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.
Coming Soon!
Coming Soon!
Presented at the 2022 Lifesavers Conference. PPT of the presentation can be found here.
Coming Soon!
Luke Neurauter (VTTI/VT)*
Marty Miller (VTTI/VT)
Jacob Walters (VTTI/VT)
Eileen Herbers (Student-VTTI/VT)
Dan Glaser (GM)
Start Date: 2020-11-01
End Date: 2022-04-30
Status: Active
Grant Number: 69A3551747115
Total Funding: $1,602,787
Source Organization: Safe-D National UTC
Project Number: 05-091
Automated Vehicles
Big Data Analytics
Driver Factors and Interfaces
Vehicle Technology
RiP URL
UTC Project Information Form
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC 20590 United States
Virginia Polytechnic Institute and State University
Virginia Tech Transportation Institute
3500 Transportation Research Plaza
Blacksburg, Virginia 24061
USA