Are riders of electric two-wheelers safer than bicyclists in collisions with motor vehicles?

Trajectory planning framework for autonomous vehicles based on collision injury prediction for vulnerable road users

Due to the escalating occurrence and high casualty rates of accidents involving Electric Two-Wheelers (E2Ws), it has become a major safety concern on the roads. Additionally, with the widespread adoption of current autonomous driving technology, a greater challenge has arisen for the safety of vulnerable road participants. Most existing trajectory planning methods primarily focus on the safety, comfort, and dynamics of autonomous vehicles themselves, often overlooking the protection of vulnerable road users (VRUs), typically E2W riders. This paper aims to investigate the kinematic response of E2Ws in vehicle collisions, including the 15 ms Head Injury Criterion (HIC15). It analyzes the impact of key collision parameters on head injuries, establishes injury prediction models for anticipated scenarios, and proposes a trajectory planning framework for autonomous vehicles based on predicting head injuries of VRUs. Firstly, a multi-rigid-body model of two-wheeler-vehicle collision was established based on a real accident database, incorporating four critical collision parameters (initial collision velocity, initial collision position, and collision angle). The accuracy of the multi-rigid-body model was validated through verifications with real fatal accidents to parameterize the collision scenario. Secondly, a large-scale effective crash dataset has been established by the multi-parameterized crash simulation automation framework combined with Monte Carlo sampling algorithm. The training and testing of the injury prediction model were implemented based on the MLP + XGBoost regression algorithm on this dataset to explore the potential relationship between the head injuries of the E2W riders and the crash variables. Finally, based on the proposed injury prediction model, this paper generated a trajectory planning framework for autonomous vehicles based on head collision injury prediction for VRUs, aiming to achieve a fair distribution of collision risks among road users. The accident reconstruction results show that the maximum error in the final relative positions of the E2W, the car, and the E2W rider compared to the real accident scene is 11 %, demonstrating the reliability of the reconstructed model. The injury prediction results indicate that the MLP + XGBoost regression prediction model used in this article achieved an R2 of 0.92 on the test set. Additionally, the effectiveness and feasibility of the proposed trajectory planning algorithm were validated in a manually designed autonomous driving traffic flow scenario.

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.

A data-mining study on the prediction of head injury in traffic accidents among vulnerable road users with varying body sizes and head anatomical characteristics

Body sizes and head anatomical characteristics play the major role in the head injuries sustained by vulnerable road users (VRU) in traffic accidents. In this study, in order to study the influence mechanism of body sizes and head anatomical characteristics on head injury, we used age, gender, height, and Body Mass Index (BMI) as characteristic parameters to develop the personalized human body multi-rigid body (MB) models and head finite element (FE) models. Next, using simulation calculations, we developed the VRU head injury dataset based on the personalized models. In the dataset, the dependent variables were the degree of head injury and the brain tissue von Mises value, while the independent variables were height, BMI, age, gender, traffic participation status, and vehicle speed. The statistical results of the dataset show that the von Mises value of VRU brain tissue during collision ranges from 4.4 kPa to 46.9 kPa at speeds between 20 and 60 km/h. The effects of anatomical characteristics on head injury include: the risk of a more serious head injury of VRU rises with age; VRU with higher BMIs has less head injury in collision accidents; height has very erratic and nonlinear impacts on the von Mises values of the VRU's brain tissue; and the severity of head injury is not significantly influenced by VRU's gender. Furthermore, we developed the classification prediction models of head injury degree and the regression prediction models of head injury response parameter by applying eight different data mining algorithms to this dataset. The classification prediction models have the best accuracy of 0.89 and the best R2 value of 0.85 for the regression prediction models.

Analysis of the consequences of car to micromobility user side impact crashes

INTRODUCTION

The strong rise in modes of travel commonly referred to as micromobility has changed the mobility patterns and lifestyles in cities worldwide, especially after the COVID-19 pandemic. It has led to a significant increase in the number of crashes involving these types of vehicles, especially bicycles and stand-up e-scooters. The risk of crashes is higher at intersections where motor-vehicles perform a turning maneuver crossing a bike lane.

