Synthetic Distracted Driving (SynDD1) dataset for analyzing distracted behaviors and various gaze zones of a driver

Date
2022
Authors
Rahman, Mohammed Shaiqur
Venkatachalapathy, Archana
Sharma, Anuj
Wang, Jiyang
Gursoy, Senem Velipasalar
Anastasiu, David
Wang, Shuo
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arXiv
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Civil, Construction and Environmental EngineeringInstitute for Transportation
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, 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.
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This is a pre-print of the article Rahman, Mohammed Shaiqur, Archana Venkatachalapathy, Anuj Sharma, Jiyang Wang, Senem Velipasalar Gursoy, David Anastasiu, and Shuo Wang. "Dataset for Analyzing Various Gaze Zones and Distracted Behaviors of a Driver." arXiv preprint arXiv:2204.08096 (2022). DOI: 10.48550/arXiv.2204.08096. Attribution 4.0 International (CC BY 4.0) Copyright 2022 The Authors. Posted with permission.
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