Method for adjusting chest injury risk curve of pedestrian human body model

Abstract

The invention relates to the technical field of vehicle safety, in particular to a pedestrian body model chest injury risk curve adjusting method. Comprising the following steps: analyzing the relationship between the speed and the chest injury risk, and drawing a first curve between the vehicle speed and the injury risk based on accident statistical data; fitting data of a rib cortical bone uniaxial stretching material mechanics experiment; constructing a vehicle-pedestrian side impact simulation experiment matrix under different working conditions, and generating a simulation sample number; extracting the rib strain of the human body model in the sampling model, calculating the fracture probability of a single rib of the chest of the human body model, and then calculating the maximum simple injury probability of the specified chest of the human body model; fitting the relationship between the vehicle speed in the simulation model and the chest injury risk of the human body model, and drawing a second curve between the vehicle speed in the sample and the chest injury risk of the pedestrian model; and constructing an objective function according to the difference between the first risk curve and the second risk curve. According to the technical scheme, the accuracy of accident damage prediction can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle safety, and particularly to a method for adjusting the chest injury risk curve of a pedestrian human body model. Background Art

[0002] With the increase in the number of automobiles, the research on the protection of pedestrian traffic accidents has become an important topic in the field of vehicle safety. Pedestrian chest injury is one of the common fatal injuries in traffic accidents. Especially in vehicle side collision accidents, the risk of chest rib fractures and accompanying organ injuries increases significantly.

[0003] Traditional risk assessment methods often rely on a large amount of accident statistical data, and reveal the relationship between vehicle speed and pedestrian injury risk through statistical analysis. However, although this method can provide a certain macroscopic perspective, it has limitations in detailedly describing the specific mechanism and probability of pedestrian chest injuries.

[0004] In recent years, with the development of computer simulation technology, vehicle-pedestrian collision simulation experiments have become an important means to study pedestrian injury risk. By constructing accurate pedestrian human body models and vehicle models, various collision scenarios can be simulated in a virtual environment, thereby obtaining rich simulation data. These data provide a new perspective and possibility for in-depth analysis of pedestrian chest injury risk.

[0005] In the prior art, the Logistic regression model is widely used to analyze the relationship between speed and injury risk. By fitting accident statistical data, a first curve between vehicle speed and pedestrian chest injury risk can be drawn. This curve can intuitively show the influence trend of vehicle speed on pedestrian chest injury risk, and provide a certain reference basis for traffic safety planning and management.

[0006] However, analyzing only based on accident statistical data has limitations. On the one hand, the collection of accident data is often restricted by various factors, such as data integrity, accuracy, and sample size, which may lead to certain biases in the analysis results. On the other hand, accident statistical data cannot comprehensively reflect the specific mechanism and details of pedestrian chest injuries, such as the probability and degree of rib fractures. Summary of the Invention

[0007] The purpose of the present invention is to propose a method for adjusting the chest injury risk curve of a pedestrian human body model, and this technical solution can improve the accuracy of accident injury prediction.

[0008] To achieve the above purpose, the present invention provides a method for adjusting the chest injury risk curve of a pedestrian human body model, including: analyzing the relationship between speed and chest injury risk by using the Logistic regression model, and drawing a first curve between vehicle speed and injury risk in accident statistical data; Use the survival model to fit the data of the uniaxial tensile mechanical experiment of rib cortical bone, and obtain the rib strain and rib fracture probability model; Construct a vehicle-pedestrian side impact simulation experiment matrix under different working conditions, and generate the number of simulation samples; Extract the rib strain of the human body model in the sampling model, use the rib strain and fracture probability model to calculate the probability of a single rib fracture in the pedestrian model's chest, and then use the generalized binomial probability model to calculate the maximum abbreviated injury probability of the specified chest of the human body model; Use the Logistic regression model to fit the relationship between the vehicle speed and the chest injury risk of the human body model in the simulation model, and draw the second curve between the vehicle speed and the chest injury risk of the pedestrian model in the sampling sample according to the results of the regression model; Construct an objective function based on the gap between the first risk curve and the second risk curve, so as to obtain a new pedestrian chest injury risk curve oriented to accident characteristics.