METHOD

The consequences of a passenger car-to-micromobility vehicle side-impact crashes, considering both bicycle and e-scooter, were studied based on the results of the simulation of several scenarios with PC-Crash software. Two injury criteria were applied: Head Injury Criterion (HIC15) and 3 ms chest acceleration criterion.

RESULTS

When motor-vehicle speed is lower than 50 km/h, the 3 ms chest acceleration never exceeds the 60 g threshold. However, at 50 km/h, it is close to 50 g in the case of e-scooter rides. At this speed, HIC15 is considerably greater than 1000, both for bicycles and for e-scooters, and the safety margin of 700 is exceeded at 45 km/h for e-scooters.

CONCLUSIONS

In case of motor vehicle-to-micromobility vehicle side-impact crash, riding a bicycle is safer than riding an e-scooter since the observed HIC15 experienced by the cyclists is lower than that experienced by the e-scooter rider when motor vehicle speed is greater than 30 km/h.

PRACTICAL APPLICATIONS

To reduce micromobility users injury risk at intersections, motor vehicle speed limit should be equal or lower than 40 km/h. At this impact speed, the activation of hood or bumper airbags could be justified.

Ocular injuries associated with two-wheeled electric transportation devices and motorcycle accidents

Electric bicycles and scooters have gained popularity among riders; studies assessing these device-related injuries have not specified ocular trauma. Our study examined the types and risk factors for ocular and periocular injuries associated with electric devices compared to motorcycle accidents. The study was conducted on the National Trauma Registry database from 20 trauma centers, including patients involved in accidents with electric bicycles, scooters, and motorcycles between 2014 to 2019. Injured riders were assigned into two groups: motorcycle group (M) and electric bicycle & scooter group (E). Data such as gender, age, protective gear use, ocular injury type, injury severity score (ISS), and ocular surgery were captured. Logistic regression models were conducted for injury types and the need for surgery. 8181 M-riders and 3817 E-riders were involved in an accident and hospitalized. E-riders suffered from ocular injury more than M-riders. Males were most vulnerable and the ages of 15-29. Orbital floor fracture was the most common injury, followed by ocular contusion, eyelid laceration, and other ocular wounds. Electric bicycle and scooter riders are more likely to suffer from ocular injury than motorcycle riders. Riders without helmets are at greater risk for injuries, specifically orbital floor fractures. ISS of 16 + was associated with injury demanding ocular surgery.

Numerical investigation of the rider's head injury in typical single-electric self-balancing scooter accident scenarios

As the use of electric self-balancing scooters (ESSs) increases, so does the number of related traffic accidents. Because of the special control method, mechanical structure and driving posture, ESSs are prone to various single-vehicle accidents, such as collisions with fixed obstacles and falls due to mechanical failures. In various ESS accident scenarios, the rider's head injury is the most frequent injury type. In this study, several typical single-ESS accident scenarios are reconstructed via computational methods, and the risk of riders' head/brain injury is assessed in depth using various injury criteria. Results showed that two types of ESSs (solo- and two-wheeler) do not have clear differences in head kinematics and head injury risks; the head kinematics (or falling posture) and ESS accident scenario exhibit a distinct effect on the head injury responses; half of the analysed ESS riders have a 50% probability of skull fracture, a few riders have a 50% risk of abbreviated injury scale (AIS) 4+ brain injury, and none has a diffuse axonal injury; the ESS speed plays an important role in producing the head/brain injury in ESS riders, and generally, higher ESS speed generates higher level of predicted head injury parameters. These findings will provide theoretical support for preventing head injury among ESS riders and data support for developing and legislating ESSs.

Should anthropometric differences between the commonly used pedestrian computational biomechanics models and Chinese population be taken into account when predicting pedestrian head kinematics and injury in vehicle collisions in China?

