Method for determining the influence of head kinematic boundary conditions on head injury

Abstract

The present application discloses a method for determining the influence of head kinematic boundary conditions on head injury, the method comprising the following steps: obtaining accident information of multiple traffic accident cases that meet preset conditions, wherein the multiple traffic accident cases all involve collisions between vulnerable road users (VRUs) and vehicles; simulating the traffic accident process based on the accident information of the traffic accident cases, outputting head injury indicators and extracting head kinematic boundary conditions in the collision; performing correlation analysis on the extracted head kinematic boundary conditions and the output head injury indicators from the simulation of the multiple traffic accident cases using a univariate linear regression analysis method, calculating the correlation between each head kinematic boundary condition and each head injury indicator, and determining the degree of influence of each head kinematic boundary condition on head injury based on the calculation results. The present application can improve the accuracy and diversity of the correlation between the determined head kinematic boundary conditions and head injury indicators.

Description

Technical Field

[0001] The present application relates to the field of traffic safety technology, and in particular to a method for determining the correlation between head kinematic boundary conditions and head injury indicators in a car collision accident. Background Art

[0002] Head injuries to vulnerable road users (VRUs) are the leading cause of death and injury in vehicle collision accidents. However, the head impactor method used in current pedestrian protection assessment procedures only considers a single collision boundary and cannot fully reflect the entire head impact response process in real accidents. In addition, the adopted head injury assessment criterion (HIC) only considers linear head motion, which has limitations in head injury risk assessment. Furthermore, assessment procedures for the protection of other VRUs are urgently needed.

[0003] Existing methods for determining the correlation between head kinematic boundary conditions and head injury indicators rely on early animal and cadaveric experimental data, or on sports accident data. Compared to the collision loads in traffic accidents, the head impact magnitude in these cases is much smaller, resulting in a low frequency of head injuries, making it difficult to obtain accident data with specific injury reports, significantly affecting the authenticity of head risk predictions. Furthermore, the reconstruction methods used in existing determination methods are relatively simple, and the input impact conditions are somewhat single and ideal, failing to fully reflect the actual load scenarios of head collisions. This results in low accuracy in the correlation between the determined head kinematic boundary conditions and head injury indicators. Summary of the Invention

[0004] The main purpose of this application is to provide a method for determining the impact of head kinematic boundary conditions on head injuries, aiming to improve the accuracy and diversity of the correlation between the determined head kinematic boundary conditions and head injury indicators.

[0005] To achieve the above objectives, an embodiment of the present application provides a method for determining the impact of head kinematic boundary conditions on head injuries, the method comprising the following steps:

[0006] S1. Obtaining accident information of multiple traffic accident cases that meet preset conditions, wherein the multiple traffic accident cases all involve collisions between vulnerable road users (VRUs) and vehicles;

[0007] S2. For each traffic accident case, simulate the traffic accident process based on the accident information of the traffic accident case, output head injury indicators and extract the head kinematic boundary conditions in the collision;

[0008] Wherein, step S2 includes:

[0009] S21. Establish a multi-rigid-body pedestrian model and a multi-rigid-body vehicle model based on the accident information of the traffic accident case;

[0010] S22. Using multi-rigid-body dynamics analysis software to simulate and calculate the traffic accident process based on the accident information, the multi-rigid-body pedestrian model, and the multi-rigid-body vehicle model, a virtually reconstructed traffic accident scene is obtained.

[0011] S23, establishing a multi-rigid body-finite element pedestrian coupling model based on the human body finite element model and the multi-rigid body pedestrian model;

[0012] S24. After replacing the multi-rigid-body pedestrian model with the multi-rigid-body-finite-element pedestrian coupling model in the virtually reconstructed traffic accident scene, perform traffic accident process simulation calculation again to output head injury indicators, wherein the head injury indicators include a head injury criterion (HIC), a generalized acceleration model (GAMBIT) for brain injury threshold, a brain injury criterion (BrIC), a rotational injury criterion (RIC), a head impact power (HIP), a maximum principal strain (MPS), and a cumulative strain damage metric (CSDM); and

[0013] S25. Intercepting complete collision process information between the vulnerable road user (VRU) head and the vehicle during the simulated traffic accident, from the moment the head of the vulnerable road user (VRU) contacts the vehicle to the moment the head of the VRU separates from the vehicle at the end of the collision, and extracting head kinematic boundary conditions during the collision from the complete collision process information. The head kinematic boundary conditions include the peak linear velocity (PLV), peak rotational velocity (PRV), peak linear acceleration (PLA), peak rotational acceleration (PRA), and collision duration (ID) of the center of mass of the VRU head during the collision with the vehicle.

[0014] S3. After completing the simulation of the multiple traffic accident cases, a univariate linear regression analysis method is used to perform a correlation analysis on the head kinematic boundary conditions extracted from the simulation of the multiple traffic accident cases and the output head injury indicators, and the correlation between each head kinematic boundary condition and each head injury indicator is calculated. The degree of influence of each head kinematic boundary condition on the head injury is determined based on the calculation results.

[0015] Preferably, the prediction model of the univariate linear regression analysis method is:

[0016] Y t =ax t +b

[0017] In the above formula, x t is the head kinematic boundary condition; Y t is the head injury index; a, b represent the parameters of the linear regression equation, and a, b are obtained by the following two equations:

[0018]

[0019] In the above two formulas, n represents the number of data in the data set, x i is the value of the i-th head kinematic boundary condition in the data set, Y i is the value of the i-th head injury index in the data set.