[0009] Beneficial effects of the basic solution: By using the Logistic regression model to analyze the accident statistical data and the simulation model data respectively, and drawing the curve of speed and chest injury risk (the first curve), the impact of vehicle speed on the pedestrian chest injury risk can be accurately quantified.

[0010] Use the survival model to fit the data of the uniaxial tensile mechanical experiment of rib cortical bone, obtain the rib strain and rib fracture probability model, and accurately model the rib fracture probability from the perspective of material mechanical properties, making the chest injury assessment more scientific and accurate, being able to more carefully reflect the actual injury situation, and reflecting the pedestrian chest injury risk under different collision conditions.

[0011] Construct a vehicle-pedestrian side impact simulation experiment matrix under different working conditions (including different vehicle types, speeds, ages, genders, heights and impact directions), and generate the number of simulation samples, comprehensively simulate various possible accident scenarios, increase the coverage of the research and the fit with the actual scenario, make the research results more general and practical application value, and restore the occurrence of real-world traffic accidents as much as possible under the limited number of simulation samples.

[0012] Extract the rib strain of the human body model from the sampling model, combine the rib fracture probability model to calculate the probability of a single rib fracture, and then use the generalized binomial probability model to calculate the maximum abbreviated injury probability of the specified chest. Through a multi-level calculation process, the influence of various factors on the chest injury probability is comprehensively considered, realizing a comprehensive and accurate assessment of the pedestrian chest injury risk.

[0013] Construct an objective function based on the gap between the first curve plotted according to accident statistics data and the second curve plotted according to simulation model data, optimize the gap between the first risk curve and the second risk curve, and a pedestrian chest injury risk curve that more conforms to the actual accident characteristics can be generated. Integrating the advantages of actual accident data and simulation data, the risk curve is optimized to make it more conform to the pedestrian chest injury risk situation in actual accidents.

[0014] As an implementable preferred solution, it also includes screening and statistically analyzing traffic accident data, specifically including screening accident data of pedestrian collisions in the traffic accident database. The accident data includes accident type, vehicle type, speed, pedestrian information, and injury situation; among them, the pedestrian information includes age, gender, and height, and the injury situation includes whether rib fractures occur, the number of fractures, and whether other chest injuries are accompanied.

[0015] As an implementable preferred solution, use the Logistic regression model to analyze the relationship between speed and chest injury risk. The expression is as follows:

[0016]

[0017] Among them, represents the occurrence probability of MAIS2+ of pedestrian chest injury, represents the occurrence probability of MAIS3+ of pedestrian chest injury. Both probabilities are obtained through accident data statistics. β 0 、 β 1 、 β 2 and β 3 represent model parameters, speed represents the vehicle speed in accident statistics.

[0018] As an implementable preferred solution, the survival model includes at least one of the Weibull model, the Log-normal model, and the Log-logistic model, and the optimal model is selected through the AIC value. The formula is as follows:

[0019] Among them, is the maximum value of the likelihood function of the model, is the number of variables in the model.

[0020] As an implementable preferred solution, age is incorporated into the survival model as a covariate, and the parameters are adjusted to reflect the difference in fracture probability among different age groups.

[0021] As an implementable and preferred solution, the vehicle in the vehicle-pedestrian side impact simulation experiment matrix adopts a general model, and the pedestrian impact direction includes multiple direction parameters.

[0022] As an implementable and preferred solution, the expression of the generalized binomial probability model is as follows:

[0023]

[0024] Wherein, is the probability of MAIS2+ injury occurring in the chest of the pedestrian model, is the probability of MAIS3+ injury occurring in the chest of the pedestrian model, is the probability of 0 rib fractures, is the probability of 1 rib fracture, , and are the probabilities of the i-th, j-th, and k-th rib fractures.

[0025] As an implementable and preferred solution, the relationship between the vehicle speed and the chest injury risk of the human model in the simulation model is fitted using the Logistic regression model, and the formula is as follows:

[0026]

[0027] Wherein, is the probability of MAIS2+ injury occurring in the chest of the pedestrian model, is the probability of MAIS3+ injury occurring in the chest of the pedestrian model, , , and represent model parameters, represents the vehicle speed in the sampling simulation.