Computational biomechanics models play a key role in predicting/evaluating pedestrian head kinematics and injury risk in car-to-pedestrian collisions. The human multibody models most commonly used in car-to-pedestrian collision reconstruction, such as pedestrian model by The Netherlands Organisation for Applied Scientific Research TNO, are built using the anthropometry of Western European population as defined in TNO (2013) human multibody model database. In this study, we investigate the effects of the anthropometric differences between the Western European and Chinese populations on the pedestrian head kinematics and injury responses predicted using multibody models. The comparison was conducted through car-to-pedestrian collision simulations using pedestrian multibody models representing anthropometric characteristics of Western European and Chinese populations, three typical vehicle shapes (sedan, SUV and minivan), five initial vehicle impact speeds (30, 35, 40, 45, 50 km/h), and six pedestrian walking postures. The results indicate that the change of pedestrian model anthropometry (from Western European to Chinese) exerts appreciable effects on both the predicted initial boundary conditions of the head-to-windscreen impact (in particular the head-to-windscreen impact angle) and the head injury indices in the impact with the road surface (secondary impact).

Can emergency avoidance behavior reduce injuries to electric two-wheeler riders in vehicle collisions?

OBJECTIVES

The aim of this study was to examine the effects of emergency avoidance behaviors on the kinematics and injuries of electric two-wheeler (ETW) riders.

METHODS

Four typical riding postures of ETW riders before collisions, including one normal posture and three avoidance postures, were identified through analysis of 298 videos of vehicle to ETW accidents. Crash simulations were then performed using the Total Human Model of Safety (THUMS) occupant model, ETW and a sedan finite element (FE) model, and the kinematics of ETW riders were compared. The risk of head injury and lower extremity injury was also investigated.

RESULTS

When the struck foot position of the ETW rider was lower than the ETW pedal, the lower extremity was struck by the sedan bumper and ETW frame from the right and left side respectively, and the upper body of the rider rotated around the hood leading edge. At a car velocity of 40 km/h, the rider was at high risk of head injury and the tibia was fractured. The medial cruciate ligament (MCL) was ruptured in both the 20 km/h and 40 km/h collisions. When the struck foot position of the ETW rider was higher than the pedal, the lower extremity was hit by the bumper and then rebounded. In this situation, the bending moments of the femur and tibia, as well as the bending angle and shear displacement of the knee joint were less than the injury threshold in all crash simulations. Furthermore, when the head was turned toward the colliding car, the risk of head injury varied with the emergency avoidance posture.

CONCLUSIONS

The height of the struck foot relative to the ETW pedal influenced the rider's global kinematics, and head and lower extremity injuries risk. In the struck side foot landing and both feet landing postures, the lower extremity was restrained and compressed by the ETW frame, resulting in a high risk of tibia fracture and MCL rupture. Reducing the impact velocities could effectively mitigate the injury risk of the ETW riders; however, loading patterns remain an important factor influencing the risk of lower extremity injury.

Evaluation of Urban Traffic Accidents Based on Pedestrian Landing Injury Risks

In comparison with vehicle-to-pedestrian collision, pedestrian-to-ground contact usually results in more unpredictable injuries (e.g., intracranial, neck, and abdominal injuries). Although there are many studies for different applications of such methods, this paper conducts an in-depth analysis of urban traffic pedestrian accidents. The effects of pedestrian rotation angle (PRA) and pedestrian facing orientation (PFO) on head and neck injury risk in a ground contact are investigated by the finite element numerical models and different probabilistic analyses. It goes without saying that this study provides a theoretical basis for the prediction and protection study of pedestrian ground contact injury risk. In our experiments, 24 pedestrian-to-ground simulations are carried out by the THUMS v4.0.2 model considering eight PRAs (0°, 45°, 90°, 135°, 180°, 225°, 270°, 315°, 360°) and three PFOs (x+, x−, y+). Each test was simulated with loading the average linear and rotational velocities that obtained from real-world pedestrian accidents at the pedestrian’s center of gravity. The results show that both PRAs and PFOs have significant impacts on head and neck injuries. Head HIC value caused by PRA 0–135° is much higher than that caused by PRA 180–315°. Neck injury risk caused by PRA 180° is the greatest one in comparison with other PRAs. The PRAs 90° and 270° usually induce a relatively lower neck injury risk. For PFO, the risk of head and neck injury was lower than PFOy+ and PFOx+ or PFOx−, which means PFOy+ was a safer landing orientation for both head and neck. The potential risk of head and neck injuries caused by the ground contact was strongly associated with the symmetry/asymmetric features of human anatomy.

