Frontal collisions between cars and trucks lead to high fatality rate of the car driver. Therefore the Japanese road administration established a directive, conformity to ECE-R.93 (2000/40/EC), compulsory since September 1st, 2011. As known, this directive describes a ‘rigid’ Front Underrun Protection (FUP) device installed on a truck. New developments are in the direction of energy absorbing devices in order to manage more severe impacts between both vehicles. The question is how to estimate the effectiveness of these devices.

Using a virtual car fleet, the effect of different FUP devices installed on or integrated with a truck front end can be estimated by simulation, in terms of injury severity and crash severity. The relationship between both makes it possible to estimate injury severity via crash severity. By transferring injury severity to AIS scale and fatality rate, a coupling can be made with real accidents and their effects on injuries. The other subject is to indicate the car severity by replacing a specific car fleet to a general device, in order to simplify the evaluation. The paper shows the steps from the simulations, to the analyses and simplifications, transfer to AIS scale and mapping on the real accident database, to predict the reduction of fatalities by using different types of energy absorbing FUPs (e.a.FUP).

In order to represent the car fleet, the Moving Progressive Deformable Barrier (MPDB) was selected. The MPDB was modelled to collide to a truck with an e.a.FUP. By this method, number of fatalities, or fatality reduction rate of the car for a certain e.a.FUP was estimated from the MPDB crash severity.

The processes in this study are based on simulations and accident investigation and analysis. The vehicle models used in the simulations are mainly validated on NCAP frontal impact tests. Some cars were validated at higher speeds, up to 90 km/h.

In this paper the prediction of injury levels is only based on the HIC to show the concept/principle of the method, but the method can be extended with other injury parameters.

The method described in this paper uses the Acceleration Severity Index (ASI) of a car-to-truck frontal collision in order to determine the probability of injury and fatalities. It uses AIS scaling and mapping on a matrix of relevant car to truck accidents. This simplified method can be applied to predict the e.a.FUP effectiveness in terms of injury reduction, and especially the fatality reduction.