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WreckWatch: Automatic Traffic Accident Detection and Notification with Smartphones

  • Jules WhiteEmail author
  • Chris Thompson
  • Hamilton Turner
  • Brian Dougherty
  • Douglas C. Schmidt
Article

Abstract

Traffic accidents are one of the leading causes of fatalities in the US. An important indicator of survival rates after an accident is the time between the accident and when emergency medical personnel are dispatched to the scene. Eliminating the time between when an accident occurs and when first responders are dispatched to the scene decreases mortality rates by 6%. One approach to eliminating the delay between accident occurrence and first responder dispatch is to use in-vehicle automatic accident detection and notification systems, which sense when traffic accidents occur and immediately notify emergency personnel. These in-vehicle systems, however, are not available in all cars and are expensive to retrofit for older vehicles. This paper describes how smartphones, such as the iPhone and Google Android platforms, can automatically detect traffic accidents using accelerometers and acoustic data, immediately notify a central emergency dispatch server after an accident, and provide situational awareness through photographs, GPS coordinates, VOIP communication channels, and accident data recording. This paper provides the following contributions to the study of detecting traffic accidents via smartphones: (1) we present a formal model for accident detection that combines sensors and context data, (2) we show how smartphone sensors, network connections, and web services can be used to provide situational awareness to first responders, and (3) we provide empirical results demonstrating the efficacy of different approaches employed by smartphone accident detection systems to prevent false positives.

Keywords

smartphones traffic accident detection cyber-physical systems mobile cyber-physical systems 

Notes

Acknowledgements

This work was supported by a grant from the National Science Foundation, RAPID:Collaborative Research:Cloud Environmental Analysis and Relief, CNS# 1047753.

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Jules White
    • 1
    Email author
  • Chris Thompson
    • 2
  • Hamilton Turner
    • 1
  • Brian Dougherty
    • 2
  • Douglas C. Schmidt
    • 2
  1. 1.Dept. of Electrical and Computer EngineeringVirginia TechBlacksburgUSA
  2. 2.Dept. of Electrical Engineering and Computer ScienceVanderbilt UniversityNashvilleUSA

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