The results of this study support the development of procedures for virtual testing with Human Body Models for far-side assessments by having a closer look at the field data.

Datasets from the US (Crash Injury Research and Engineering Network (CIREN) and Crash Investigation Sampling System (CISS)) and Europe (German In-Depth Accident Study (GIDAS) and Central Database for In-Depth Accident Study (CEDATU)) were analysed to identify who is injured in far-side scenarios and how. Parameters of the crash scenarios, the injured persons, and the injury types were analysed. A bias towards females was observed in GIDAS, CISS and CIREN cases, but no significant difference in terms of injury risk was observed. Thoracic and head injuries were found most often in all analysed datasets. Abdominal injuries were also common. No clear trend in terms of anthropometries was visible in any of the datasets. Average BMIs were comparable between the different datasets, heights and injured body regions. While head injuries were more often related to taller occupants, thoracic injuries were most relevant throughout all height groups.

The results indicate that ideally, a wide range of anthropometries should be considered in the virtual assessments, as no clear trends on the most vulnerable populations were identified in the field data.