A study on correlation of pedestrian head injuries with physical parameters using in-depth traffic accident data and mathematical models

doi:10.1016/j.aap.2018.07.012

Accident Analysis & Prevention

Volume 119, October 2018, Pages 91-103

https://doi.org/10.1016/j.aap.2018.07.012 Get rights and content

Highlights

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The 43 real-world pedestrian accidents were collected with detailed record of head injury severity.
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Head-to-windscreen collisions in the accidents were reconstructed using a human head FE model.
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The brain injury risks were analyzed via logistic regression model based on the established injury predictor and tolerance threshold.
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The determined brain injury predictor and tolerance was suggested for assessment of vehicle safety performance.

Abstract

The objective of the present study is to predict brain injuries and injury severities from realworld traffic accidents via in-depth investigation of head impact responses, injuries and brain injury tolerances. Firstly, a total of 43 passenger car versus adult pedestrian accidents were selected from two databases of the In-depth Investigation of Vehicle Accidents in Changsha of China (IVAC) and the German In-Depth Accident Study (GIDAS). In a previous study the 43 accidents were reconstructed by using the multi-body system (MBS) model (Peng et al., 2013a) for determining the initial conditions of the head-windscreen impact in each accident. Then, a study of the head injuries and injury mechanisms is carried out via 43 finite element (FE) modelings of a head strike to a windscreen, in which the boundary and loading conditions are defined according to results from accident reconstructions, including impact velocity, position and orientation of the head FE model. The brain dynamic responses were calculated for the physical parameters of the coup/countercoup pressure, von Mises and maximum shear stresses at the cerebrum, the callosum, the cerebellum and the brain stem. In addition, head injury criteria, including the cumulative strain damage measure (CSDM) (with tissue level strain threshold 0.20) and the dilatational damage measure (DDM), were developed in order to predict the diffuse axonal injury (DAI) and contusions, respectively. The correlations between calculated parameters and brain injuries were determined via comparing the simulation results with the observed injuries in accident data. The regression models were developed for predicting the injury risks in terms of the brain dynamic responses and the calculated CSDM and DDM values. The results indicate that the predicted values of 50% probability causing head injuries in the Abbreviated Injury Scale (AIS) 2+ correspond to coup pressure 167 kPa, countercoup pressure −117 kPa, von Mises 16.3 kPa and shear stress 7.9 kPa respectively, and causing AIS 3+ head injuries were 227 kPa, −169 kPa, 24.2 kPa and 12.2 kPa respectively. The results also suggest that a 50% probability of contusions corresponds to CSDM value of 48% at strain levels of 0.2, and the 50% probability of contusions corresponds to a DDM value of 6.7%.

Introduction

Pedestrians are regarded as an extremely vulnerable and high-risk group of road users since they are unprotected in vehicle impacts (Anderson et al., 1997; SDSBG, 2005; Oh et al., 2008; Kong and Yang, 2010; TABC, 2010; ITARDA, 2010). In vehicle-to-pedestrian accidents, the head is one of the most frequently injured body parts and may lead to disability or even death, which has been widely investigated during the past four decades in order to understand the injury mechanisms and to develope safety counter-measures (Ashton et al., 1977; Willinger et al., 1995; Otte and Pohlemann, 2001; Maki et al., 2003; Fildes et al., 2004; Yang, 2005; Mizuno, 2005; Neal-Sturgess et al., 2007; Khaykin and Larner, 2016; Li et al., 2017). To date it was identified that the head injuries mainly result from contacts with vehicle front parts and also from ground contact (Otte and Pohlemann, 2001; Peng et al., 2012; Shang et al., 2018). Furthermore, the automobile windscreen has been identified as one of the main sources for pedestrian head injuries. Otte (1999) reported that the windscreen was the most frequent vehicle source of head injury in an analysis of 543 cases and Mizuno (2005) reported that the windscreen glass was the leading source of head injury for adult pedestrians in the Summary Report of IHRA Pedestrian Safety Working Group activity. Margriet et al. (2011) also found that windscreen area was the main source of the head injury in vehicle-to-pedestrian collisions through analysis of accident data.

