In Europe approximately 1250 children younger than 15 years of age die in traffic each year. The number of children severely injured in traffic is dramatically higher. Within the ECE-R44 regulation the safety of children in cars has been regulated by means of certification of child restraint systems (CRS). Much has been achieved, but further reduction of injuries seems possible. The ECE-R44 regulation provides a simplified set up and test configuration, that may be different from the real-world environment in which a child is injured.

In this study, a virtual testing approach was followed to explore the effect of one particular aspect, i.e. the posture of a child in a CRS, on the injury potential in a typical car crash. The investigation focussed on the vulnerable child population seated in ECE-R44 Group I seats. A photo-study was performed with 10 children in the age group from one to three years. Their positions were recorded on short and longer drives. Few children remained seated in the standard position. Most children slouched, slanted and turned their head and rested it on the side-support of the CRS. Extreme positions such as leaning forward, escaping from the harness or holding feet were observed. In the MADYMO simulation environment a non-deforming finite element model of a CRS was combined with multi-body models of Q1.5 and Q3 dummies and of human child models representing 1.5 and 3-year-olds. They were set up in realistic poses. The dummy models were adapted to enable these poses, while the human models were used to compare the biofidelity performance. From the simulated response between the ECE-R44 prescribed position and various common and extreme positions children were found to be in, it was shown that children are at an increased risk in relatively common positions. High lateral neck loads were observed in slanted positions, while correctly restrained children that managed to escape from their shoulder harness sustained large amounts of head excursion. Virtual testing was shown to be a valuable tool to predict trends in situations that are more closely related to the actual automotive environment than current regulations or hardware testing do.