Head injury remains one of the most frequent and severe injuries sustained by vehicle occupants, motorcyclists, pedestrians and cyclists in road accidents and account for approximately 40% of road fatalities in the European Union (EU). One essential requirement for reducing the incidence of fatal and severe head injuries is to develop head injury assessment methods that can accurately and comprehensively assess the potential head injury risk under a broad range of head impact conditions. At present, the most widely accepted method of assessing head injury risk in road safety research is the Head Injury Criterion (HIC). However, HIC only considers the injury risk to the head resulting from linear head accelerations. In an attempt to develop improved head injury criteria for specific mechanisms, 68 head impact conditions that occurred in motor sport, motorcyclist, American football and pedestrian accidents were re-constructed with a state of the art finite element (FE) human head model (ULP head model). Statistical regression analysis was then carried out on the head loading parameters from the accidents, such as the peak linear and rotational acceleration of the head, and predictions from the head model, such as the Von Mises stress or strain and pressure in the brain, in order to determine which of the investigated parameters provided the most accurate metrics for the injuries sustained in the real world head trauma under consideration. The results show that Von Mises shearing strain within the brain is much better correlated with moderate Diffuse Axonal Injuries (DAI) as HIC or acceleration peaks are. For severe DAI, however, this improvement is less important. Another significant improvement of injury prediction based on FE head model is the one related to skull fracture, for which the proposed criteria present a higher correlation factor than HIC. Finally, SubDural Haematomas (SDH) are also better predicted with the FE model than HIC even if improvement is still needed for this injury mechanism.