Advertisement

Early Vehicle Accident Detection and Notification Based on Smartphone Technology

  • Roberto G. Aldunate
  • Oriel A. Herrera
  • Juan Pablo Cordero
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8276)

Abstract

Prompt assistance to people involved in vehicle accidents could make a significant difference on the consequences of such accidents. Some vehicle manufacturers include technology embedded into the vehicles they build to detect and communicate crash vehicle events to emergency agencies. Nevertheless, this approach adds cost to the vehicles, and as it is only present at a small proportion of vehicles in most urban settings. Nowadays, most cell phones are equipped with a diversity of sensors, including accelerometers, GPS units, microphones, among others, which present an opportunity for these devices to be used, while carried by people, as both sensors for vehicle accidents and remote notification of such events. By means of simulations, this article presents encouraging results regarding using smartphones for vehicle crash detection. The main conclusion presented is that a model for early detection of vehicle accidents has been elaborated and preliminary proved.

Keywords

Sensors Vehicle Accident Smartphone 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Peden, R., Scurfield, R.: World Health Organization. World Report on road Traffic Injury Prevention (2004)Google Scholar
  2. 2.
    Bretz, E.A.: The car: Just a web browser with tires. Spectrum, IEEE38.1, 92–94 (2001)CrossRefGoogle Scholar
  3. 3.
    Farris, P.: General Motors OnStar (2008)Google Scholar
  4. 4.
    Bekiaris, E.D., Spadoni, A., Nikolaou, S.I.: SAFERIDER Project: new safety and comfort in Powered Two Wheelers. In: 2nd Conference on Human System Interactions, HSI 2009. IEEE (2009)Google Scholar
  5. 5.
    Montanari, R., Borin, A., Spadoni, A.: SAFERIDER: results from Yamaha test site on advanced rider assistance system. In: Proceedings of the 9th ACM SIGCHI Italian Chapter International Conference on Computer-Human Interaction: Facing Complexity, pp. 132–138. ACM, New York (2011)Google Scholar
  6. 6.
    Smith, A.: 46% of American adults are smartphone owners. Pew Internet & American Life Project (2012), http://www.pewinternet.org/Reports/2012/Smartphone-Update-2012/Findings.aspx (accessed April 27, 2012)
  7. 7.
    Fan, Y., Chen, Q., Liao, C.F., Douma, F.: UbiActive: Smartphone-Based Tool for Trip Detection and Travel-Related Physical Activity Assessment. Submitted for Presentation at the Transportation Research Board 92nd Annual Meeting (2012)Google Scholar
  8. 8.
    McAffer, J., Lemieux, J., Aniszczyk, C.: Eclipse rich client platform. Addison-Wesley Professional (2010)Google Scholar
  9. 9.
    Lee, S., Diez, E.: A Comparative Analysis of Latin NCAP to Global NCAPs. In: Transportation Research Board 92nd Annual Meeting. No. 13-1397 (2013)Google Scholar
  10. 10.
    Mohan, P., Padmanabhan, V.N., Ramjee, R.: Nericell: rich monitoring of road and traffic conditions using mobile smartphones. In: Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems. ACM (2008)Google Scholar
  11. 11.
    Martinez, F.J., Toh, C.K., Cano, J.C., Calafate, C.T., Manzoni, P.: Emergency services in future intelligent transportation systems based on vehicular communication networks. IEEE Intelligent Transportation Systems Magazine 2(2), 6–20 (2010)CrossRefGoogle Scholar
  12. 12.
    Fogue, M., Garrido, P., Martinez, F.J., Cano, J.C., Calafate, C.T., Manzoni, P., Sanchez, M.: Prototyping an automatic notification scheme for traffic accidents in vehicular networks. In: 2011 IFIP Wireless Days (WD). IEEE (2011)Google Scholar
  13. 13.
    Barba, C.T., Mateos, M.A., Soto, P.R., Mezher, A.M., Igartua, M.A.: Smart city for VANETs using warning messages, traffic statistics and intelligent traffic lights. In: Intelligent Vehicles Symposium (IV). IEEE (2012)Google Scholar
  14. 14.
    Borriello, G., Chalmers, M., LaMarca, A., Nixon, P.: Delivering real-world ubiquitous location systems. Communications of the ACM 48(3), 36–41 (2005)CrossRefGoogle Scholar
  15. 15.
    Aldunate, R., Nussbaum, M., Pena-Mora, F.: An Empirical Wi-Fi Based Location Mechanism for Urban Search and Rescue Operations. In: Langendoerfer, P., Liu, M., Matta, I., Tsaoussidis, V. (eds.) WWIC 2004. LNCS, vol. 2957, pp. 48–61. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  16. 16.
    af Wåhlberg, A. E.: Characteristics of low speed accidents with buses in public transport. Accident Analysis & Prevention 34(5), 637–647 (2002)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Roberto G. Aldunate
    • 1
  • Oriel A. Herrera
    • 2
  • Juan Pablo Cordero
    • 2
  1. 1.College of Applied Health SciencesUniversity of Illinois at Urbana-ChampaignUSA
  2. 2.Engineering Informatics SchoolCatholic University of TemucoChile

Personalised recommendations