  This article presents a synthetic distracted driving (SynDD2 - a continuum of
SynDD1) dataset for machine learning models to detect and analyze drivers'
various distracted behavior and different gaze zones. We collected the data in
a stationary vehicle using three in-vehicle cameras positioned at locations: on
the dashboard, near the rearview mirror, and on the top right-side window
corner. The dataset contains two activity types: distracted activities and gaze
zones for each participant, and each activity type has two sets: without
appearance blocks and with appearance blocks such as wearing a hat or
sunglasses. The order and duration of each activity for each participant are
random. In addition, the dataset contains manual annotations for each activity,
having its start and end time annotated. Researchers could use this dataset to
evaluate the performance of machine learning algorithms to classify various
distracting activities and gaze zones of drivers.