Common head injuries in vehicle-to-pedestrian collisions are skull fracture, laceration, cerebral injuries including contusion, concussion, intracranial hematoma, and diffuse axonal injury (DAI). Therefore, preventing and minimizing head injuries has become a critical issue and it is fundamental to understand the mechanisms of these injuries. Various studies have been made in this field, but the injury mechanism and the tolerance thresholds of the brain remain controversial. Nowadays, more numerical models with higher biofidelity have been developed, which provide an efficient way to study the head injuries. Finite Element Methods (FEM) has been considered the best tool for investigating human head response under controlled impact conditions. Ward and Thompson (1975) presented a detailed finite element brain model which provides the important internal structural characteristics and surface geometry of the human brain and insight in brain dynamics. In order to study the human head response to impact loading, the head FE model was developed by Khalil and Hubbard (1977) and they found that the load spatial distribution has sufficient influence on skull strains. Willinger et al. (1995) developed a three-dimensional finite element model to distinguish the risk of focal lesion or sub-dural hematoma from diffuse axonal injury risks. Zhang et al. (2001) developed the human head FE model to investigate internal responses of the brain and improve head injury protection. Head Finite Element models were created using different head sizes and various element mesh densities and the frontal impacts towards padded surfaces were analyzed by Kleiven and von Holst (2002). The simulation results suggested that the size dependence of the intracranial stresses associated with injury is not predicted by the HIC and head size should be considered for new head injury criteria. A head finite element model was used by Zong et al. (2006) in order to predict the stress levels inside a head subjected to impact loading in a virtual environment. The human body head model (HBM-head) was developed by Yang et al., 2007, Yang et al., 2008 for investigations of head injuries in traffic accident. Different types of head injuries (skull fracture, focal brain injury, DAI, contusions, focal lesions) can be evaluated by using head response parameters such as head injury criterion (HIC), head impact power (HIP), skull von Mises stress, intracranial pressure, brain principal shear strain, cumulative strain damage measure (CSDM) and dilatational damage measure (DDM) (Takhounts et al., 2003; Zhang et al., 2004; Yang et al., 2008; Marjoux et al., 2008). There are a bunch of studies on the FE modeling head injuries in different crash events, but very few simulation studies on pedestrian head injuries in connection with in-depth analysis of accident data due to difficulty for acquisition of detailed data about road, vehicle, and injuries etc., and only relying on simulation results would make it difficult to establish a meaningful injury criterion. On the other hand there is still needs of additional study on pedestrian head injuries for improving vehicle safety performance. This research has main contributions to the better understanding the pedestrian head injuries by establishing the correlation of the head injuries with calculated physical parameter based on the real-world accident data from head to windscreen impact. The established predictors and the tolerance thresholds in this study can be applied to evaluate the effectiveness of improved protective measures by calculating the degradation of these injury parameter values.

In this study, the head to windscreen collisions were reconstructed by using FE models based on real-world pedestrian accidents. A total of 43 passenger car versus adult pedestrian accidents were selected from the two databases which have been established in China and Germany respectively. The objective is to analyze brain responses and determine brain injury tolerances, which can be used to develop head injury criterion and predict brain injuries.

Section snippets

Method and materials

This is a continuous study on the pedestrian head injuries and injury mechanisms from vehicle collisions by using a comprehensive methodology, in which a few approaches were used including the in depth analysis of the selected 43 accident data, the analysis of the kinematics via the multi-body system (MBS) accident reconstructions of the car to pedestrian impact, the reconstructions of the head-windscreen impact in each accident via a human head FE model, and a statistical analysis of brain

Statistic analysis of the initial head contact location

The left and right head hemispheres were divided clockwise into 6 equal parts. As shown in Fig. 5, the percentages of occurrence of the initial head contact location of left and right side are 47% and 53% respectively. Highest frequency (53%) of initial contact was observed at the occipital segment of the parts L1 and R1, which compared with the initial contact frequency 26% at the lateral segment of the parts L2 and R2 and the 21% at frontal segment of parts L3 and R3.

Fig. 6 shows the location

Discussion

To understand the relationship between clinical symptoms and brain injury mechanisms, many investigations have been done via experimental testing with physical or biological substitutes, but it is difficult or even impossible to reproduce the injuries in real world traffic accidents due to the complicated dynamic loading conditions suffered by the victims. With the rapid development of the advanced human body mathematical models is using the head FE models to study head injury biomechanics in

Conclusion

The head-to-windscreen impacts in forty-three selected real-world vehicle-to-pedestrian accidents were reconstructed using a finite element head model. The resulting brain response parameters were used for determining tolerance threshold and predicting brain injury risks, including coup pressure, contrecoup pressure, von Mises stress, shear stress, CSDM and DDM were chosen as predictors of potential brain injuries. The predictors and the tolerances for brain injuries were proposed and the

Acknowledgements

The authors would like to thank the In-depth Investigation of Vehicle Accidents in Changsha (IVAC) team and the accident research unit (ARU) of medical university of Hannover for the valuable accident data. In addition, thanks for the financial support of the National Natural Science Foundation of China (10472031 and 11202077), the GM Research & Development Center (GMRD209), the USA, the China Scholarship Council (CSC) and the Postdoctoral Science Foundation of China (2015M570691).

