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4D – Distracted Driver Detection Device

Authors: Ananya Sridhar;

4D – Distracted Driver Detection Device

Abstract

This paper describes an inexpensive distracted driver detection device built using a Raspberry PI3, a video camera, and python code. Distracted and drowsy driving are two of the leading causes of automobile accidents in the United States, and this inexpensive standalone can help prevent those deaths. live video feed of the driver’s face is read in and through the facial landmark detector within DLIB, the co-ordinates of the eyes and mouth are extracted in real time. The ratio of the distance between the eyelids in the vertical direction to the horizontal direction defined as Eye Open Ratio is used to evaluate if the eye is open or closed and a similar ratio on the mouth(Mouth Open Ratio) is used to identify if it is open or closed. By looking at the eye and mouth open ratio for several consecutive frames, it is determined with >95% accuracy whether the driver is drowsy, distracted, or yawning. If any of these behaviors are noted, the device will prompt an audible warning to encourage the driver to focus on the road. The prototype was tested under a variety of lighting conditions from dark to bright light and on different subjects with and without glasses. This test data was used to determine the threshold for when the eye or mouth is determines open or closed. Additionally, the prototype’s settings are customizable for a primary driver to further improve the accuracy. The device connects to a smart phone and sends information with the time stamp of the distracted driver incident. This device can be used to prevent distracted or drowsy driving-related deaths, and is an inexpensive attachment that can easily be fitted into a preexisting vehicle.

Keywords

Raspberry Pi, Python, Night vision camera. Distracted driving, Management of Technology and Innovation, General Engineering

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    1
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
gold