Safe-D: Safety through Disruption

Improving Methods to Measure Attentiveness through Driver Monitoring

Abstract

Distracted drivers are involved in approximately 4 million vehicle accidents each year in the U.S. (Dingus et al. 2016). These crashes result in many lives lost and billions of dollars in damages. This widespread issue has resulted in the adoption of regulations in the European Union that will require all new vehicles produced by mid-2022 to be equipped with driver monitoring systems (DMS; Gibbs, 2019). Although new vehicles would be required to incorporate driver monitoring, the optimal approach for determining/identifying inattention is still up for debate.  This project leverages previous research, naturalistic databases, and input from recent literature to develop robust algorithms for assessing when drivers are inattentive to the driving task, while also investigating limitations of different approaches and sources of information. Effectively detecting distraction and inattention can enable automakers to develop countermeasures against this behavior and thereby increase safety for all road users.​​

Project Highlights

  • This research found that algorithms used to determine driver attention should be designed with an understanding of their limitations and that glance location data alone cannot definitively determine a driver’s attention state. At a minimum, both glance location and speed should be used to assess driver attention.
  • A variety of algorithms were designed to measure driver inattention, as well as ways to directly compare algorithm performances to find specific events that create differences between methods.
  • This project was presented at the 2022 LifeSavers Conference in Chicago in the workshop, “What Are You Looking At? The Importance of Driver Monitoring.” The student presenting this project was also recognized as a 2022 LifeSavers Traffic Safety Scholar.

Final Report

05-091 Final Report

EWD & T2 Products

Presented at the 2022 Lifesavers Conference. PPT of the presentation can be found here.

Presentations/Publications

Herbers, E.M. (2022, August 18). Improving Methods to Measure Attentiveness Through Driver Monitoring [PowerPoint]. Distracted Driving Summit, Norfolk, VA. https://www.drivesmartva.org/distracted-driving-summit/

Herbers, E.M. (2022, March 14). Improving Methods to Measure Attentiveness Through Driver Monitoring [PowerPoint]. LifeSavers Conference, Chicago, IL. https://lifesaversconference.org/wp-content/uploads/2022/03/VT-04-Herbers.pdf

Herbers, E.M. (2021, December 1). Improving Methods to Measure Attentiveness Through Driver Monitoring [PowerPoint]. Engineering Mechanics Seminar, Virginia Tech.

Miller, D., Herbers, E., Walters, J., Neurauter, L. (2022, April). Safe-D 05-019 Improving Methods to Measure Attentiveness Through Driver Monitoring (Report 05-019). Safe-D UTC.

Final Dataset

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

Research Investigators (PI*)

Luke Neurauter (VTTI/VT)*
Marty Miller (VTTI/VT)
Jacob Walters (VTTI/VT)
Eileen Herbers (Student-VTTI/VT)
Dan Glaser (GM)

Project Information

Start Date: 2020-11-01
End Date: 2022-04-30
Status: Complete
Grant Number: 69A3551747115
Total Funding: $1,602,787
Source Organization: Safe-D National UTC
Project Number: 05-091

Safe-D Theme Areas

Automated Vehicles
Big Data Analytics

Safe-D Application Areas

Driver Factors and Interfaces
Vehicle Technology

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
USA