References (47)

R.W.G. Anderson et al.
Vehicle travel speeds and the incidence of fatal pedestrian crashes
Accid. Anal. Prev.
(1997)
S.N. Huang et al.
Optimization of a reversible hood for protecting a pedestrian’s head during car collisions
Accid. Anal. Prev.
(2010)
T.B. Khalil et al.
Parametric study of head response by finite element modeling
J. Biomech.
(1977)
S. Kleiven et al.
Consequences of head size following trauma to the human head
J. Biomech.
(2002)
C.Y. Kong et al.
Logistic regression analysis of pedestrian casualty risk in passenger vehicle collisions in China
Accid. Anal. Prev.
(2010)
G. Li et al.
The influence of passenger car front shape on pedestrian injury risk observed from German in-depth accident data
Accid. Anal. Prev.
(2017)
T. Maki et al.
Comparative analysis of vehicle–bicyclist and vehicle–pedestrian accidents in Japan
Accid. Anal. Prev.
(2003)
D. Marjoux et al.
Head injury prediction apability of the HIC, HIP, SIMon and ULP criteria
Accid. Anal. Prev.
(2008)
J. Nie et al.
A study of fatality risk and head dynamic response of cyclist and pedestrian based on passenger car accident data analysis and simulations
Traffic Inj. Prev.
(2015)
C. Oh et al.
Assessing the safety benefits of an advanced vehicular technology for protecting pedestrians
Accid. Anal. Prev.
(2008)

Y. Peng et al.

Finite element modeling of crash test behavior for windscreen laminated glass

Int. J. Impact Eng.

(2013)

J.K. Yang et al.

A study on head injury biomechnics in car-to-pedestrian collisions using human body models

J. Biomech.

(2007)

J.F. Yao et al.

Investigation of head injuries by reconstructions of real-world vehicle-versus-adult-pedestrian accidents

Saf. Sci.

(2008)

Z. Zong et al.

A three-dimensional human head finite element model and power flow in a human head subject to impact loading

J. Biomech.

(2006)

AAAM, Abbreviated Injury Scale 2005 (AIS 2005). Association for the Advancement of Automotive Medicine, Des Plaines,...

S.J. Ashton et al.

Pedestrian Injuries and the Car Exterior. SAE Technical Paper 770092

(1977)

S. Barbat et al.

Objective rating metric for dynamic systems

L. Eriksson et al.

Using the objective rating method (Orm) as a quality assessment tool for physical tests, test methods, and mathematical models

B. Fildes et al.

Pedestrian impact priorities using real-world crash data and harm

C. Gehre et al.

Objective rating of signals using test and simulation responses

Institute for Traffic Accident Research and Data Analysis of Japan (ITARDA)

Annual Traffic Accident Report in 2010, Tokyo, 2011 (in Japanese); CARE Database 2010, Traffic Safety Basic Facts 2010, Pedestrian

(2010)

Khaykin, A., Larner, D.L., 2016. Adhesive Vehicle Front End for Mitigation of Secondary Pedestrian Impact. Google...

S. Kleiven

Predictors for traumatic brain injuries evaluated through accident reconstruction

Stapp Car Crash J.

(2007)

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A study on correlation of pedestrian head injuries with physical parameters using in-depth traffic accident data and mathematical models

Highlights

Abstract

Introduction

Section snippets

Method and materials

Statistic analysis of the initial head contact location

Discussion

Conclusion

Acknowledgements

Accid. Anal. Prev.

Accid. Anal. Prev.

J. Biomech.

J. Biomech.

Accid. Anal. Prev.

Accid. Anal. Prev.

Accid. Anal. Prev.

Accid. Anal. Prev.

Traffic Inj. Prev.

Accid. Anal. Prev.

Int. J. Impact Eng.

J. Biomech.

Saf. Sci.

J. Biomech.

Pedestrian Injuries and the Car Exterior. SAE Technical Paper 770092

Objective rating metric for dynamic systems

Using the objective rating method (Orm) as a quality assessment tool for physical tests, test methods, and mathematical models

Pedestrian impact priorities using real-world crash data and harm

Objective rating of signals using test and simulation responses

Annual Traffic Accident Report in 2010, Tokyo, 2011 (in Japanese); CARE Database 2010, Traffic Safety Basic Facts 2010, Pedestrian

Predictors for traumatic brain injuries evaluated through accident reconstruction

Stapp Car Crash J.