Detecting Driver Drowsiness With Multi-Sensor Data Fusion Combined With Machine Learning

Kulhandjian, Hovannes; Mineta Transportation Institute; San Jose State University. College of Business

doi:10.31979/mti.2021.2015

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Detecting Driver Drowsiness With Multi-Sensor Data Fusion Combined With Machine Learning

2021-09-01
By Kulhandjian, Hovannes

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Details:

Creators:

Kulhandjian, Hovannes
Corporate Creators:

Mineta Transportation Institute ; San Jose State University. College of Business

Mineta Transportation Institute ; San Jose State University. College of Business Less -
Corporate Contributors:

State of California SB1 2017/2018, Trustees of the California State University Sponsored Programs Administration
Subject/TRT Terms:

[+]

Accident Prevention Advanced Driver Assistance Systems (ADAS) Applied Machine Learning Data Fusion Detection And Identification Systems Driver Support Systems Drowsiness Drowsy Driver Detection Intelligent Transportation Systems Machine Learning
Publication/ Report Number:

21–25
DOI:

https://doi.org/10.31979/mti.2021.2015
Resource Type:

Tech Report
Geographical Coverage:

United States
Corporate Publisher:

Mineta Transportation Institute
Abstract:

According to the National Highway Traffic Safety Administration, in 2017 drowsy driving resulted in 50,000 injuries across 91,000 police-reported accidents, as well as almost 800 deaths. Through the application of visual and radar sensors combined with machine learning, this research developed a drowsy driver detection system aimed to prevent potentially fatal accidents. The working prototype of Advanced Driver Assistance Systems can be installed in present-day vehicles to detect drowsy drivers with over 95% accuracy. It integrates two types of visual surveillance to examine the driver for signs of drowsiness. A camera is used to monitor the driver’s eyes, mouth and head movement in order to recognize when a discrepancy occurs in the driver's eye blinking pattern, yawning incidence, and/or head drop, thereby signaling that the driver may be experiencing fatigue or drowsiness. The micro-Doppler sensor in the system allows the driver's head movement to be captured at all times. Through data fusion and deep learning, the system quickly analyzes and classifies a driver's behavior under various conditions in real-time monitoring. This research could be implemented to reduce drowsy driving, thereby, making the roads safer for everyone and ultimately saving lives.
Format:

PDF
Funding:

ZSB12017-SJAUX
Collection(s):

US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:c61c7104a5d707a501b637eb3b1c3b4054e50eb0cbfdc309675beaee18b37b29
Download URL:

https://rosap.ntl.bts.gov/view/dot/59148/dot_59148_DS1.pdf
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Detecting Driver Drowsiness With Multi-Sensor Data Fusion Combined With Machine Learning

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