Risk Factors for Road-Traffic Injuries Associated with E-Bike: Case-Control and Case-Crossover Study

The Electric Bike (EB) has become an ideal mode of transportation because of its simple operation, convenience, and because it is time saving, economical and environmentally friendly. However, electric bicycle road-traffic injuries (ERTIs) have become a road-traffic safety problem that needs to be solved urgently, bringing a huge burden to public health. In order to provide basic data and a theoretical basis for the prevention and control of ERTIs in Shantou, mixed research combining a case-control study and a case-crossover study was carried out to investigate the cycling behavior characteristics and injury status of EB riders in Shantou city, and to explore the influencing factors of ERTI. The case-control study selected the orthopedic inpatient departments of three general hospitals in Shantou. The case-crossover study was designed to assess the effect of brief exposure on the occurrence of ERTIs, in which each orthopedic inpatient serves as his or her own control. Univariable and multivariable logistic regressions were used to examine the associated factors of ERTIs. In the case-control study, multivariable analysis showed that chasing or playing when cycling, finding the vehicle breakdown but continuing cycling, not wearing the helmet, and retrograde cycling were risk factors of ERTIs. Compared with urban road sections, suburb and township road sections were more likely to result in ERTIs. Astigmatism was the protective factor of ERTI. The case-crossover study showed that answering the phone or making a call and not wearing a helmet while cycling increased the risk of ERTIs. Cycling in the motor-vehicle lane and cycling on the sidewalk were both protective factors. Therefore, the traffic management department should effectively implement the policy about wearing a helmet while cycling, increasing the helmet-wearing rate of EB cyclists, and resolutely eliminate illegal behaviors such as violating traffic lights and using mobile phones while cycling. Mixed lanes were high-incidence road sections of ERTIs. It was suggested that adding people-non-motor-vehicles/motor vehicles diversion and isolation facilities in the future to ensure smooth roads and safety would maximize the social economic and public health benefits of EB.

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.

Assessment of standing passenger traumatic brain injury caused by ground impact in subway collisions

Human head is the most vulnerable region in subway collisions. To design a safer subway, the head impact biomechanical response should be studied first. This paper aims to investigate the standing passenger head-ground impact dynamic response and traumatic brain injury (TBI) in subway collisions. A standing passenger-subway interior dynamic model was numerically developed by using our previous validated finite element (FE)-multibody (MB) coupled human body model, which was integrated by the Total Human Model for Safety (THUMS) head-neck FE model and the extracted remaining body segments pedestrian MB model of TNO. A parametric study considering the handrail type, standing angle, and friction coefficient between the shoes and ground was performed. Results show that the passenger dynamic response could be divided into two categories according to whether the passenger hit handrails. Passenger TBIs severity could be efficiently alleviated by the passenger body (excluding the head) hitting the handrail first before head-ground impact. The probabilities of DAI in the cerebellum and brain stem were low. A statistical analysis of TBIs demonstrated that the risks of TBIs were sensitive to the handrail type in subway collisions, but did not to the standing angle and friction coefficient. This study provides practical help for improving the interior crashworthiness performance of subways.

