Multimodal Distraction Detection

Distracted Driving continues to be a cause of traffic accidents despite prevailing legislation. The goal of this project is to automatically determine when the driver is becoming distracted. This information can be sent to a warning system in a car or can be used to help shut down the distracting activity. While it is possible to detect distraction fairly reliably using such information as speech, gas pedal and brake use, and steering wheel trajectory, the use of automatic detection of head movement will make the distraction detector more robust. In many cases, the driver turns their head to either talk to a passenger or, more frequently, look at a smart device. In this project, we will gather data in a driving simulator from 50 subjects who are first asked to drive the course and then asked to watch the recording of their driving and tell us where they were distracted. They will use their own smart devices to listen to and dictate email. The conditions will vary with email of differing degrees of cognitive load, road conditions of varying difficulty and varying need to look at the smart device. The resulting database will be used to train and test a new and robust distraction detector.

Language

  • English

Project

  • Status: Active
  • Funding: $85000
  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590

    Technologies for Safe and Efficient Transportation University Transportation Center

    Carnegie Mellon University
    Pittsburgh, PA  United States  15213
  • Project Managers:

    Ehrlichman, Courtney

  • Performing Organizations:

    Carnegie Mellon University

    Pittsburgh, PA  United States 
  • Principal Investigators:

    Eskenazi, Maxine

  • Start Date: 20160101
  • Expected Completion Date: 20170131
  • Actual Completion Date: 0

Subject/Index Terms

Filing Info

  • Accession Number: 01586687
  • Record Type: Research project
  • Source Agency: Technologies for Safe and Efficient Transportation University Transportation Center
  • Files: UTC, RiP
  • Created Date: Jan 8 2016 3:21PM