Body sensor networks for driver distraction identification

Amardeep Sathyanarayana, Sandhya Nageswaren, Hassan Ghasemzadeh, Roozbeh Jafari, John H.L. Hansen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

42 Scopus citations

Abstract

Cars have become a part of almost everyone's life taking people from one place to another. In such a fast paced mode of transport, there are a variety of ways in which drivers can get distracted while driving. Getting stuck in a traffic jam, doing other tasks simultaneously while driving- for example drinking, reading, talking over the mobile phone are various forms of distractions. Early detection of driver distraction can reduce the number of accidents. This paper describes the initial analysis of a system for detecting driver distractions using data from the Controller Area Network (CAN) and motion sensor (accelerometer and gyroscope). The paper mainly focuses on distractions perceivable with leg and head movements of the driver. The data from these expressive parts of the driver yield a high accuracy of distraction detection of over 90%. With such high accuracies, reliable systems could be built to have early warning or corrective mechanisms which would avoid or reduce the intensity of accidents caused due to driver distractions.

Original languageEnglish (US)
Title of host publicationProceedings of the 2008 IEEE International Conference on Vehicular Electronics and Safety, ICVES 2008
Pages120-125
Number of pages6
DOIs
StatePublished - 2008
Externally publishedYes
Event2008 IEEE International Conference on Vehicular Electronics and Safety, ICVES 2008 - Columbus, OH, United States
Duration: Sep 22 2008Sep 24 2008

Publication series

NameProceedings of the 2008 IEEE International Conference on Vehicular Electronics and Safety, ICVES 2008

Conference

Conference2008 IEEE International Conference on Vehicular Electronics and Safety, ICVES 2008
Country/TerritoryUnited States
CityColumbus, OH
Period9/22/089/24/08

ASJC Scopus subject areas

  • Artificial Intelligence
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Transportation

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