[0020] Preferably, the preset conditions include:

[0021] The accident participants in the accident information involve VRU;

[0022] Accident information is sufficient to support virtual reconstruction of traffic accident scenes;

[0023] The VRU head in the accident information collided with the vehicle body.

[0024] Preferably, the multiple traffic accident cases include pedestrian-vehicle collision accident cases and two-wheeled vehicle and car collision accident cases. For pedestrian-vehicle collision accident cases, the multi-rigid body vehicle model includes a multi-rigid body car vehicle model; for two-wheeled vehicle and car collision accident cases, the multi-rigid body vehicle model includes a multi-rigid body car vehicle model and a multi-rigid body two-wheeled vehicle model.

[0025] Preferably, before performing the simulation of the traffic accident process, the method further includes:

[0026] Removing the front windshield structure of the multi-rigid-body automobile model;

[0027] The coupling module in the multi-rigid-body dynamics analysis software is used to connect the finite element front windshield model with the rigid bodies of the corresponding parts of the multi-rigid-body vehicle model to establish a multi-rigid-body-finite-element vehicle coupling model.

[0028] In the virtually reconstructed traffic accident scene, the multi-rigid body vehicle model is replaced with a multi-rigid body-finite element vehicle coupling model.

[0029] Preferably, the establishing of a multi-rigid body-finite element pedestrian coupling model based on the human body finite element model and the multi-rigid body pedestrian model includes:

[0030] Removing the head and neck structure of the multi-rigid-body pedestrian model;

[0031] The head and neck of the human finite element model are extracted, and the coupling module in the multi-rigid-body dynamics analysis software is used to connect the muscles and end nodes of the cervical vertebrae in the neck of the human finite element model, which were originally connected to the torso, with the rigid bodies at the corresponding parts of the upper torso of the multi-rigid-body pedestrian model after the head and neck are removed, thus obtaining a multi-rigid-body-finite-element pedestrian coupling model.

[0032] Preferably, the multi-rigid-body pedestrian model is obtained by scaling and adjusting the TNO 50 percentile adult male and 5 percentile female multi-rigid-body pedestrian models using the multi-rigid-body dynamics analysis software MADYMO; the human body finite element model uses the virtual human body model THUMS 50 percentile adult male finite element model.

[0033] Preferably, the calculating the correlation between each head kinematic boundary condition and each head injury index, and determining the degree of influence of each head kinematic boundary condition on the head injury according to the calculation result, includes:

[0034] Calculate the correlation coefficient R between each head kinematic boundary condition and each head injury index to obtain the determination coefficient R 2 , according to the coefficient of determination R 2 To determine the impact of various head kinematic boundary conditions on head injury.

[0035] Preferably, before performing correlation analysis on the extracted head kinematic boundary conditions and the output head injury indicators by simulating the plurality of traffic accident cases using a univariate linear regression analysis method, the method further includes:

[0036] The linear acceleration peaks in the extracted head kinematic boundary conditions are divided into three magnitude groups: low, medium, and high according to the peak value.

[0037] The rotational acceleration peaks in the extracted head kinematic boundary conditions are divided into three magnitude groups: low, medium, and high according to the peak values; and

[0038] The rotational velocity peaks in the extracted head kinematic boundary conditions are divided into two magnitude groups: low and high.

[0039] The method of using a univariate linear regression analysis method to simulate the multiple traffic accident cases and perform correlation analysis on the extracted head kinematic boundary conditions and the output head injury indicators includes:

[0040] The correlation between the low, medium and high magnitude groups of peak linear acceleration and various head injury indicators was calculated;

[0041] The correlations between the low, medium, and high magnitude groups of peak rotational acceleration and various head injury indicators were calculated.

[0042] The correlations between the low and high magnitude groups of peak rotational velocity and various head injury indicators were calculated.

[0043] Preferably, the calculation of the correlation between the low, medium and high magnitude groups of the linear acceleration peak and each head injury indicator includes:

[0044] The correlation coefficients R between the low, medium and high magnitude groups of the linear acceleration peak and each head injury index were calculated to obtain the determination coefficient R 2 , according to the coefficient of determination R 2 To determine the impact of low, medium and high peak linear acceleration levels on head injury;

[0045] The correlations between the three groups of low, medium and high magnitudes of the calculated peak rotational acceleration and various head injury indicators include:

[0046] The correlation coefficients R between the low, medium, and high magnitude groups of the peak rotational acceleration and each head injury index were calculated to obtain the determination coefficient R 2 , according to the coefficient of determination R 2 To determine the impact of low, medium and high peak linear acceleration levels on head injury;

[0047] The correlations between the low and high magnitude groups of the calculated peak rotational velocity and the head injury indicators include:

[0048] The correlation coefficients R between the low, medium and high magnitude groups of peak rotational velocity and each head injury index were calculated to obtain the determination coefficient R 2 , according to the coefficient of determination R 2 To determine the impact of low and high peak rotation speed groups on head injuries.