Electric Bicyclist Injury Severity during Peak Traffic Periods: A Random-Parameters Approach with Heterogeneity in Means and Variances

Accidents involving electric bicycles, a popular means of transportation in China during peak traffic periods, have increased. However, studies have seldom attempted to detect the unique crash consequences during this period. This study aims to explore the factors influencing injury severity in electric bicyclists during peak traffic periods and provide recommendations to help devise specific management strategies. The random-parameters logit or mixed logit model is used to identify the relationship between different factors and injury severity. The injury severity is divided into four categories. The analysis uses automobile and electric bicycle crash data of Xi’an, China, between 2014 and 2019. During the peak traffic periods, the impact of low visibility significantly varies with factors such as areas with traffic control or without streetlights. Furthermore, compared with traveling in a straight line, three different turnings before the crash reduce the likelihood of severe injuries. Roadside protection trees are the most crucial measure guaranteeing riders’ safety during peak traffic periods. This study reveals the direction, magnitude, and randomness of factors that contribute to electric bicycle crashes. The results can help safety authorities devise targeted transportation safety management and planning strategies for peak traffic periods.

Study of typical electric two-wheelers pre-crash scenarios using K-medoids clustering methodology based on video recordings in China

Crash safety of electric two-wheelers (ETWs) has been one of the most important safety issues in China due to their high proportion of involvement in traffic accidents. Automated Emergency Braking (AEB) systems have proven to be effective in reducing the number of fatalities and injuries in traffic accidents. Providing test scenarios is one of the fundamental tasks required for establishing a set of AEB test programs for ETWs. Compared to traditional in-depth accident data, accident data accompanied with video recordings provide more accurate accident information prior to a crash as both the traffic environment and the crash process can be observed from the video. In this study, a set of typical AEB test scenarios for ETWs was developed using accident data with video information. Video recordings of 630 car-to-ETW crashes in China from 2010 to 2021 were selected from the VRU Traffic Accident database with Video (VRU-TRAVi). A K-medoids¹ cluster analysis was carried out based on variables including the collision time, visual obstruction, motion of the car and ETW before the collision, relative motion direction between the car and ETW, and the ETW type. The velocity information of cars and ETWs was also accounted for in each clustering scenario. Seven typical pre-crash scenarios were obtained, including five electric-scooter (E-scooter) scenarios (representing two scenarios where the ETWs are approaching the car from the left side, two scenarios where the ETWs are approaching the car in the same direction and another scenario where the ETWs are approaching the car in the opposite direction) and two electric-bike (E-bike) scenarios where the E-bikes are approaching the car in the perpendicular direction. Both E-bike scenarios are consistent with the E-scooter scenario except for the ETW type and velocity range; therefore, by combining the E-bike and E-scooter scenarios, five ETW scenarios were finally recommended as AEB test scenarios. By comparing with typical scenarios extracted based on the China In-Depth Accident Study (CIDAS) data and the China New Car Assessment Program (C-NCAP) test scenarios, the results show that future AEB test scenarios for ETWs should focus on scenarios with visual obstructions and scenarios where either the car or the ETW is turning, with a velocity range of 15-30 km/h for ETWs.

The Head AIS 4+ Injury Thresholds for the Elderly Vulnerable Road User Based on Detailed Accident Reconstructions

Compared with the young, the elderly (age greater than or equal to 60 years old) vulnerable road users (VRUs) face a greater risk of injury or death in a traffic accident. A contributing vulnerability is the aging processes that affect their brain structure. The purpose of this study was to investigate the injury mechanisms and establish head AIS 4+ injury tolerances for the elderly VRUs based on various head injury criteria. A total of 30 elderly VRUs accidents with detailed injury records and video information were selected and the VRUs’ kinematics and head injuries were reconstructed by combining a multi-body system model (PC-Crash and MADYMO) and the THUMS (Ver. 4.0.2) FE models. Four head kinematic-based injury predictors (linear acceleration, angular velocity, angular acceleration, and head injury criteria) and three brain tissue injury criteria (coup pressure, maximum principal strain, and cumulative strain damage measure) were studied. The correlation between injury predictors and injury risk was developed using logistical regression models for each criterion. The results show that the calculated thresholds for head injury for the kinematic criteria were lower than those reported in previous literature studies. For the brain tissue level criteria, the thresholds calculated in this study were generally similar to those of previous studies except for the coup pressure. The models had higher (>0.8) area under curve values for receiver operator characteristics, indicating good predictive power. This study could provide additional support for understanding brain injury thresholds in elderly people.

