Citation

Rahman MS, Venkatachalapathy A, Sharma A, Wang J, Gursoy SV, Anastasiu D, Wang S. Data Brief 2023; 46: e108793.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.dib.2022.108793

PMID

36506800

PMCID

PMC9730022

Abstract

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 [1], [2], [3], and gaze zones [4], [5], [6] 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.

Language: en

Keywords

Driver distraction; Driver behavior; Activity analysis; Activity recognition; Head orientation