[0049] The method for determining the influence of head kinematic boundary conditions on head injury proposed in the present application obtains accident information of multiple traffic accident cases that meet preset conditions, and the multiple traffic accident cases all involve collisions between vulnerable road users (VRU) and vehicles, that is, traffic accident data of real vulnerable road users (VRU) and vehicle collisions are used as samples. The collision boundaries of human heads caused by traffic accidents are more complex and diverse, and the data source is real and reliable, which greatly improves the authenticity of head risk prediction; for each traffic accident case, the traffic accident process is simulated according to the accident information of the traffic accident case, and the head injury index is output and the head kinematic boundary conditions in the collision are extracted. First, according to the accident information, the multi-rigid body pedestrian model and the multi-rigid body vehicle model, the multi-rigid body dynamics analysis software is used to simulate and calculate the traffic accident process to obtain a virtually reconstructed traffic accident scene, rather than direct accident information, multi-rigid body-finite element pedestrian coupling model and multi-rigid body vehicle model, which can greatly reduce the amount of calculation of the virtually reconstructed traffic accident scene; in the virtually reconstructed traffic accident scene, the multi-rigid body pedestrian model is replaced with the multi-rigid body-finite element After the pedestrian coupling model, the traffic accident process simulation calculation is performed again to make the output head injury index and the extracted head kinematic boundary conditions more accurate; and the univariate linear regression analysis method is used to simulate the multiple traffic accident cases to perform correlation analysis on the extracted head kinematic boundary conditions and the output head injury index, calculate the correlation between each head kinematic boundary condition and each head injury index, and determine the degree of influence of each head kinematic boundary condition on head injury based on the calculation results; in this way, the degree of influence of multiple head kinematic boundary conditions on multiple head injuries can be obtained at the same time, and the accuracy of the correlation between the determined head kinematic boundary conditions and head injury indicators is high and diverse, thereby providing important data and method references for the optimization and updating of VRU safety assessment regulations / procedures, and providing important parameters for the establishment of injury prediction equations. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A flowchart of a method for determining the impact of head kinematic boundary conditions on head injury provided in one embodiment of the present application;

[0051] Figure 2 for Figure 1 A detailed flow chart of step S2 in the method shown;

[0052] Figure 3 This is a schematic diagram of the linear regression results between the peak value of collision linear acceleration and various head injury indicators;

[0053] Figure 4This is a schematic diagram of the linear regression results between the peak value of collision rotational acceleration and various head injury indicators;

[0054] Figure 5 Schematic diagram of the linear regression results between the peak linear velocity of the collision and various head injury indicators;

[0055] Figure 6 This is a schematic diagram of the linear regression results between the peak collision rotation velocity and various head injury indicators;

[0056] Figure 7 This is a schematic diagram of the linear regression results between collision duration and various head injury indicators;

[0057] Figure 8 Schematic diagram comparing the overall correlation coefficients of head injury indicators based on head kinematics and brain tissue deformation;

[0058] Figure 9 is the distribution diagram of the linear acceleration peak after grouping;

[0059] Figure 10 is the distribution diagram of the peak value of rotational acceleration after grouping;

[0060] Figure 11 is the distribution diagram of the peak rotation speed after grouping;

[0061] Figure 12 This is a schematic diagram of the linear regression results between the peak linear acceleration of the low-intensity group and various head injury indicators;

[0062] Figure 13 This is a schematic diagram of the linear regression results between the peak linear acceleration and various head injury indicators in the middleweight group;

[0063] Figure 14 This is a schematic diagram of the linear regression results between the peak linear acceleration of the high-intensity group and various head injury indices;

[0064] Figure 15 This is a schematic diagram of the linear regression results between the peak rotational acceleration and various head injury indicators in the low-intensity group;

[0065] Figure 16 This is a schematic diagram of the linear regression results between the peak rotational acceleration and various head injury indicators in the middleweight group;

[0066] Figure 17 This is a schematic diagram of the linear regression results between the peak rotational acceleration and various head injury indicators in the high-intensity group;

[0067] Figure 18 This is a schematic diagram of the linear regression results between the peak rotational velocity and various head injury indicators in the low-intensity group;

[0068] Figure 19Schematic diagram of the linear regression results between the peak rotational velocity and various head injury indicators in the high-intensity group.

[0069] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0070] It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0071] See also Figure 1 One embodiment of the present application provides a method for determining the impact of head kinematic boundary conditions on head injury, the method comprising the following steps:

[0072] S1. Obtaining accident information of multiple traffic accident cases that meet preset conditions, wherein the multiple traffic accident cases all involve collisions between vulnerable road users (VRUs) and vehicles;

[0073] S2. For each traffic accident case, simulate the traffic accident process based on the accident information of the traffic accident case, output head injury indicators and extract the head kinematic boundary conditions in the collision;

[0074] S3. After completing the simulation of the multiple traffic accident cases, a univariate linear regression analysis method is used to perform a correlation analysis on the head kinematic boundary conditions extracted from the simulation of the multiple traffic accident cases and the output head injury indicators, and the correlation between each head kinematic boundary condition and each head injury indicator is calculated. The degree of influence of each head kinematic boundary condition on the head injury is determined based on the calculation results.

[0075] In step S1, the preset conditions include:

[0076] The accident participants in the accident information involve VRU;

[0077] Accident information is sufficient to support virtual reconstruction of traffic accident scenes;

[0078] The VRU head in the accident information collided with the vehicle body.

[0079] The accident information of the multiple traffic accident cases can be obtained by screening in the traffic accident database according to the preset conditions, that is, using real accident information for the virtual reconstruction of the traffic accident scene. As an example, in an application example of this embodiment, the accident information of the multiple traffic accident cases is obtained from the In-depth Investigation of Vehicle Accident in Changsha (IVAC) database and the traffic accident database of the China Automotive Engineering Research Institute. Based on the preset conditions, a total of 50 typical traffic accident cases in which the head of a VRU collided with a vehicle were screened, including 25 pedestrian-vehicle collision accidents and 25 two-wheeled vehicle-car collision accidents. The basic accident information obtained based on the accident information of the 50 traffic accident cases is shown in Table 1 below.