Dental and maxillofacial injuries associated with electric-powered bikes and scooters in Israel: A report for 2014-2019

BACKGROUND/AIMS

Electric-Powered Bikes and powered scooters present a new method of transportation and are becoming commonly used worldwide. However, the reports on traumatic dental injuries related to their use are scarce. The aim of this study was to report the frequency and severity of dental and maxillofacial injuries associated with electric-powered bikes and scooters in Israel between the years 2014 and 2019.

METHODS

This was a retrospective cohort study based on data from the Israeli National Trauma Registry (INTR). The INTR provides comprehensive data on hospitalized patients from all six Level I trauma centers (TC) and 15 of the 20 Level II TCs in Israel. All injured patients who were hospitalized due to a traffic collision between 2014 and 2019 were identified. The data for those hospitalized due to an e-bike or motorized scooter accident were extracted as well as for pedestrians who were injured as a result of a crash with these vehicles.

RESULTS

A total of 3,686 hospital admissions were related to electric-powered bikes and scooters. Of those, 378 (10.3%) were oral and maxillofacial injuries. Most of the oral and maxillofacial injuries were attributed to powered bikes (321 out of 378; 84.92%) and the rest to powered scooters. There was a constant increase in general as well as the oral and maxillofacial injuries during the study years. Almost 20% of the cases involved injuries to the teeth. Overall, 291 pedestrians were reported to be injured due to electric-powered bikes and scooters; 29 (9.97%) of them, suffered from oral and maxillofacial injuries. Most of those were children aged 0-15 years (41.38%) and elders older than 60 years (37.39%).

CONCLUSIONS

Trauma related to electric-powered bikes and scooters is an increasing concern. Dental professionals should be actively involved in educational and legislative efforts focusing on the prevention of e-bike and scooter-related injuries, in general, and specifically maxillofacial injuries.

Evaluation of injury thresholds for predicting severe head injuries in vulnerable road users resulting from ground impact via detailed accident reconstructions

The aim of this study was to evaluate the effectiveness of various head injury criteria and associated risk functions in prediction of vulnerable road users (VRUs) severe head injuries caused by ground impact during vehicle collisions. Ten VRU accidents with video information were reconstructed by using Chalmers Pedestrian Model, vehicle multi-body system models and the THUMS (Ver. 4.0.2) finite element model. The head kinematics were used to calculate injury risks for seven head kinematics-based criteria: head angular velocity and acceleration, linear acceleration, head injury criterion (HIC), head impact power (HIP) and two versions of brain injury criterion (i.e., BRIC and BrIC). In addition, the intracranial responses were used to estimate seven tissue injury criteria, Von Mises stress, shear stress, coup pressure (C.P.) and countercoup pressure (CC.P.), maximum principal strain (MPS), cumulative strain damage measure (CSDM), and dilatation damage measure (DDM). A review of the medical reports for all cases indicated that each individual suffered severe head injuries and died. The injury risks predicted through simulations were compared to the head injuries recorded in the medical or forensic reports. The results indicated that 75-100% of the reconstructed ground impact accidents injuries were correctly predicted by angular acceleration, linear acceleration, HIC, C.P., MPS and CSDM_0.15. Shear stress, CC.P. and CSDM_0.25 correctly predicted 50-75% of the reconstructed accidents injuries. For angular velocity, HIP, BRIC and BrIC, the injuries were correctly predicted for less than 50% of the reconstructed accidents. The Von Mises stress and DDM did not correctly predict any reconstructed accidents injuries. The results could help to understand the effectiveness of the brain injury criteria for future head injury evaluation.