How much does the injury risk between average female and average male anthropometry differ? - A simulation study with open source tools for virtual crash safety assessments.

Differences in injury risk between females and males are often reported in field data analysis. The aim of this study was to investigate the differences in kinematics and injury risks between average female and male anthropometry in two exemplary use cases. A simulation study comprising the newly introduced VIVA+ human body models (HBM) was performed for two use cases. The first use case relates to whiplash associated disorders sustained in rear impacts and the second to femur fractures in pedestrians impacted by passenger cars as field data indicates that females have higher injury risk compared to males in these scenarios. Detailed seat models and a generic vehicle exterior were used to simulate crash scenarios close to those currently tested in consumer information tests. In the evaluations with one of the vehicle seats and one car shape the injury risks were equal for both models. However, the risk of the average female HBM for whiplash associated disorders was 1.5 times higher compared to the average male HBM for the rear impacts in the other seat and 10 times higher for proximal femur fractures in the pedestrian impacts for one of the two evaluated vehicle shapes.. Further work is needed to fully understand trends observed in the field and to derive appropriate countermeasures, which can be performed with the open source tools introduced in the current study.

Holistic pedestrian safety assessment for average males and females.

Objective: An integrated assessment framework that enables holistic safety evaluations addressing vulnerable road users (VRU) is introduced and applied in the current study. The developed method enables consideration of both active and passive safety measures and distributions of real-world crash scenario parameters. Methods: The likelihood of a specific virtual testing scenario occurring in real life has been derived from accident databases scaled to European level. Based on pre-crash simulations, it is determined how likely it is that scenarios could be avoided by a specific Autonomous Emergency Braking (AEB) system. For the unavoidable cases, probabilities for specific collision scenarios are determined, and the injury risk for these is determined, subsequently, from in-crash simulations with the VIVA+ Human Body Models combined with the created metamodel for an average male and female model. The integrated assessment framework was applied for the holistic assessment of car-related pedestrian protection using a generic car model to assess the safety benefits of a generic AEB system combined with current passive safety structures. Results: In total, 61,914 virtual testing scenarios have been derived from the different car-pedestrian cases based on real-world crash scenario parameters. Considering the occurrence probability of the virtual testing scenarios, by implementing an AEB, a total crash risk reduction of 81.70% was achieved based on pre-crash simulations. It was shown that 50 in-crash simulations per load case are sufficient to create a metamodel for injury prediction. For the in-crash simulations with the generic vehicle, it was also shown that the injury risk can be reduced by implementing an AEB, as compared to the baseline scenarios. Moreover, as seen in the unavoidable cases, the injury risk for the average male and female is the same for brain injuries and femoral shaft fractures. The average male has a higher risk of skull fractures and fractures of more than three ribs compared to the average female. The average female has a higher risk of proximal femoral fractures than the average male. Conclusions: A novel methodology was developed which allows for movement away from the exclusive use of standard-load case assessments, thus helping to bridge the gap between active and passive safety evaluations.

Are There Any Significant Differences in Terms of Age and Sex in Pedestrian and Cyclist Accidents?

This study has analyzed sex-specific differences in pedestrian and cyclist accidents involving passenger cars. The most frequently injured body regions, types of injuries, which show sex-specific differences and the general accident parameters of females and males were compared. Accident data from three different European countries (Austria, Netherlands, Sweden) were analyzed. The current analysis shows that for both, females and males, pedestrian and cyclist injuries are sustained mainly to the body regions head, thorax, upper extremities and lower extremities. The results show that the odds for sustaining skeletal injuries to the lower extremities (incl. pelvis) in females are significantly higher. It was observed in all datasets, that the odds of females being involved in a rural accident or an accident at night are lower than for males. Elderly pedestrian and cyclist (≥60YO) tend to sustain more severe injuries (AIS2+ and AIS3+) than younger pedestrian and cyclists (<60YO) in some of the datasets. The findings of this study highlight the differences in males and females in both, accident scenarios and sustained injuries. Further investigations are needed to distinguish between gender- and sex-specific differences causing the different injury patterns.

Analysis of Swedish and Dutch accident data on cyclist injuries in cyclist-car collisions.

Objective: To reduce the number of severe injuries sustained by cyclists in crashes with vehicles, it is important to understand which kinds of injuries are occurring to identify what should be assessed by means of virtual testing.Method: A detailed analysis of injuries was made based on Swedish and Dutch accident data. The most frequently injured body regions and the most frequent single injuries of these body regions were analysed.Results: Cyclists most frequently injured their heads, upper and lower extremities, and bone fractures as well as brain injuries were identified as one of the most important injuries.Conclusions: For the virtual assessment of cyclist protection, injury predictors for long bone, skull and pelvic fractures as well as brain injuries are required in Human Body Models.