[0080] Table 1 Basic information of accident

[0081]

[0082]

[0083] The accident information of a traffic accident case includes pedestrian information and vehicle information. Pedestrian information may include gender, age, height, weight, speed at the time of collision, etc. Vehicle information may include speed at the time of collision, brand and model, curb weight, vehicle size, speed at the time of collision, etc.

[0084] The number and range of accident information selected for traffic accident cases are not limited to this. The number of cases may be more than 50 or less than 50, and the range of cases may be selected from other traffic accident databases.

[0085] See also Figure 2 , step S2 includes:

[0086] S21. Establish a multi-rigid-body pedestrian model and a multi-rigid-body vehicle model based on the accident information of the traffic accident case;

[0087] S22. Using multi-rigid-body dynamics analysis software to simulate and calculate the traffic accident process based on the accident information, the multi-rigid-body pedestrian model, and the multi-rigid-body vehicle model, a virtually reconstructed traffic accident scene is obtained.

[0088] S23, establishing a multi-rigid body-finite element pedestrian coupling model based on the human body finite element model and the multi-rigid body pedestrian model;

[0089] S24. After replacing the multi-rigid-body pedestrian model with a multi-rigid-body-finite-element pedestrian coupling model in the virtually reconstructed traffic accident scene, perform traffic accident process simulation calculation again to output head injury indicators, wherein the head injury indicators include head injury criterion (HIC), generalized acceleration model for brain injury threshold (GAMBIT), brain injury criterion (BrIC), rotational injury criterion (RIC), head impact power (HIP), maximum principal strain (MPS), and cumulative strain damage measure (CSDM); and

[0090] S25. Capture complete collision process information between the head of the vulnerable road user (VRU) and the vehicle during the simulated traffic accident, from the moment contact occurs to the moment separation of the VRU head from the vehicle at the end of the collision, and extract head kinematic boundary conditions during the collision from the complete collision process information. The head kinematic boundary conditions include the peak linear velocity (PLV), peak rotational velocity (PRV), peak linear acceleration (PLA), peak rotational acceleration (PRA), and impact duration (ID) of the center of mass during the collision between the VRU head and the vehicle.

[0091] In step S21, the multi-rigid-body pedestrian model is obtained by scaling and adjusting the 50th percentile adult male and 5th percentile female multi-rigid-body pedestrian models of the Netherlands Organization for Applied Scientific Research (TNO) using the multi-rigid-body dynamics analysis software MADYMO.

[0092] The multiple traffic accident cases include pedestrian-vehicle collisions and two-wheeler-car collisions. For pedestrian-vehicle collisions, the multi-rigid-body vehicle model includes a multi-rigid-body vehicle model; for two-wheeler-car collisions, the multi-rigid-body vehicle model includes a multi-rigid-body vehicle model and a multi-rigid-body two-wheeler model. Both the multi-rigid-body vehicle model and the multi-rigid-body two-wheeler model are multi-body modeled based on the specific vehicle type involved in the accident, and their primary structures are assigned stiffness properties.

[0093] In step S22 , the multi-body dynamics analysis software may be TNO's MADYMO (MAthematical DYnamic MOdel).

[0094] In step S23, the multi-rigid body-finite element pedestrian coupling model is established based on the human body finite element model and the multi-rigid body pedestrian model, including:

[0095] Removing the head and neck structure of the multi-rigid-body pedestrian model;

[0096] The head and neck of the human finite element model were extracted, and the coupling module in the multi-rigid-body dynamics analysis software was used to connect the muscles and end nodes of the cervical vertebrae in the neck of the human finite element model, which were originally connected to the torso, with the rigid bodies at the corresponding parts of the upper torso of the multi-rigid-body pedestrian model after the head and neck were removed (from left to right, the left clavicle, upper torso, and right clavicle), thus obtaining a multi-rigid-body-finite-element pedestrian coupling model.

[0097] The human finite element model uses the 50th percentile adult male finite element model from the THUMS (Total Human Model for Safety) virtual human model developed by Toyota's Central Research and Development Center. The head and neck sections of the human finite element model include the head and cranium, neck, and thoracic vertebrae T1-T3. The human finite element model incorporates key anatomical structures of the human cranium, such as the scalp, skull, meninges, cerebrospinal fluid, cerebrum, cerebellum, brainstem, and falx. The multi-rigid-body pedestrian model is composed of hinges with human joint characteristics and ellipsoids representing the geometric dimensions, mass, and moment of inertia of various human body parts.

[0098] In step S24, the head injury indicators include the head injury criterion HIC, the brain injury threshold generalized acceleration model GAMBIT, the brain injury criterion BrIC, the rotational injury criterion RIC, the head impact power HIP, the maximum principal strain MPS and the cumulative strain damage measurement CSDM, among which HIC, GAMBIT, BrIC, RIC and HIP are head injury indicators based on head kinematics, and MPS and CSDM are head injury indicators based on brain tissue strain.

[0099] The head injury criterion (HIC) is the most widely used head injury indicator, but its drawback is that it only calculates linear acceleration of the head and does not consider the influence of rotational motion. The rotational injury criterion (RIC) differs from HIC in that it only uses rotational acceleration. The generalized acceleration model for brain injury threshold (GAMBIT) and the head impact power (HIP) consider both linear and rotational acceleration of the head. The BrIC calculation process is based on the head rotation velocity and peak rotational acceleration. MPS is output through a human finite element model. CSDM refers to the volume ratio of the area in the brain tissue where the MPS exceeds a preset threshold to the entire brain tissue. In this embodiment, the thresholds are set to 0.15 and 0.25, respectively.

