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Current EuroNCAP test specifications attempt to predict pedestrian lower limb injury in a lateral impact with a rigid legform test device developed by the UK's TRL (Transportation Research Lab). Research shows that the measurements taken from this device (knee bending angle, knee shear, and upper tibia acceleration) do not necessarily correspond to accurate injury prediction. Recent research suggests that the primary improvement to the current test device would be a flexible legform, or one that has more biofidelity (i.e., simulates actual human lower limb response). The work presented in this paper first reviews current legforms developed for pedestrian impact testing, including the TRL impactor used in EuroNCAP tests, Honda's POLAR II pedestrian dummy, and JAMA/JARI's FLEX-PLI legform impactor. Component level testing shows the FLEX-PLI performance to be close to the human lower limb response corridors. However, there are still areas of potential improvement with this design.

Detailed finite element modeling of the human body offers a potential major enhancement to the prediction of injury risk during vehicle impacts. Currently, vehicle crash safety countermeasure development is based on a combination of testing with established anthropomorphic test devices (i.e., ATD or dummy) and a mixture of multi-body (dummy) and finite element (vehicle) modeling. If a relatively simple finite element model can be developed to capture additional information beyond the capabilities of the multi-body systems, it would allow improved countermeasure development through more detailed prediction of performance. A simpler finite element model of human bones could be developed if it were shown that less complex finite element material modeling provides sufficient prediction of long bone macro-level strength. This study investigates the importance of including material anisotropy in the finite element model of a human femur.

Automobile safety can be improved by anticipating a crash before it occurs and thereby providing additional time to deploy safety technologies. This requires an accurate, fast and robust pre-crash sensor that measures telemetry, discriminates between classes of objects over a range of conditions, and has sufficient range and area of coverage surrounding the vehicle. The sensor must be combined with an algorithm that integrates data to identify threat levels. No one sensor provides adequate information to meet these diverse and demanding requirements. However the requirements can be met with an optimal combination of multiple types of sensors. Previous work considered criteria for evaluating various sensors to find an optimal combination. This work presents test methods and results for selected sensors proposed for use in a pre-crash detection system.

The future of vehicle safety will benefit greatly from precrash detection - the ability of a motor vehicle to predict the occurrence of an accident before it occurs. There are many different sensor technologies currently available for pre-crash detection. However no single sensor technology has demonstrated enough information gathering capability within the cost constraints of vehicle manufacturers to be used as a stand alone device. A proposed solution consists of combining information from multiple sensors in an intelligent computer algorithm to determine accurate precrash information. In this paper, a list of sensors currently available on motor vehicles and those that show promise for future development is presented. These sensors are then evaluated based on cost, information gathering capability and other factors.

Compressible plastic foams are used throughout the interior and bumper systems of modern automobiles for safety enhancement and damage prevention. Consequently, modeling of foams has become very important for automobile engineers. To date, most work has focused on predicting foam performance up to approximately 80% compression. However, in certain cases, it is important to predict the foam under maximum compression, or ‘bottoming-out.’ This paper uses one such case-a thin low-density bumper foam impacted by a pedestrian leg-form at 11.1 m/s-to investigate the ‘bottoming-out’ phenomenon. Multiple material models in three different explicit Finite Element Method (FEM) packages (RADIOSS, FCRASH, and LS-DYNA) were used to predict the performance. The finite element models consisted of a foam covered leg-form impacting a fixed bumper beam with a foam energy absorber.