Improving Methods to Measure Attentiveness through Driver Monitoring

Miller, Marty; Herbers, Eileen; Walters, Jacob; Neurauter, Luke; Safety through Disruption (Safe-D) University Transportation Center (UTC); Virginia Polytechnic Institute and State University; Virginia Tech Transportation Institute

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Improving Methods to Measure Attentiveness through Driver Monitoring

2022-07-01
Datasets Available
By Miller, Marty ; Herbers, Eileen ; Walters, Jacob ; ...

[PDF-1.77 MB]

English

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Creators:

Miller, Marty ; Herbers, Eileen ; Walters, Jacob ; Neurauter, Luke

Miller, Marty ; Herbers, Eileen ; Walters, Jacob ; Neurauter, Luke Less -
Corporate Creators:

Safety through Disruption (Safe-D) University Transportation Center (UTC) ; Virginia Polytechnic Institute and State University ; Virginia Tech Transportation Institute

Safety through Disruption (Safe-D) University Transportation Center (UTC) ; Virginia Polytechnic Institute and State University ; Virginia Tech Transportation Institute Less -
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology Less -
Subject/TRT Terms:

[+]

Algorithms Driver Distraction Driver Performance Drivers Human Factors In Crashes Machine Learning Traffic Safety
Publication/ Report Number:

05-091
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final research
Corporate Publisher:

Safety through Disruption (Safe-D) University Transportation Center (UTC)
Abstract:

Driver inattention poses a significant problem on today’s roadways, increasing risk for all road users. This report details our efforts in developing algorithms to detect driver inattention. A benchmark dataset was developed based on video review of driving events. Buffer-based algorithms were developed and compared using this benchmark dataset. The benchmark events were also used as a training dataset for machine learning models. Driver glance locations were important for determining driver attentiveness. In addition, vehicle speed was important for understanding the driving context, which was found to have a large impact on driver behavior.
Format:

PDF
Funding:

69A3551747115/05-091
Collection(s):

University Transportation Centers
Main Document Checksum:

[+]

urn:sha256:5385c4b8db20f65cb5d0f4308288df7fc59f9ec2cc588ac9949688befc1596c2
Download URL:

https://rosap.ntl.bts.gov/view/dot/64151/dot_64151_DS1.pdf
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No Additional Files

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Improving Methods to Measure Attentiveness through Driver Monitoring

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