Will Advanced Driving Assistance Systems (ADAS) and Highly Automated Driving (HAD) perform in the expected manner? Will they actually make road traffic safer, or will they potentially introduce new critical situations or road accidents? It is almost impossible to address these questions solely through real-world tests. A promising tool to provide appropriate answers in a time- and cost-efficient way without exposing subjects to risk are virtual assessment methods. Reliable safety assessments are only possible, if the traffic simulations provide realistic traffic, including critical situations and road accidents. This paper provides a review of how human error contributes to critical situations and accidents in road traffic. The focus is on the causes and mechanisms of human error, which driver behavior models must address in order to simulate realistic traffic. For this purpose, Rasmussen's error taxonomies are applied to the traffic context and extended with further research. The paper shows the causes of those human errors and that the underlying mechanisms thereof should be taken into account in order to obtain more transparent and realistic driver behavior models. It is shown, which concepts for modelling realistic traffic exist and how virtual safety assessment could benefit from this development. In addition, the driver behavior model DReaM (Driver Reaction Model) is presented to address the issues resulting from existing cognitive driver models.