  Safety assessment of crash and conflict avoidance systems is important for
both the automotive industry and other stakeholders. One type of system that
needs such an assessment is a driver monitoring system (DMS) with some
intervention (e.g., warning or nudging) when the driver looks off-road for too
long. Although using computer simulation to assess safety systems is becoming
increasingly common, it is not yet commonly used for systems that affect driver
behavior, such as DMSs. Models that generate virtual crashes, taking
crash-causation mechanisms into account, are needed to assess these systems.
However, few such models exist, and those that do have not been thoroughly
validated on real-world data. This study aims to address this research gap by
validating a rear-end crash-causation model which is based on four
crash-causation mechanisms related to driver behavior: a) off-road glances, b)
too-short headway, c) not braking with the maximum deceleration possible, and
d) sleepiness (not reacting before the crash). The pre-crash kinematics were
obtained from the German GIDAS in-depth crash database. Challenges with the
validation process were identified and addressed. Most notably, a process was
developed to transform the generated crashes to mimic the crash severity
distribution in GIDAS. This step was necessary because GIDAS does not include
property-damage-only (PDO) crashes, while the generated crashes cover the full
range of severities (including low-severity crashes, of which many are PDOs).
Our results indicate that the proposed model is a reasonably good crash
generator. We further demonstrated that the model is a valid method for
assessing DMSs in virtual simulations; it shows the safety impact of shorter
"longest" off-road glances. As expected, cutting away long off-road glances
substantially reduces the number of crashes that occur and reduces the average
delta-v.