[0100] In step S25, the head kinematic boundary conditions include the linear velocity peak value PLV, rotational velocity peak value PRV, linear acceleration peak value PLA, rotational acceleration peak value PRA, and collision duration ID of the center of mass of the VRU head during the collision with the vehicle. Linear acceleration and rotational acceleration are important kinematic parameters in the study of craniocerebral injury, and the acceleration peak value and waveform have a significant impact on head injury. Obtaining the acceleration response of the injury process from a real head collision is particularly important for the study of injury mechanism and protection. Linear velocity and rotational velocity are also important head kinematic parameters. Rotational velocity is highly correlated with brain injury. Linear velocity has always been the most direct input condition, affecting the linear and rotational motion of the head and the injury response. The collision duration determines the overall kinematic loading during the head collision.

[0101] In step S3, the prediction model of the univariate linear regression analysis method is:

[0102] Y t =ax t +b

[0103] In the above formula, x t is the head kinematic boundary condition; Y t is the head injury index; a, b represent the parameters of the linear regression equation, and a, b are obtained by the following two equations:

[0104]

[0105] In the above two formulas, n represents the number of data in the data set, x i is the value of the i-th head kinematic boundary condition in the data set, Y i is the value of the i-th head injury index in the data set.

[0106] Calculating the correlation between each head kinematic boundary condition and each head injury index, and determining the influence of each head kinematic boundary condition on the head injury according to the calculation results includes:

[0107] Calculate the correlation coefficient R between each head kinematic boundary condition and each head injury index to obtain the determination coefficient R 2 , according to the coefficient of determination R 2 To determine the impact of various head kinematic boundary conditions on head injury.

[0108] The correlation coefficient (R) measures the degree of correlation between two variables, and its value range is [-1, 1]. The closer the absolute value of the correlation coefficient is to 1, the stronger the correlation between the variables. If the correlation coefficient is greater than 0, it indicates a positive correlation between the two variables; if the correlation coefficient is less than 0, it indicates a negative correlation between the two variables.

[0109] In the application example of this embodiment, when the univariate linear regression analysis method is used to simulate 50 traffic accident cases, the extracted head kinematic boundary conditions and the output head injury indicators are analyzed for correlation. The linear regression results between the center of mass linear velocity peak PLV, rotational velocity peak PRV, linear acceleration peak PLA, rotational acceleration peak PRA, and collision duration ID during the collision between the VRU head and the vehicle and various head injury indicators during the collision are as follows: Figures 3 to 7 The linear regression results of head injury indicators based on head kinematics and brain tissue deformation are shown in Tables 2 and 3. The overall correlation coefficient comparison is as follows: Figure 8 As shown. Among them, p is the measure of whether the two variables are related, and the coefficient of determination R 2 = 0.2 is used as the correlation observation threshold of the linear regression results. Figure 8 Null means that the linear regression results are not significant (i.e. p>0.01).

[0110] Table 2 Linear regression results of head injury indicators based on head kinematics

[0111]

[0112]

[0113] Table 3 Linear regression results of head injury indicators based on brain tissue strain

[0114]

[0115] The linear regression results indicate that the correlations between head kinematic boundary conditions and head injury indices based on head kinematics are generally higher than those between head kinematic boundary conditions and head injury indices based on brain tissue strain, while the correlations between collision duration ID and various head injury indices are relatively low. In the regression analysis of head injury indices based on head kinematics, with the exception of collision duration ID, the peak values ​​of parameters for other head kinematic boundary conditions correlated well with most head injury indices. Overall, the acceleration-related parameters PLA and PRA correlated more strongly with various head injury indices than the velocity-related parameters PLV and PRV. The correlations between linear motion and HIC and HIP were stronger than those between rotational motion and HIC and HIP, while the correlations between rotational motion and GAMBIT, BrIC, and RIC were more significant than those between linear motion and GAMBIT, BrIC, and RIC.

[0116] More specifically, PRA and BrIC (R 2 =0.984, p<0.01), PRA and GAMBIT (R 2 =0.975, p<0.01), PLA and HIP (R 2 =0.845, p<0.01), PLA and HIC (R 2 =0.765, p<0.01) had the most significant correlation.

[0117] In the linear regression results of head injury indicators based on brain tissue strain, although the correlation between the peak values ​​of the head kinematic boundary conditions and the head injury indicators was not significant, the correlation between PRV and head injury indicators was higher than that of all other parameters. 0.25 , CSDM 0.15 , MPS correlation coefficients were 0.628, 0.398, and 0.381, respectively (p<0.01).