[0028] As an implementable and preferred solution, after constructing the objective function based on the gap between the first risk curve and the second risk curve, the coefficient of the starting risk curve for target optimization or adding a correction parameter. Description of the Drawings

[0029] Figure 1 is a logical schematic diagram of the method for adjusting the chest injury risk curve of the pedestrian human model.

[0030] Figure 2 is a schematic diagram of the first injury risk curve.

[0031] Figure 3 is a schematic diagram of the injury risk curve 3.

[0032] Figure 4 is the objective function schematic diagram of Specific implementation manners

[0033] To make the technical solutions and their advantages of the present application clearer, the technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings. It can be understood that the specific embodiments described herein are only partial embodiments of the present invention, which are only used to explain the present application and not to limit the present application. It should be noted that the technical features or combinations of technical features described in the following embodiments should not be considered as isolated, and they can be combined with each other to achieve better technical effects. The same reference numerals in the accompanying drawings of the following embodiments represent the same features or components, which can be applied to different embodiments.

[0034] In addition, unless otherwise defined, the technical terms or scientific terms used in the description of the present invention should have the ordinary meanings understood by those of ordinary skill in the art to which the present invention belongs.

[0035] The present invention will be further described in detail below with reference to the accompanying drawings: Refer to Figure 1 , a method for adjusting the chest injury risk curve of a pedestrian human body model, comprising: Step S100, screening and statistically analyzing traffic accident data, including: Step S101, screening out accident data of pedestrian collisions in the traffic accident database. The traffic accident database should contain rich traffic accident records, including accident types, vehicle models, speeds, pedestrian information (such as age, gender, height), injury conditions, etc. During the screening process, the integrity and accuracy of the accident data need to be ensured.

[0036] Step S102, statistically analyzing information such as vehicle models, speeds, ages, and chest injury conditions in different accidents. Table 1 is the accident statistical table, and the specific statistical contents include: vehicle models involved in the accident (such as sedans, SUVs, minivans, MPVs, etc.), vehicle speeds (divided by speed intervals), pedestrian ages (divided by age groups), and chest injury conditions (such as whether rib fractures occur, the number of fractures, and whether other chest injuries are accompanied). In addition, the maximum abbreviated injury (MAIS) level needs to be statistically analyzed, especially the occurrence of MAIS2+ (moderate injury, accompanied by 2-3 rib fractures) and MAIS3+ (severe injury, accompanied by 4 or more rib fractures).

[0037] Table 1 Accident Statistical Table

[0038] Step S200, analyze the relationship between speed and chest injury risk, including: Step S201, use the Logistic regression model to analyze the relationship between speed and chest injury risk. The model can be expressed as:

[0039]

[0040] where, represents the occurrence probability of MAIS2+ for pedestrian chest injury, represents the occurrence probability of MAIS3+ for pedestrian chest injury. Both probabilities are obtained through accident data statistics. β 0 , β 1 , β 2 and β 3 represent model parameters, speed represents the vehicle speed in accident statistics.

[0041] Step S202, according to the results of the regression model, draw the first curve based on the relationship between vehicle speed and injury risk in accident statistics data, as Figure 2 shown. The abscissa is the vehicle speed in accident statistics, and the ordinate is the injury risk probability corresponding to the maximum abbreviated injury scale; Step S300, construct a rib strain and fracture probability model, including: Step S301, use the survival model to fit the data of the uniaxial tensile material mechanics experiment of rib cortical bone, and obtain the rib strain and rib fracture probability model. The survival model is a class of statistical models for processing survival data (such as time-to-event data), including the Weibull model, the Log-normal model, and the Log-logistic model, etc. In this step, these three models will be used to fit the uniaxial tensile test data of rib cortical bone respectively, and the AIC value will be calculated to judge the function fitting quality. The calculation formula of the AIC value is:

[0042] where, is the maximum value of the likelihood function of the model, is the number of variables in the model.

[0043] Compare the AIC values corresponding to the three survival models, and select the model with the smallest AIC value as the basis for predicting the chest injury probability of the human body model in the follow-up.

[0044] Step S302: Incorporate age as a covariate into the model to account for the effect of age on the probability of rib fractures. By adjusting the model parameters, the relationship between rib strain and fracture probability at different age groups can be obtained.

