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Reduction of Driver Stress Using AmI Technology while Driving in Motorway Merging Sections

  • Kashif Zia
  • Andreas Riener
  • Alois Ferscha
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6439)

Abstract

High average intensity of traffic and problems like traffic congestions, road safety, etc. are challenging problems striking highway operators in these days. With the broad application of intelligent transport systems (ITS), particularly for the most dense street sections, some of these problems can be minimized or even solved; supplementary great potential is attributed to applications based on state-of-the art technology like car-to-x communication, for instance by extending an individuals “field of vision” by observations taken from all the vehicles in front. In this work we present a simulation based approach for improving driving experience and increasing road safety in merging sections by redirecting vehicles in advance according to a negotiation of requirements and desires of the flowing traffic on the main road and cars merging from the entrance lane. The simulation experiments performed in a cellular automaton based environment were data driven and on real scale, using traffic flow data on a minute-by-minute basis from a large urban motorway in a main city of the European Union. Our results have shown that the application of AmI technology has potential to influence driver’s behavior (seamlessly invoking for a lane change well before an abrupt merging point) resulting in a reduction of panic, particularly for sections with limited range of view.

Keywords

Data driven simulation Driver assistance Motorway merging Field of view extension Vibro-tactile seat Safety belt interface 

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References

  1. 1.
    Buld, S., Hoffmann, S., Totzke, I., et al.: Adaptive driver assistance on the basis of traffic condition – exemplary demonstration for highway entrance. In: Aachener Kolloquium Fahrzeug- und Motorentechnik, ch. 15, pp. 1758–1782 (2006)Google Scholar
  2. 2.
    Dahm, M.: Grundlagen der Mensch-Computer-Interaktion, 1st edn., 368 pages. Pearson Education, London (2005) ISBN: 978-3-8273-7175-1Google Scholar
  3. 3.
    Erp, J.B.V., Veen, H.A.V.: Vibrotactile in-vehicle navigation system. Transportation Research Part F: Traffic Psychology and Behaviour 7(4-5), 247–256 (2004)CrossRefGoogle Scholar
  4. 4.
    Ferscha, A., Riener, A.: Pervasive Adaptation in Car Crowds. In: First International Workshop on User-Centric Pervasive Adaptation (UCPA) at MOBILWARE 2009, Berlin, Germany, April 27, p. 6. Springer, Heidelberg (2009)Google Scholar
  5. 5.
    Hills, B.L.: Vision, mobility, and perception in driving. Perception 9, 183–216 (1980)CrossRefGoogle Scholar
  6. 6.
    Ho, C., Tan, H., Spence, C.: Using spatial vibrotactile cues to direct visual attention in driving scenes. Transportation Research Part F: Psychology and Behaviour 8(6), 397–412 (2005)CrossRefGoogle Scholar
  7. 7.
    Kwon, D., Kim, S.: Haptic Interfaces for Mobile Devices: A Survey of the State of the Art. Recent Patents on Computer Science 1(2), 84–92 (2008)CrossRefGoogle Scholar
  8. 8.
    Liu, R., Hyman, G.: Towards a generic guidance for modelling motorway traffic merge. In: European Transport Conference (ETC), Leiden, Netherlands, October 6-8, p. 17. Association for European Transport, AET (2008)Google Scholar
  9. 9.
    Mauter, G., Katzki, S.: The Application of Operational Haptics in Automotive Engineering. In: Business Briefing: Global Automotive Manufacturing & Technology 2003, pp. 78–80. Team for Operational Haptics, Audi AG (2003)Google Scholar
  10. 10.
    New Jersey Motor Vehicle Commission: New Jersey Driver Manual, ch. 5: Defense Driving (2008)Google Scholar
  11. 11.
    Riener, A.: Sensor-Actuator Supported Implicit Interaction in Driver Assistance Systems, 1st edn. Vieweg+Teubner Research, Wiesbaden (January 14, 2010) ISBN-13: 978-3-8348-0963-6Google Scholar
  12. 12.
    Riener, A., Zia, K., Ferscha, A.: AmI technology helps to sustain speed while merging – A data driven simulation study on Madrid motorway ring M30. In: DS-RT 2010, Fairfax, VA, USA, October 17-20, p. 10. IEEE CS Press, Los Alamitos (2010)Google Scholar
  13. 13.
    Wilensky, U.: Netlogo modeling environment, http://ccl.northwestern.edu/netlogo, http://ccl.northwestern.edu/netlogo (last retrieved July 30, 2010)

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Kashif Zia
    • 1
  • Andreas Riener
    • 1
  • Alois Ferscha
    • 1
  1. 1.Institute for Pervasive ComputingJohannes Kepler University LinzLinzAustria

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