicle parameters and waveform data; The experimental data are preprocessed to obtain preprocessed data; A similarity function for the waveform data in the preprocessed data is constructed by weighted summation of multiple polynomial functions. Based on the waveform data and corresponding similarity functions in the preprocessed data, the weights of the similarity functions are calculated; Based on the weights of the similarity function and the vehicle parameters in the preprocessed data, a mapping relationship between the vehicle parameters and the weights of the similarity function is determined. Based on the mapping relationship and the vehicle parameters of the target vehicle, waveform data of the target vehicle under collision conditions is generated; The method of constructing a similarity function for the waveform data in the preprocessed data by weighted summation of multiple polynomial functions includes: The formula for calculating the similarity function is: ； wherein is an independent variable in the waveform data, is a similarity function, is a th polynomial function, , is a weight of a th polynomial function, is a number of polynomial functions.
2. The method of claim 1, wherein, The preprocessing of the experimental data to obtain preprocessed data includes: The vehicle parameters are normalized to obtain normalized vehicle parameters. The waveform data is filtered to obtain filtered waveform data.
3. The method of claim 1, wherein, The step of calculating the weight of the similarity function based on the waveform data and the corresponding similarity function in the preprocessed data includes: The weight that minimizes the weighted squared error between the waveform data and the corresponding similarity function in the preprocessed data is used as the weight of the similarity function.
4. The method of claim 3, wherein The formula for calculating the weighted squared error is: ； wherein, is a weighted square error, is a weight of the th polynomial function, is a th independent variable in the waveform data, is a waveform amplitude corresponding to the th independent variable, is a function value of the th polynomial function corresponding to the th independent variable.
5. The method of claim 1, wherein, Determining the mapping relationship between the vehicle parameters and the weights of the similarity function based on the weights of the similarity function and the vehicle parameters in the preprocessed data includes: Based on the number of polynomial functions and their corresponding weights in the similarity function, and the vehicle parameters in the preprocessed data, the mapping relationship between the vehicle parameters and the weights of the similarity function is determined.
6. The method of claim 5, wherein, The number of polynomial functions in the similarity functions is determined as follows: Calculate the correlation coefficient between similar functions containing different numbers of polynomial functions and the waveform data in the preprocessed data; The minimum number of correlation coefficients greater than a preset threshold is selected as the number of polynomial functions in the similarity function.
7. An apparatus for constructing a vehicle crash universal waveform, characterized by comprising: include: The test data acquisition module is used to acquire test data of multiple vehicles under the same collision conditions; wherein, the test data includes vehicle parameters and waveform data; The preprocessing module is used to preprocess the experimental data to obtain preprocessed data; The similarity function construction module is used to construct similarity functions for waveform data in the preprocessed data by weighted summation of multiple polynomial functions; The similarity weight calculation module is used to calculate the weight of the similarity function based on the waveform data and the corresponding similarity function in the preprocessed data; The mapping relationship determination module is used to determine the mapping relationship between the vehicle parameters and the weights of the similarity function based on the weights of the similarity function and the vehicle parameters in the preprocessed data. The waveform data generation module is used to generate waveform data of the target vehicle under collision conditions based on the mapping relationship and the vehicle parameters of the target vehicle. The similarity function construction module is further configured as follows: The formula for calculating the similarity function is: ； wherein is an independent variable in the waveform data, is a similarity function, is a polynomial function of degree is a polynomial function of degree , is a weight of a polynomial function of degree is a polynomial function of degree is a number of polynomial functions.
8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program for performing the method described in any one of claims 1-6.
9. An electronic device, comprising: include: processor; Memory used to store the processor's executable instructions; The processor is used to execute the method described in any one of claims 1-6.