  The use of virtual safety assessment as the primary method for evaluating
vehicle safety technologies has emphasized the importance of crash scenario
generation. One of the most common crash types is the rear-end crash, which
involves a lead vehicle and a following vehicle. Most studies have focused on
the following vehicle, assuming that the lead vehicle maintains a constant
acceleration/deceleration before the crash. However, there is no evidence for
this premise in the literature. This study aims to address this knowledge gap
by thoroughly analyzing and modeling the lead vehicle's behavior as a first
step in generating rear-end crash scenarios. Accordingly, the study employed a
piecewise linear model to parameterize the speed profiles of lead vehicles,
utilizing two rear-end pre-crash/near-crash datasets. These datasets were
merged and categorized into multiple sub-datasets; for each one, a multivariate
distribution was constructed to represent the corresponding parameters.
Subsequently, a synthetic dataset was generated using these distribution models
and validated by comparison with the original combined dataset. The results
highlight diverse lead-vehicle speed patterns, indicating that a more accurate
model, such as the proposed piecewise linear model, is required instead of the
conventional constant acceleration/deceleration model. Crashes generated with
the proposed models accurately match crash data across the full severity range,
surpassing existing lead-vehicle kinematics models in both severity range and
accuracy. By providing more realistic speed profiles for the lead vehicle, the
model developed in the study contributes to creating realistic rear-end crash
scenarios and reconstructing real-life crashes.