[0118] In the above-mentioned method for determining the influence of head kinematic boundary conditions on head injury in the present application, accident information of multiple traffic accident cases that meet preset conditions is obtained, and the multiple traffic accident cases all involve collisions between vulnerable road users (VRU) and vehicles, that is, real traffic accident data of collisions between vulnerable road users (VRU) and vehicles are used as samples. The collision boundaries of human heads caused by traffic accidents are more complex and diverse, and the data source is real and reliable, which greatly improves the authenticity of head risk prediction; for each traffic accident case, the traffic accident process is simulated according to the accident information of the traffic accident case, and the head injury index is output and the head kinematic boundary conditions in the collision are extracted. First, according to the accident information, the multi-rigid body pedestrian model and the multi-rigid body vehicle model, the multi-rigid body dynamics analysis software is used to simulate and calculate the traffic accident process to obtain a virtually reconstructed traffic accident scene, rather than direct accident information, multi-rigid body-finite element pedestrian coupling model and multi-rigid body vehicle model, which can greatly reduce the amount of calculation of the virtually reconstructed traffic accident scene; in the virtually reconstructed traffic accident scene, the multi-rigid body pedestrian model is replaced with the multi-rigid body-finite element After the pedestrian coupling model, the traffic accident process simulation calculation is performed again to make the output head injury index and the extracted head kinematic boundary conditions more accurate; and the univariate linear regression analysis method is used to simulate the multiple traffic accident cases to perform correlation analysis on the extracted head kinematic boundary conditions and the output head injury index, calculate the correlation between each head kinematic boundary condition and each head injury index, and determine the degree of influence of each head kinematic boundary condition on head injury based on the calculation results; in this way, the degree of influence of multiple head kinematic boundary conditions on multiple head injuries can be obtained at the same time, and the accuracy of the correlation between the determined head kinematic boundary conditions and head injury indicators is high and diverse, thereby providing important data and method references for the optimization and updating of VRU safety assessment regulations / procedures, and providing important parameters for the establishment of injury prediction equations.

[0119] Considering that the VRU head in traffic accident cases is subject to highly diverse collision loads, resulting in extremely complex collision injury mechanisms, if only the influence of a single head kinematic (linear or rotational) is simply considered in the same accident case without specifically and comprehensively considering the unique head collision loads in traffic accident cases, this may lead to the output of head injury indicators showing "high input, low injury" or "low input, high injury" in some dimensions.

[14] As a further improvement, before performing correlation analysis on the extracted head kinematic boundary conditions and the output head injury indicators from the simulation of the plurality of traffic accident cases using a univariate linear regression analysis method, the method further includes:

[0120] The linear acceleration peaks in the extracted head kinematic boundary conditions are divided into three magnitude groups: low, medium, and high according to the peak value.

[0121] The rotational acceleration peaks in the extracted head kinematic boundary conditions are divided into three magnitude groups: low, medium, and high according to the peak values; and

[0122] The rotational velocity peaks in the extracted head kinematic boundary conditions are divided into two magnitude groups: low and high according to the peak value.

[0123] In the application example of this embodiment, for 50 traffic accident cases, the distribution of the linear acceleration peak value after grouping, the distribution of the rotation acceleration peak value after grouping, and the distribution of the rotation speed peak value after grouping are respectively as follows: Figures 9 to 11 shown.

[0124] In the case of magnitude grouping, in step S3, the correlation analysis between the head kinematic boundary conditions extracted from the simulation of the plurality of traffic accident cases and the output head injury index using a univariate linear regression analysis method includes:

[0125] The correlation between the low, medium and high magnitude groups of peak linear acceleration and various head injury indicators was calculated;

[0126] The correlations between the low, medium, and high magnitude groups of peak rotational acceleration and various head injury indicators were calculated.

[0127] The correlations between the low and high magnitude groups of peak rotational velocity and various head injury indicators were calculated.

[0128] The correlations between the three magnitude groups of low, medium and high calculated linear acceleration peak values ​​and various head injury indicators include:

[0129] The correlation coefficients R between the low, medium and high magnitude groups of the linear acceleration peak and each head injury index were calculated to obtain the determination coefficient R 2 , according to the coefficient of determination R 2 To determine the impact of low, medium and high peak rotational acceleration levels on head injury;

[0130] The correlations between the three groups of low, medium and high magnitudes of the calculated peak rotational acceleration and various head injury indicators include:

[0131] The correlation coefficients R between the low, medium, and high magnitude groups of the peak rotational acceleration and each head injury index were calculated to obtain the determination coefficient R 2 , according to the coefficient of determination R 2 To determine the impact of low, medium and high peak linear acceleration levels on head injury;

[0132] The correlations between the low and high magnitude groups of the calculated peak rotational velocity and the head injury indicators include:

[0133] The correlation coefficients R between the low, medium and high magnitude groups of peak rotational velocity and each head injury index were calculated to obtain the determination coefficient R 2 , according to the coefficient of determination R 2 To determine the impact of low and high peak rotation speed groups on head injuries.

[0134] In the application example of this embodiment, for 50 traffic accident cases, a linear regression analysis method was used to perform correlation analysis on the grouped rotational acceleration peak value PRA, the grouped linear acceleration peak value PLA, the grouped rotation velocity peak value PRV, and the output head injury index. The linear regression results of the low-level group linear acceleration peak value and each head injury index are as follows: Figure 12 The linear regression results of the middleweight group's peak linear acceleration and various head injury indicators are shown in Figure 13 As shown in the figure, the linear regression results of the peak linear acceleration of high collision and various head injury indicators are as follows: Figure 14 The linear regression results of low-impact rotational acceleration peak and various head injury indicators are shown in Figure 15 The linear regression results of the peak value of rotational acceleration in a mid-collision and various head injury indices are shown in Figure 16 The linear regression results of the peak value of high collision rotational acceleration and various head injury indicators are shown in Figure 17 The linear regression results of the peak rotational velocity in low-impact collisions and various head injury indices are shown in Figure 18 The linear regression results of the peak rotational velocity of high collision and various head injury indicators are shown in Figure 19 Among them, the linear regression results of head injury indicators based on head kinematics and brain tissue deformation at different magnitudes are shown in Tables 4 and 5.

