Monte carlo method for estimating whole-body injury metrics from pedestrian impact simulation results

A method for generating case-specific vehicle models from a single-view vehicle image for accurate pedestrian injury reconstructions

Developing vehicle finite element (FE) models that match real accident-involved vehicles is challenging. This is related to the intricate variety of geometric features and components. The current study proposes a novel method to efficiently and accurately generate case-specific buck models for car-to-pedestrian simulations. To achieve this, we implemented the vehicle side-view images to detect the horizontal position and roundness of two wheels to rectify distortions and deviations and then extracted the mid-section profiles for comparative calculations against baseline vehicle models to obtain the transformation matrices. Based on the generic buck model which consists of six key components and corresponding matrices, the case-specific buck model was generated semi-automatically based on the transformation metrics. Utilizing this image-based method, a total of 12 vehicle models representing four vehicle categories including family car (FCR), Roadster (RDS), small Sport Utility Vehicle (SUV), and large SUV were generated for car-to-pedestrian collision FE simulations in this study. The pedestrian head trajectories, total contact forces, head injury criterion (HIC), and brain injury criterion (BrIC) were analyzed comparatively. We found that, even within the same vehicle category and initial conditions, the variation in wrap around distance (WAD) spans 84-165 mm, in HIC ranges from 98 to 336, and in BrIC fluctuates between 1.25 and 1.46. These findings highlight the significant influence of vehicle frontal shape and underscore the necessity of using case-specific vehicle models in crash simulations. The proposed method provides a new approach for further vehicle structure optimization aiming at reducing pedestrian head injury and increasing traffic safety.

Evaluation of AIS3+ car occupant injuries using deterministic and probabilistic methods in frontal crashes

Computational modelling was used to assess the capability of a deterministic and a probabilistic method to predict the incidence of AIS3+ injuries in passenger car occupants by comparing the predictions of the methods to the actual injuries observed in real-world crashes. The likelihood of sustaining an injury was first calculated using a computer model for a selected set of injury criteria in different impact conditions based on real-world crashes; AIS3+ injuries were then predicted using each method separately. Regardless of the method, the number of serious injuries was over-predicted. It was also noted that the used injury criteria suggested the occurrence of specific injuries that were not observed in the real world. Although both methods are susceptible to be adapted to improve their predictions, the question of the suitability of using some of the most commonly accepted injury criteria used with crash test dummies for injury assessment with human body models deserves further research.

Investigation on the Modeling and Reconstruction of Head Injury Accident Using ABAQUS/Explicit

A process of modeling and reconstructing human head injuries involved in traffic crashes based on ABAQUS/Explicit is presented in this paper. A high-fidelity finite element (FE) model previously developed by the authors is employed to simulate a real accident case that led to head injury. The most probable head impact position informed by CT images is used for the FE modeling and simulation since the head impact position is critical for accident reconstruction and future analysis of accidents that involve human head injuries. Critical von Mises stress on the skull surface of the head model is chosen as the evaluation criterion for the head injury and FE simulations on 60 cases with various human head-concrete ground impact conditions (impact speeds and angles) were run to obtain those stress values. The FE simulation results are compared with the CT images to determine the minimum speed that will cause skull fracture and the corresponding contact angle at that speed. Our study shows that the minimum speed that would cause skull fracture is 3.5 m/s when the contact angle between the occipital position of the injured head and the ground is about 30°. Effects of the impact speed and the contact angle on the maximum von Mises stress of the head model are revealed from the simulations. The method presented in this paper will help forensic pathologists to examine the head impact injuries and find out the real reasons that lead to those injuries.

A novel approach to investigate effects of front-end structures on injury response of e-bike riders: Combining Monte Carlo sampling, automatic operation, and data mining

Transportation safety related to e-bikes is becoming more problematic with the growing popularity in recent decade years, however, rare studies focused on the protection for e-bike riders in traffic accidents. This paper aimed to investigate the relationship between vehicle front-end structures and rider's injury based on a novel approach including modeling, sampling, and analyzing. Firstly, a parametrized model for front-end structures of the vehicle was developed with nine parameters to realize the standardization of multi-body models of car to e-bike collision considering three stature riders and different impacting velocities. Secondly, a framework, combining Monte Carlo sampling for twelve initial variables and automatic operation for 1000 impact simulations, was built to obtain valid results automatically and then to construct a big dataset. Finally, according to the sensitive variables to riders' vulnerable regions, the decision tree algorithm was further adopted to develop the decision or prediction model on injuries. The novel approach achieved the stochastical generation of vehicle shapes and the automatic operation of multi-body models. The results showed that the rider's head, pelvis, and thighs were more vulnerable to being injured in the car to e-bike perpendicular accidents. The three decision tree models (HIC₁₅, lateral force of pelvis, bending moment of upper leg) were validated to be accurate and reliable according to the confusion matrix with the precision of more than 80% and the receiver operating characteristic curves (ROC) with the under area more than 85%. Based on decision tree models, not only the effects of front-end structural parameters on the corresponding injury but also the interaction mechanism between various variables can be clearly interpreted. Each route from the same root node to hierarchical middle nodes then to various leaf nodes represented a decision-making process. And the different branches under the same decision node directly illustrated the correlation between variables, which is highly readable and comprehensible. During the safety performance design of front-end structures, the rational value of variables could be decided according to decision routes that resulted in lower injury levels; Even if the accident was inevitable, the collision parameters could be controlled within a certain range for the least injury according to the prediction rules. Based on the novel framework coupling Monte Carlo sampling and automatic operation, it's foreseeable to apply the parametric and standard car-to-e-bike collision models to develop the virtual test system and to optimize front-end shapes for rider's protection.

Injury Metrics for Assessing the Risk of Acute Subdural Hematoma in Traumatic Events

Worldwide, the ocurrence of acute subdural hematomas (ASDHs) in road traffic crashes is a major public health problem. ASDHs are usually produced by loss of structural integrity of one of the cerebral bridging veins (CBVs) linking the parasagittal sinus to the brain. Therefore, to assess the risk of ASDH it is important to know the mechanical conditions to which the CBVs are subjected during a potentially traumatic event (such as a traffic accident or a fall from height). Recently, new studies on CBVs have been published allowing much more accurate prediction of the likelihood of mechanical failure of CBVs. These new data can be used to propose new damage metrics, which make more accurate predictions about the probability of occurrence of ASDH in road crashes. This would allow a better assessement of the effects of passive safety countermeasures and, consequently, to improve vehicle restraint systems. Currently, some widely used damage metrics are based on partially obsolete data and measurements of the mechanical behavior of CBVs that have not been confirmed by subsequent studies. This paper proposes a revision of some existing metrics and constructs a new metric based on more accurate recent data on the mechanical failure of human CBVs.

Leveraging machine learning for predicting human body model response in restraint design simulations

The objective of this study was to leverage and compare multiple machine learning techniques for predicting the human body model response in restraint design simulations. Parametric simulations with 16 independent variables were performed. Ordinary least-squares (OLS), least absolute shrinkage and selection operator (LASSO), neural network (NN), support vector regression (SVR), regression forest (RF), and an ensemble method were used to develop response surface models of the simulations. The hyperparameters of the machine learning techniques were optimized through grid search and cross-validation to avoid under-fitting and over-fitting. The ensemble method outperformed other techniques, followed by LASSO, SVR, NN, RF, and OLS. Findings indicated that optimizing the metamodel hyper-parameters are essential to predict the optimum set of restraint design parameters.