Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Synthetic Distracted Driving (SynDD1) dataset for analyzing distracted
  behaviors and various gaze zones of a driver   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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The categories of this paper are: nucl-th
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The categories of this paper are: eess
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The categories of this paper are: hep-lat
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The categories of this paper are: solv-int
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The categories of this paper are: dg-ga
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-bio
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The categories of this paper are: acc-phys
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The categories of this paper are: stat
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The categories of this paper are: cmp-lg
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The categories of this paper are: mtrl-th
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The categories of this paper are: alg-geom
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The categories of this paper are: nlin
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The categories of this paper are: astro-ph
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The categories of this paper are: funct-an
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The categories of this paper are: adap-org