[0135] Table 4 Linear regression results of head injury indicators based on head kinematics at different levels

[0136]

[0137]

[0138] Table 5 Linear regression results of head injury indicators based on brain tissue strain at different magnitudes

[0139]

[0140]

[0141] From the analysis results of head injury index based on head kinematics at different levels in Table 4, it can be seen that GAMBIT has a good performance in the low-level group (R 2 =0.711, p<0.01) and the high-intensity group (R 2 =0.703, p<0.01), showed a better correlation than that in the middleweight group; HIP showed a higher correlation in the middleweight group (R 2 =0.793, p<0.01); HIC showed a significant correlation with LA in the middle and high weight groups, and the correlation was higher in the high weight group (R 2 =0.761,p<0.01).

[0142] Among the groups of rotational acceleration RA, the high-level group showed no significant correlation with the injury indicators; both GAMBIT and BrIC showed a high correlation in the low and medium-level RA groups (R 2 >0.82, p<0.01); RIC showed correlation only in the low-grade RA group, while HIP showed a lower correlation only in the moderate-grade RA group.

[0143] In the rotational velocity RV magnitude group, the correlation between each injury index and it was weak, and RIC was weak in the high magnitude group (R 2 =0.4732, p<0.01) showed a higher 2 =0.2556, p<0.01) had a better correlation; GAMBIT and BrIC were significantly correlated only in the high-level group, while BrIC had a relatively high correlation with the high-level group of RV (R 2 =0.4695,p<0.01).

[0144] When analyzing the injury index analysis results based on brain tissue strain (Table 5), it was found that the linear correlation between the linear acceleration LA, rotational acceleration RA, and rotational velocity RV and the head injury index was relatively limited. 0.15 Compared with its low level (R 2 =0.672, p<0.01) and high magnitude (R 2 =0.776, p<0.01) were significantly correlated; for rotational acceleration RA, only CSDM 0.15 There is a weak correlation with its low-level group (R 2 =0.4345, p<0.01). For the rotation speed RV levels, MPS, CSDM 0.15 , CSDM 0.25 The correlation is relatively significant, especially in CSDM 0.15 (R 2=0.4579, p<0.01) and CSDM 0.25 (R 2 =0.472, p < 0.01) and low magnitude. It can be seen that, in general, the injury index based on brain tissue strain is more sensitive to rotational velocity RV than to linear acceleration LA and rotational acceleration RA.

[0145] The determination coefficient R was obtained by calculating the correlation coefficient R between the low, medium and high magnitude groups of the peak rotational acceleration and each head injury index. 2 , according to the coefficient of determination R 2 This method can determine the degree of impact of low, medium and high magnitude groups of linear acceleration peak on head injuries, which can further provide important data and method references for the optimization and update of VRU safety assessment regulations / procedures, and provide important parameters for the establishment of injury prediction equations.

[0146] As a further improvement, the determination method in the embodiment of the present application further includes:

[0147] Removing the front windshield structure of the multi-rigid-body automobile model;

[0148] The coupling module in the multi-rigid-body dynamics analysis software is used to connect the finite element front windshield model with the rigid bodies of the corresponding parts of the multi-rigid-body vehicle model to establish a multi-rigid-body-finite-element vehicle coupling model.

[0149] In the virtually reconstructed traffic accident scene, the multi-rigid body vehicle model is replaced with a multi-rigid body-finite element vehicle coupling model.

[0150] By replacing the multi-rigid body vehicle model with a multi-rigid body-finite element vehicle coupling model in the virtually reconstructed traffic accident scene, the head injury index output in step S24 and the head kinematic boundary conditions extracted in step S25 can be made more accurate. In this way, the degree of influence of each head kinematic boundary condition on the head injury ultimately determined by the determination method can also be more accurate.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for determining the influence of head kinematic boundary conditions on head injury, characterized in that: The method comprises the following steps: S1. Obtaining accident information of multiple traffic accident cases that meet preset conditions, wherein the multiple traffic accident cases all involve collisions between vulnerable road users (VRUs) and vehicles; S2. For each traffic accident case, simulate the traffic accident process based on the accident information of the traffic accident case, output head injury indicators and extract the head kinematic boundary conditions in the collision; Wherein, step S2 includes: S21. Establish a multi-rigid-body pedestrian model and a multi-rigid-body vehicle model based on the accident information of the traffic accident case; S22. Using multi-rigid-body dynamics analysis software to simulate and calculate the traffic accident process based on the accident information, the multi-rigid-body pedestrian model, and the multi-rigid-body vehicle model, a virtually reconstructed traffic accident scene is obtained. S23, establishing a multi-rigid body-finite element pedestrian coupling model based on the human body finite element model and the multi-rigid body pedestrian model; S24. After replacing the multi-rigid-body pedestrian model with the multi-rigid-body-finite-element pedestrian coupling model in the virtually reconstructed traffic accident scene, perform traffic accident process simulation calculation again to output head injury indicators, wherein the head injury indicators include a head injury criterion (HIC), a generalized acceleration model (GAMBIT) for brain injury threshold, a brain injury criterion (BrIC), a rotational injury criterion (RIC), a head impact power (HIP), a maximum principal strain (MPS), and a cumulative strain damage metric (CSDM); and S25. Intercepting complete collision process information between the vulnerable road user (VRU) head and the vehicle during the simulated traffic accident, from the moment the head of the vulnerable road user (VRU) contacts the vehicle to the moment the head of the VRU separates from the vehicle at the end of the collision, and extracting head kinematic boundary conditions during the collision from the complete collision process information. The head kinematic boundary conditions include the peak linear velocity (PLV), peak rotational velocity (PRV), peak linear acceleration (PLA), peak rotational acceleration (PRA), and collision duration (ID) of the center of mass of the VRU head during the collision with the vehicle. S3. After completing the simulation of the multiple traffic accident cases, a univariate linear regression analysis method is used to perform a correlation analysis on the head kinematic boundary conditions extracted from the simulation of the multiple traffic accident cases and the output head injury indicators, and the correlation between each head kinematic boundary condition and each head injury indicator is calculated. The degree of influence of each head kinematic boundary condition on the head injury is determined based on the calculation results.

