International Journal of Automotive Technology

, Volume 19, Issue 1, pp 167–177 | Cite as

Belief and fuzzy theories for driving behavior assessment in case of accident scenarios

Article

Abstract

The estimation of the overspeed risk before the accident is among the main goals of this paper. The proposed method uses the Energy Equivalent Speed (EES) to assess the severity of an eventual accident. However, the driver behavior evaluation should take into account the parameters related to the Driver, the Vehicle and the Environment (DVE) system. For this purpose, this paper considers a two-level strategy to predict the global risk of an event using the Dempster-Shafer Theory (DST) and the Fuzzy Theory (FT). This paper presents two methods to develop the Expert Model-based Basic Probability Assignment (EM based BPA), which is the most important task in the DST. The first one is based on the accident statistics and the second method deals with the relationship between the Fuzzy and Belief measurements. The experimental data is collected by one driver using our test vehicle and a Micro-intelligent Black Box (Micro-iBB) to collect the driving data. The sensitivity of the developed models is analysed. Our main evaluation concerns the Usage Based Insurance (UBI) applications based on the driving behavior. So, the obtained masses over the defined referential subsets in the DST are used as a score to compute the driver’s insurance premium.

Keywords

Advanced driver assistance system Driver-vehicle-environment system Fuzzy inference systems Belief theory Driver behavior 

