This article presents a synthetic distracted driving (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 for the classification of
various distracting activities and gaze zones of drivers.

本文介绍了 SynDD1 数据集，它是用于检测和分析驾驶员各种分心行为和不同注视区域的机器学习模型的人工合成数据集。研究人员可以使用此数据集评估机器学习算法用于分类驾驶员各种分心活动和注视区域的性能。