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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.

An automated driving system (ADS) shall provide safer conditions for highly automated vehicle (HAV) users compared to standard vehicles since human error is excluded. In the following decades, however, one can expect a mixed fleet of both standard and automated vehicles on roads. Therefore, collisions between manually driven cars and HAVs are to be expected. On the other hand, HAVs’ occupants access more room in the vehicle which allows them to rotate their seats to have a comfortable position. This work aims to address the issue of HAV’s occupant safety using tools of numerical simulations. We consider an FE model of a seat with the standard three-point belt at two initial orientations 45° and 90°. The occupant (50th percentile male) is represented with the Virthuman model. We test the idea of employing the active seat rotation system. By detecting a crash well in time an initially rotated seat is reoriented into a standard seating orientation in a rear-end crash.

The aim of this study is to use numerical simulations for safety assessment of an innovative active head restraint system. This system was developed to protect the head and neck of an occupant in a car without a head airbag during a side impact. Its FE model is created and embedded it in a model of a small car with a side airbag. The dynamics of the head restraint activation are also taken into account. The virtual human body model Virthuman is used to represent occupants. The model is scaled for pre-selected human individuals to cover large numbers of occupants of different sizes. It extends conventional virtual evaluation of new safety designs via existing pre-defined mono-purpose side dummies and their FE models. The benefit of the head restraint system is evaluated in side impact scenarios inspired by the pole tests performed by EuroNCAP. Transversal impacts to a pole at 29 and 32 km/h are considered at 90° and 75° angles from driver and the opposite side.

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.

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.

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.

The paper concerns the development of a new scalable virtual human body model. The model has been developed to assess safety risk during various complex crash scenarios including impacts from different directions. The novel approach described couples the basic multi-body structure with deformable segments, resulting in short calculation time. Each multi-body structure segment carries the particular surface parts that are linked to the segment with non-linear springs representing the behavior of related soft tissues. The response of particular body segments (head, thorax, pelvis, lower extremities) is validated in known impact scenarios and the response of the model is tuned to the experimental corridors obtained from literature. The tuning process involved the adjustment of both model material and numerical parameters in order to get the correct response for all the tests.