  The most common type of accident on the road is a rear-end crash. These
crashes have a significant negative impact on traffic flow and are frequently
fatal. To gain a more practical understanding of these scenarios, it is
necessary to accurately model car following behaviors that result in rear-end
crashes. Numerous studies have been carried out to model drivers' car-following
behaviors; however, the majority of these studies have relied on simulated
data, which may not accurately represent real-world incidents. Furthermore,
most studies are restricted to modeling the ego vehicle's acceleration, which
is insufficient to explain the behavior of the ego vehicle. As a result, the
current study attempts to address these issues by developing an artificial
intelligence framework for extracting features relevant to understanding driver
behavior in a naturalistic environment. Furthermore, the study modeled the
acceleration of both the ego vehicle and the leading vehicle using extracted
information from NDS videos. According to the study's findings, young people
are more likely to be aggressive drivers than elderly people. In addition, when
modeling the ego vehicle's acceleration, it was discovered that the relative
velocity between the ego vehicle and the leading vehicle was more important
than the distance between the two vehicles.