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References

  1. Al-Abadi, A. M. (2015). The application of Dempster–Shafer theory of evidence for assessing groundwater vulnerability at Galal Badra basin, Wasit governorate, east of Iraq. Applied Water Science 7, 4, 1725–1740.CrossRefGoogle Scholar
  2. Angkititrakul, P., Miyajima, C. and Takeda, K. (2011). Modeling and adaptation of stochastic driver-behavior model with application to car following. IEEE Intelligent Vehicles Symp., 814–819.Google Scholar
  3. Boudraa, A. O., Bentabet, A., Salzentein, F. and Guillon, L. (2004). Demspter-Shafer's basic probability assignement based on fuzzy membership functions. Electronic Letters on Computer Vision and Image Analysis 4, 1, 1–9.Google Scholar
  4. Burdzik, R., Folega, P., Konieczny, L. and Warczek, J. (2012). Analysis of material deformation work measures in determination of a vehicle's collision speed. Archives of Materials Science and Engineering 58, 1, 12–21.Google Scholar
  5. Cirillo, J. (1968). Interstate system accident research study II. Public Roads 35, 3, 71–75.Google Scholar
  6. Deng, Y., Shi, W., Zhu, Z. and Qi, L. (2004). Combining belief functions based on distance of evidence. Decision Support Systems 38, 3, 489–493.CrossRefGoogle Scholar
  7. Denoeux, T. (2006). Constructing belief functions from sample data using multinomial confidence regions. Int. J. Approximate Reasoning 42, 1, 228–252.MathSciNetCrossRefMATHGoogle Scholar
  8. Dutta, P. and Ali, T. (2011). Fuzzy focal elements in Dempster-Shafer theory of evidence: Case study in risk analysis. Int. J. Computer Applications 34, 1, 46–53.Google Scholar
  9. Ge, H. X., Cheng, R. J. and Li, Z. P. (2008). Two velocity difference model for a car following theory. Physica A 387, 21, 5239–5245.CrossRefGoogle Scholar
  10. Gilbert, S. and Halsey-Watkins, R. (2013). Cartes Interactives des Accidents Routiers au Québec. http://www.lapresse.ca/multimedias/201310/14/01-4699525-cartes-interactives-des-accidents-routiers-au-quebec.phpGoogle Scholar
  11. Glaser, S., Vanholme, B., Mammar, S., Gruyer, D. and Nouveliere, L. (2010). Maneuver-based trajectory planning for highly autonomous vehicles on real road with traffic and driver interaction. IEEE Trans. Intelligent Transportation Systems 11, 3, 589–606.CrossRefGoogle Scholar
  12. Jiang, W., Yang, Y., Luo, Y. and Qin, X. Y. (2015). Determining basic probability assignment based on the Improved similarity measures of generalized fuzzy numbers. Int. J. Computers, Communications and Control 10, 3, 333–347.CrossRefGoogle Scholar
  13. Ly, M. V., Martin, S. and Trivedi, M. M. (2013). Driver classification and driving style recognition using inertial sensors. IEEE Intelligent Vehicles Symp., 1040–1045.Google Scholar
  14. Martin, A. (2008). Implementing general belief function framework with a practical codification for low complexity. Florentin Smarandache & Jean Dezert. Advances and Applications of DSmT for Information Fusion, American Research Press Rehoboth.Google Scholar
  15. Martin, A. and Osswald, C. (2006). A new generalization of the proportional conflict redistribution rule stable in terms of decision. American Research Press 2, 1, 39–88.Google Scholar
  16. Meng, X., Lee, K. K. and Xu, Y. (2006). Human driving behavior recognition based on hidden markov models. IEEE Int. Conf. Robotics and Biomimetics, Kumming, China.Google Scholar
  17. Molina, J. (2005). Commande de L’inter-distance Entre Deux Véhicules. Ph. D. Dissertation. Institut National Polytechnique de Grenoble. Grenoble, France.Google Scholar
  18. NHTSA (2014). Traffic Safety Facts 2012 Data. https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812021Google Scholar
  19. Ramasso, E., Panagiotakis, C., Pellerin, D. and Rombaut, M. (2008). Human action recognition in videos based on the transferable belief model. Pattern Analysis and Applications 11, 1, 1–19.MathSciNetCrossRefGoogle Scholar
  20. Ristic, B. and Smets, P. (2005). Target identification using belief functions and implication rules. IEEE Trans. Aerospace and Electronic Systems 41, 3, 1097–1102.CrossRefGoogle Scholar
  21. Rocha, R., Guidoin, S. and Délage-Béland, G. (2012). Accident Map of Montreal. http://www.montrealgazette.com/news/road-safety/map/index.htmlGoogle Scholar
  22. Smarandache, F. and Dezert, J. (2005). Information fusion based on new proportional conflict redistribution rules. Int. Conf. Information Fusion.Google Scholar
  23. Smarandache, F. and Dezert, J. (2009). Advances and Application of DSmT for Information Fusion. American Research Press (ARP), USA.MATHGoogle Scholar
  24. Solomon, D. (1964). Accidents on Main Rural Highways Related to Speed, Driver, and Vehicle. U.S. Department of Commerce/Bureau of Public Roads.Google Scholar
  25. TC (2011). Focus on Geography Series, 2011 Census. http://www12.statcan.gc.ca/census-recensement/2011/dp-pd/tbt-tt/Rp-eng.cfm?LANG=E&APATH=3&DETAIL=0&DIM=0&FL=A&FREE=0&GC=0&GID=0&GK=0&GRP=1&PID=103142&PRID=10&PTYPE=101955&S=0&SHOWALL=0&SUB=0&Temporal=2011&THEME=88&VID=0&VNAMEE=&VNAMEF=Google Scholar
  26. Wang, M. S., Jeong, N. T., Kim, K. S., Choi, S. B., Yang S. M., You, S. H., Lee, J. H. and Suh, M. W. (2016). Drowsy behavior detection based on driving information. Int. J. Automotive Technology 17, 1, 165–173.CrossRefGoogle Scholar
  27. Yang, I., Na, S. and Heo, H. (2017). Intelligent algorithm based on support vector data description for automotive collision avoidance system. Int. J. Automotive Technology 18, 1, 69–77.CrossRefGoogle Scholar

Copyright information

© The Korean Society of Automotive Engineers and Springer-Verlag GmbH Germany 2018

Authors and Affiliations

  1. 1.Department of Electric EngineeringUniversity of QuebecMontrealCanada

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