Evaluation of Performance Enhancement for Crash Constellation Prediction via Car-to-Car Communication

A Simulation Model Based Approach
  • Thomas Kuehbeck
  • Gor Hakobyan
  • Axel Sikora
  • Claude C. Chibelushi
  • Mansour Moniri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8435)

Abstract

Active safety systems for advanced driver assistance systems act within a complex, dynamic traffic environment featuring various sensor systems which detect the vehicles’ surroundings and interior. This paper describes the recent progress towards a performance evaluation of car-to-car communication (C2C) for active safety systems - in particular for crash constellation prediction. The methodology introduced in this work is designed to evaluate the impact of different sensors on the accuracy of a crash constellation prediction algorithm. The benefit of C2C communication (viewed as a virtual sensor) within a sensor data fusion architecture for pre-crash collision prediction is explored. Therefore, a simulation environment for accident scenarios analysis reproducing real-world sensor behaviour, is designed and implemented. Performance evaluation results show that C2C increases confidence in the estimated position of the oncoming vehicle. With C2C enhancement the given accuracy in time-to-collision (TTC) estimation is achievable about 110 ms earlier for moderate velocities at TTC range of [0.5s..0.2s]. The uncertainty in the vehicle position prediction at the time of collision can be reduced about half by integrating C2C communication into the sensor data fusion.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aeberhard, M., Schlichtharle, S., Kaempchen, N., Bertram, T.: Track-to-track fusion with asynchronous sensors using information matrix fusion for surround environment perception. IEEE Transactions on Intelligent Transportation Systems 13(4), 1717–1726 (2012)CrossRefGoogle Scholar
  2. 2.
    Al-Ghamdi, A.S.: Using logistic regression to estimate the influence of accident factors on accident severity. Accident Analysis & Prevention 34(6), 729–741 (2002)CrossRefGoogle Scholar
  3. 3.
    Boufous, S., Finch, C., Hayen, A., Williamson, A.: The impact of environmental, vehicle and driver characteristics on injury severity in older drivers hospitalized as a result of a traffic crash. Journal of Safety Research 39(1), 65–72 (2008)CrossRefGoogle Scholar
  4. 4.
    Braess, H.H., Seiffert, U.: Vieweg Handbuch Kraftfahrzeugtechnik. Springer DE (2011)Google Scholar
  5. 5.
    Conroy, C., Tominaga, G.T., Erwin, S., Pacyna, S., Velky, T., Kennedy, F., Sise, M., Coimbra, R.: The influence of vehicle damage on injury severity of drivers in head-on motor vehicle crashes. Accident Analysis & Prevention 40(4), 1589–1594 (2008)CrossRefGoogle Scholar
  6. 6.
    GIDAS: German in-depth accident study, http://www.gidas.org
  7. 7.
    Kaempchen, N.: Feature level fusion of laser scanner and video data for advanced driver assistance systems. Ph.D. thesis, Univ., Fak. fuer Ingenieurwiss. und Informatik (2007)Google Scholar
  8. 8.
    Karrenberg, S.: Zur Erkennung unvermeidbarer Kollisionen von Kraftfahrzeugen mit Hilfe von Stellvertretertrajektorien. Ph.D. thesis, Technischen Universität Carolo-Wilhelmina zu Braunschweig (2008)Google Scholar
  9. 9.
    Kononen, D.W., Flannagan, C.A., Wang, S.C.: Identification and validation of a logistic regression model for predicting serious injuries associated with motor vehicle crashes. Accident Analysis & Prevention 43(1), 112–122 (2011)CrossRefGoogle Scholar
  10. 10.
    Lee, J., Conroy, C., Coimbra, R., Tominaga, G.T., Hoyt, D.B.: Injury patterns in frontal crashes: The association between knee–thigh–hip (kth) and serious intra-abdominal injury. Accident Analysis & Prevention 42(1), 50–55 (2010)CrossRefGoogle Scholar
  11. 11.
    Nitz, G.: Entwicklung eines Systems zur aktiven Bremsung eines Fahrzeugs in Gefahrensituationen. Ph.D. thesis, Lehrstuhl für Messsystem- und Sensortechnik der Technischen Universität München (2008)Google Scholar
  12. 12.
    Risch, M.: Der kamm’sche kreis - wie start kann man beim kurvenfahren bremsen. Fahrphysik und Verkehr PdN-Ph. 5/51. Jg. (2002)Google Scholar
  13. 13.
    Segui-Gomez, M., Baker, S.P.: Changes in injury patterns in frontal crashes: preliminary comparisons of drivers of vehicles model years 1993-1997 to drivers of vehicles 1998–2001. In: Annual Proceedings/Association for the Advancement of Automotive Medicine 46, 1 (2002)Google Scholar
  14. 14.
    Sobhani, A., Young, W., Logan, D., Bahrololoom, S.: A kinetic energy model of two-vehicle crash injury severity. Accident Analysis & Prevention 43(3), 741–754 (2011)CrossRefGoogle Scholar
  15. 15.
    Tarko, A.P., Bar-Gera, H., Thomaz, J., Issariyanukula, A.: Model-based application of abbreviated injury scale to police-reported crash injuries. Transportation Research Record: Journal of the Transportation Research Board 2148(1), 59–68 (2010)CrossRefGoogle Scholar
  16. 16.
    Wood, D.P., Veyrat, N., Simms, C., Glynn, C.: Limits for survivability in frontal collisions: Theory and real-life data combined. Accident Analysis & Prevention 39(4), 679–687 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Thomas Kuehbeck
    • 1
  • Gor Hakobyan
    • 1
  • Axel Sikora
    • 2
  • Claude C. Chibelushi
    • 3
  • Mansour Moniri
    • 3
  1. 1.BMW AGGermany
  2. 2.HS OffenburgGermany
  3. 3.Staffordshire UniversityUK

Personalised recommendations