[0045] Step S400: Construct a vehicle-pedestrian side impact simulation experiment matrix, including: Step S401: Construct a vehicle-pedestrian side impact simulation experiment matrix under different working conditions. Refer to Table 2, where a general model is used for the vehicle.

[0046] Table 2 Vehicle-human model simulation matrix

[0047] Step S402: Use the Latin Hypercube Sampling (LHS) method to generate the number of simulation samples. In this embodiment, 80 simulation samples are generated. LHS is an efficient sampling method that can restore the occurrence of real-world traffic accidents as much as possible with a limited number of samples. Through LHS sampling, a simulation sample set containing various combinations of working conditions can be obtained.

[0048] Step S500: Extraction of simulation results and calculation of injury probability, including: Step S501: Extract the rib strain of the human model in the sampling model. Use simulation software to perform simulation calculations on the sampling samples to obtain the rib strain response of the human model during the collision process.

[0049] Step S502: Use the determined rib strain and fracture probability model to calculate the probability of a single rib fracture in the chest of the pedestrian model. By comparing the rib strain data under different working conditions, the distribution of the probability of a single rib fracture in the chest under different working conditions can be obtained.

[0050] Step S503: Use the generalized binomial probability model to calculate the maximum abbreviated injury probability (MAIS2+ and MAIS3+) specified for the chest of the human model. The calculation method is as follows:

[0051]

[0052] Where, is the probability of MAIS2+ injury occurring in the chest of the pedestrian model, is the probability of MAIS3+ injury occurring in the chest of the pedestrian model, is the probability of 0 rib fractures, is the probability of 1 rib fracture, 、 and are the probabilities of the i-th, j-th, and k-th rib fractures.

[0053] Step S600, analysis of the relationship between speed and chest injury risk in the sampling sample, including: Step S601, using the Logistic regression model to fit the relationship between vehicle speed and chest injury risk of the human model in the simulation model. The formula is as follows:

[0054]

[0055] where, , , and represent model parameters, and represents the vehicle speed in the sampling simulation.

[0056] Step S602, according to the results of the regression model, the second curve between vehicle speed and chest injury risk of the pedestrian model in the sampling sample can be drawn, as Figure 3 shown. The abscissa is the vehicle speed in the sampling simulation, and the ordinate is the corresponding chest injury risk probability of the pedestrian model.

[0057] Step S700, constructing the risk curve adjustment and objective function, including: S701. Construct the objective function according to the gap between the first risk curve and the second risk curve, as Figure 4 shown, taking the objective optimization as the coefficient of the starting risk curve or adding a correction parameter. Thus, a new pedestrian chest injury risk curve oriented to accident characteristics is obtained.

[0058] Step S702, re-verify the feasibility of this method according to traffic accidents. Select a certain number of traffic accident cases, use the adjusted risk curve to evaluate the chest injury risk of pedestrians, and compare it with the actual injury situation. By comparing the consistency degree between the evaluation result and the actual injury situation, the accuracy and applicability of this method can be verified.

[0059] The embodiment of the present disclosure also provides a system for adjusting the chest injury risk curve of a pedestrian human model, and this system applies the above-mentioned method for adjusting the chest injury risk curve of a pedestrian human model.

[0060] The embodiment of the present disclosure also provides a storage medium, in which a computer program is stored. When the computer program is executed by a processor, all steps of the above-mentioned method for adjusting the chest injury risk curve of a pedestrian human model can be realized.

[0061] Those of ordinary skill in the art can understand that all or part of the processes in implementing the method for adjusting the chest injury risk curve of a pedestrian human body model can be completed by instructing relevant hardware through a computer program. The said program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of various embodiments of the method for adjusting the chest injury risk curve of a pedestrian human body model. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in this application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0062] The above content is only an embodiment of the present invention. Common knowledge such as specific structures and characteristics known to the public in the solution is not described in detail here. Those of ordinary skill in the art know all the common technical knowledge in the technical field to which the invention belongs before the application date or the priority date, can know all the prior arts in this field, and have the ability to apply conventional experimental means before this date. Those of ordinary skill in the art can, under the inspiration given in this application, complete and implement this solution in combination with their own abilities. Some typical well-known structures or well-known methods should not become an obstacle for those of ordinary skill in the art to implement this application. It should be noted that for those skilled in the art, without departing from the structure of the present invention, several modifications and improvements can still be made, and these should also be regarded as the protection scope of the present invention, and these will not affect the implementation effect of the present invention and the practicality of the patent. The protection scope required by this application should be based on the content of its claims, and the specific implementation manners described in the specification can be used to interpret the content of the claims.

