  Generating representative rear-end crash scenarios is crucial for safety
assessments of Advanced Driver Assistance Systems (ADAS) and Automated Driving
systems (ADS). However, existing methods for scenario generation face
challenges such as limited and biased in-depth crash data and difficulties in
validation. This study sought to overcome these challenges by combining
naturalistic driving data and pre-crash kinematics data from rear-end crashes.
The combined dataset was weighted to create a representative dataset of
rear-end crash characteristics across the full severity range in the United
States. Multivariate distribution models were built for the combined dataset,
and a driver behavior model for the following vehicle was created by combining
two existing models. Simulations were conducted to generate a set of synthetic
rear-end crash scenarios, which were then weighted to create a representative
synthetic rear-end crash dataset. Finally, the synthetic dataset was validated
by comparing the distributions of parameters and the outcomes (Delta-v, the
total change in vehicle velocity over the duration of the crash event) of the
generated crashes with those in the original combined dataset. The synthetic
crash dataset can be used for the safety assessments of ADAS and ADS and as a
benchmark when evaluating the representativeness of scenarios generated through
other methods.
