AI Article Synopsis
- The article introduces a new dataset called SynDD1, designed for machine learning models to study distracted driving behaviors and eye movements of drivers.
- Data was collected using three cameras in a stationary vehicle, capturing various distracted activities and gaze zones, with participants engaging in random activities while wearing different appearance blocks, like hats or sunglasses.
- Each recorded activity is manually annotated with start and end times, allowing researchers to test and improve machine learning algorithms focused on identifying distractions in drivers.
Article 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.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9730022 | PMC |
| http://dx.doi.org/10.1016/j.dib.2022.108793 | DOI Listing |
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