2. The method according to claim 1, wherein The prediction model of the univariate linear regression analysis method is: Y t =ax t +b In the above formula, x t is the head kinematic boundary condition; Y t is the head injury index; a, b represent the parameters of the linear regression equation, and a, b are obtained by the following two equations: In the above two formulas, n represents the number of data in the data set, x i is the value of the i-th head kinematic boundary condition in the data set, Y i is the value of the i-th head injury index in the data set.

3. The method according to claim 1, wherein The preset conditions include: The accident participants in the accident information involve VRU; Accident information is sufficient to support virtual reconstruction of traffic accident scenes; The VRU head in the accident information collided with the vehicle body.

4. The method according to claim 1, wherein The multiple traffic accident cases include pedestrian-vehicle collision accident cases and two-wheeled vehicle-car collision accident cases. For pedestrian-vehicle collision accident cases, the multi-rigid body vehicle model includes a multi-rigid body car vehicle model; for two-wheeled vehicle-car collision accident cases, the multi-rigid body vehicle model includes a multi-rigid body car vehicle model and a multi-rigid body two-wheeled vehicle model.

5. The method according to claim 4, wherein Before conducting the simulation of the traffic accident process, it also includes: Removing the front windshield structure of the multi-rigid body automobile model; The coupling module in the multi-rigid-body dynamics analysis software is used to connect the finite element front windshield model with the rigid bodies of the corresponding parts of the multi-rigid-body vehicle model to establish a multi-rigid-body-finite-element vehicle coupling model. In the virtually reconstructed traffic accident scene, the multi-rigid body vehicle model is replaced with a multi-rigid body-finite element vehicle coupling model.

6. The method according to claim 1, wherein The multi-rigid body-finite element pedestrian coupling model is established based on the human body finite element model and the multi-rigid body pedestrian model, including: Removing the head and neck structure of the multi-rigid-body pedestrian model; The head and neck of the human finite element model are extracted, and the coupling module in the multi-rigid-body dynamics analysis software is used to connect the muscles and end nodes of the cervical vertebrae in the neck of the human finite element model, which were originally connected to the torso, with the rigid bodies at the corresponding parts of the upper torso of the multi-rigid-body pedestrian model after the head and neck are removed, thus obtaining a multi-rigid-body-finite-element pedestrian coupling model.

7. The method according to claim 6, wherein The multi-rigid-body pedestrian model is obtained by scaling and adjusting the TNO 50 percentile adult male and 5 percentile female multi-rigid-body pedestrian models using the multi-rigid-body dynamics analysis software MADYMO; the human body finite element model is obtained by using the virtual human body model THUMS 50 percentile adult male finite element model.

8. The method according to claim 1, wherein The calculating of the correlation between each head kinematic boundary condition and each head injury index, and determining the influence of each head kinematic boundary condition on the head injury according to the calculation result, includes: Calculate the correlation coefficient R between each head kinematic boundary condition and each head injury index to obtain the determination coefficient R 2 , according to the coefficient of determination R 2 To determine the impact of various head kinematic boundary conditions on head injury.

9. The method according to claim 1, wherein Before performing correlation analysis on the head kinematic boundary conditions extracted from the simulation of the plurality of traffic accident cases and the output head injury indicators using a univariate linear regression analysis method, the method further includes: The linear acceleration peaks in the extracted head kinematic boundary conditions are divided into three magnitude groups: low, medium, and high according to the peak value. The rotational acceleration peaks in the extracted head kinematic boundary conditions are divided into three magnitude groups: low, medium, and high according to the peak values; and The rotational velocity peaks in the extracted head kinematic boundary conditions are divided into two magnitude groups: low and high. The method of using a univariate linear regression analysis method to simulate the multiple traffic accident cases and perform correlation analysis on the extracted head kinematic boundary conditions and the output head injury indicators includes: The correlation between the low, medium and high magnitude groups of peak linear acceleration and various head injury indicators was calculated; The correlations between the low, medium, and high magnitude groups of peak rotational acceleration and various head injury indicators were calculated. The correlations between the low and high magnitude groups of peak rotational velocity and various head injury indicators were calculated.

10. The method according to claim 9, wherein The correlation between the three magnitude groups of low, medium and high calculated linear acceleration peak values ​​and various head injury indicators includes: The correlation coefficients R between the low, medium and high magnitude groups of the linear acceleration peak and each head injury index were calculated to obtain the determination coefficient R 2 , according to the coefficient of determination R 2 To determine the impact of low, medium and high peak linear acceleration levels on head injury; The correlations between the three groups of low, medium and high magnitudes of the calculated peak rotational acceleration and various head injury indicators include: The correlation coefficients R between the low, medium, and high magnitude groups of the peak rotational acceleration and each head injury index were calculated to obtain the determination coefficient R 2 , according to the coefficient of determination R 2 To determine the impact of low, medium and high peak linear acceleration levels on head injury; The correlations between the low and high magnitude groups of the calculated peak rotational velocity and the head injury indicators include: The correlation coefficients R between the low, medium and high magnitude groups of peak rotational velocity and each head injury index were calculated to obtain the determination coefficient R 2 , according to the coefficient of determination R 2 To determine the impact of low and high peak rotation speed groups on head injuries.

Images