Claims

1. A method for adjusting a chest injury risk curve of a pedestrian human body model, characterized in that: include: The relationship between speed and chest injury risk was analyzed using a logistic regression model, and a first curve between vehicle speed and injury risk based on accident statistics was drawn; The survival model was used to fit the data of uniaxial tensile material mechanics experiments of rib cortical bone to obtain the rib strain and rib fracture probability model. Construct a vehicle-pedestrian side collision simulation experiment matrix under different working conditions and generate the number of simulation samples; The rib strain of the human body model in the sampling model is extracted, and the probability of a single rib fracture in the chest of the pedestrian model is calculated using the rib strain and fracture probability model. The maximum simplified injury probability of the specified chest of the human body model is then calculated using the generalized binomial probability model. The Logistic regression model is used to fit the relationship between the vehicle speed and the chest injury risk of the human model in the simulation model, and the second curve between the vehicle speed and the chest injury risk of the pedestrian model in the sampling sample is drawn according to the results of the regression model; The objective function is constructed according to the gap between the first risk curve and the second risk curve, and the pedestrian chest injury risk curve oriented to the accident characteristics is obtained.

2. The method for adjusting the chest injury risk curve of a pedestrian human body model according to claim 1, characterized in that: It also includes screening and statistics of traffic accident data, specifically including screening accident data of pedestrian collisions in the traffic accident database, the accident data including accident type, vehicle type, speed, pedestrian information and injury conditions; The pedestrian information includes age, gender, and height; the injury status includes whether there are rib fractures, the number of fractures, and whether there are other chest injuries.

3. The method for adjusting the chest injury risk curve of a pedestrian human body model according to claim 1, characterized in that: The Logistic regression model was used to analyze the relationship between speed and chest injury risk, and the expression is as follows: in, represents the probability of pedestrian chest injury MAIS2+, represents the probability of pedestrian chest injury MAIS3+. Both probabilities are obtained through accident data statistics. β 0. β 1. β 2 and β 3 represents the model parameters, speed Represents vehicle speed in accident statistics.

4. The method for adjusting the chest injury risk curve of a pedestrian human body model according to claim 1, characterized in that: The survival model includes at least one of the Weibull model, Log-normal model and Log-logistic model, and the optimal model is selected by the AIC value. The formula is as follows: in, is the maximum value of the likelihood function of the model, is the number of variables in the model.

5. The method for adjusting the chest injury risk curve of a pedestrian human body model according to claim 4, characterized in that: Age was included as a covariate in the survival model, and the parameters were adjusted to reflect differences in fracture probability among different age groups.

6. The method for adjusting the chest injury risk curve of a pedestrian human body model according to claim 1, characterized in that: The vehicle in the vehicle-pedestrian side impact simulation experiment matrix adopts a general model, and the pedestrian collision direction includes multiple direction parameters.

7. The method for adjusting the chest injury risk curve of a pedestrian human body model according to claim 1, characterized in that: The expression of the generalized binomial probability model is as follows: in, is the probability of MAIS2+ chest injury in the pedestrian model, is the probability of MAIS3+ chest injury in pedestrian model, The probability of 0 rib fractures, is the probability of 1 rib fracture, , and is the fracture probability of the i-th, j-th and k-th ribs.

8. The method for adjusting the chest injury risk curve of a pedestrian human body model according to claim 1, characterized in that: The Logistic regression model is used to fit the relationship between vehicle speed and chest injury risk of the human model in the simulation model. The expression is as follows: in, is the probability of MAIS2+ chest injury in the pedestrian model, is the probability of MAIS3+ chest injury in the pedestrian model, , , and represents the model parameters, Represents the vehicle speed in the sampled simulation.

9. The method for adjusting the chest injury risk curve of a pedestrian human body model according to claim 1, characterized in that: After constructing the objective function based on the gap between the first risk curve and the second risk curve, The goal is to optimize the coefficients of the risk curve as a starting point or to add a correction parameter.

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