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Traffic accidents cause one of the highest numbers of severe injuries in the whole population. The numbers of deaths or seriously injured citizens prove that traffic accidents and their consequences are still a serious problem to be solved. A lot of effort is devoted to both passive and active safety systems development. The transportation standards usually define safety requirements by regulations (e.g. ECE-R94, 96/79/EC and ECE-R95, 96/27/EC in Europe) with specific dummies for children to be used. The dummies include hardware sensors for monitoring accelerations, loads and other signals and each dummy is developed for a specific scenario, but there are limitations of these dummies, such as only a specific age or calibration just for a specific test.

Human-driven vehicles are going to be replaced by highly automated vehicles as one of the future mobility trends. Even though highly automated vehicles’ active safety systems can protect against vehicle-to-vehicle accidents, the traffic mix between human-driven vehicles and highly automated vehicles is still a potential source of vehicle collisions. Additionally, occupants in highly automated vehicles will be passengers not necessarily dealing with driving anymore, so there will be a considerable number of non-standard seating configurations. Those configurations are not able to be assessed for safety by hardware testing due to their number, variability and complexity. The objective of the paper is the development of a fast virtual approach to identify the passengers’ injury risk in non-standard seating configurations under multi-directional impact scenarios and severity.

In this paper a novel approach in developing a simplified model of a vehicle front-end is presented. Its surface is segmented to form an MBS model with hundreds of rigid bodies connected via translational joints to a base body. Local stiffness of each joint is calibrated using a headform or a legform impactor corresponding to the EuroNCAP mapping. Hence, the distribution of stiffness of the front-end is taken into account. The model of the front-end is embedded in a whole model of a small car in a simulation of a real accident. The VIRTHUMAN model is scaled in height, weight and age to represent precisely the pedestrian involved. Injury risk predicted by simulation is in correlation with data from real accident. Namely, injuries of head, chest and lower extremities are confirmed. Finally, mechanical response of developed vehicle model is compared to an FE model of the same vehicle in a pedestrian impact scenario.

The paper contributes to the development of virtual biomechanical human models as a support for design and optimization of both passive and active safety systems used in various modes of transportation. The paper shows the scaling methodology as simply as possible to creating models based on a reference model regarding anthropology and flexibility. The paper describes the methodology for the scaling of hybrid human models based on a multi-body structure carrying deformable parts. The idea is to have a reference model and to create other models automatically based on the least possible number of parameters. The developed method takes into account the height, age, mass and flexibility of joints. The scaling process starts by scaling the reference model to the target height for given age (including correct height for each major segment of the human body) based on the available anthropometrical data. The scaling coefficient for each segment is also used to scale the segment mass.

In this work we present the VIRTHUMAN model as a tool for injury risk assessment in pedestrian crash scenarios. It is a virtual human body model formed of a multibody structure and deformable segments to account for the mechanical response of soft tissues. Extensive validation has been performed to ensure its biofidelity. Due to the scaling algorithm implemented, variations in the human population in terms of height, weight, gender and age can be considered. Assessment of the injury risk is done via automatic evaluation software developed. Injury criteria for individual body parts are evaluated using accelerations, forces and displacements of certain points. Injury risk is indicated by the colour of particular body parts in accordance with NCAP rating. A real accident is investigated in this work. A 60-year-old female was hit laterally by a passenger vehicle with the impact velocity of 40 km/h. The accident is reconstructed using VIRTHUMAN as pedestrian representative.

Road traffic accidents cause one of the highest numbers of severe injuries. Approximately 1.25 million people die each year as a result of road traffic crashes and between 20 and 50 million more people suffer non-fatal injuries, with many incurring a disability. Nearly half of those dying on the roads are so-called vulnerable road users, namely pedestrians, cyclists and two-wheeler riders including motorcyclists. Those vulnerable road users usually undergo complex kinematics and complex loading caused by the other vehicle impact. Virtual human body biomechanical models play an important role to assess the injuries during the impact loading especially for scenarios, where complex dynamical loading is taken into account. An additional benefit of some virtual human models is their scalability, so that they can assess the injury risk for the particular subject taking into account a wide spectrum of the whole population.

The paper contributes to the field of vehicle safety technology by the virtual approach using biomechanical virtual human body models. The goal of the paper is to exploit the previously developed scaling algorithm to create several virtual human models of a given age and body proportions and to assess the impact analysis using the sensitivity approach. Based on a validated reference model, the previously developed scaling algorithm develops virtual human body models for given height, mass, age and gender. Particular body segments are scaled based on the anthropometrical database concerning the body dimensions taking also percentiles into account. The body stiffness is driven by age dependent flexindex. Several virtual models of human bodies representing particular cadavers were generated via the automatic scaling algorithm. The frontal sled test response of three models was successfully compared to the available experimental data previously.