Safe-D: Safety through Disruption

Behavioral Indicators of Drowsy Driving: Active Search Mirror Checks


Driver impairment, due to drowsiness or fatigue, has a significant impact on the safety of all road users. Assessing an impairment such as driver drowsiness, through the use of vehicle-based technology, continues to be an area of interest. Both the initial detection, as well as continued monitoring, of driver drowsiness have been the emphasis of vehicle-based Driver Monitoring Systems (DMS). Particularly, in-vehicle eye-tracking systems have been implemented, as a way of determining driver state. Specifically, when hands-free driving assistance features are engaged, measures such as the driver’s percentage of eye closure (PERCLOS) are being used to determine driver drowsiness. However, one challenge of such a metric is its reliability; particularly with regard to false alarms (when a DMS indicates the driver is drowsy, but in fact is not). Therefore, the use of more gross-level driver behavioral-based measures may serve as a way of crosschecking the assessments of a DMS. This work aims to mine an available dataset in order to examine driver search behavior, with the goal of identifying relationships between driver vigilance and drowsy driving. The hypothesis is that driver search behavior (e.g. mirror checks) degrades with increasing levels of drowsiness. If a reliable relationship is found between driver vigilance and state of drowsiness, the practical applications may be to incorporate this measure of driver search behavior into the “toolbox” of metrics for estimating driver drowsiness.

Project Highlights

  • It was expected that DMS and PERCLOS correlation learning modules and an algorithm associated with mirror checks would have resulted from the project.  However, based on the results of the project, and the inability to draw statistically significant conclusions regarding any strong relationship related to gross-level behavioral-based measures; it was determined that such a learning module or algorithm would not be based on a reliable enough foundation to be implemented.

Final Report

Final Report 05-098

EWD & T2 Products

None provided. See highlights for reason.


None provided. See highlights for reason.

Final Dataset

The final datasets for this project are located in the Safe-D Collection on the VTTI Dataverse; DOI: 10.15787/VTT1/R8WY4F.

Research Investigators (PI*)

Jason Meyer (VT/VTTI)*
Eddy Llaneras (VT/VTTI)*

Project Information

Start Date: 2020-01-11
End Date: 2022-05-01
Status: Completed
Grant Number: 69A3551747115
Total Funding: $354,000
Source Organization: Safe-D National UTC
Project Number: 05-084

Safe-D Theme Areas

Big Data Analytics

Automated Vehicles

Safe-D Application Areas

Vehicle Technology
Driver Factors and Interfaces
Performance Measures
Planning for Safety

More Information

UTC Project Information Form

Sponsor Organization

Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC 20590 United States

Performing Organization

Virginia Polytechnic Institute and State University
Virginia Tech Transportation Institute
3500 Transportation Research Plaza
Blacksburg, Virginia 24061