Synthetic Distracted Driving (SynDD2) dataset for analyzing distracted behaviors and various gaze zones of a driver
This dataset enables researchers to evaluate machine learning algorithms for classifying distracted activities and gaze zones in drivers, but it is incremental as it builds upon the earlier SynDD1 dataset.
The authors introduced the SynDD2 dataset, a synthetic collection of driver behaviors and gaze zones captured with three in-vehicle cameras, to address the problem of detecting and analyzing distracted driving, providing manual annotations for activities with random order and duration.
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.