Case-Based Prediction of Teen Driver Behavior and Skill

  • Santiago Ontañón
  • Yi-Ching Lee
  • Sam Snodgrass
  • Dana Bonfiglio
  • Flaura K. Winston
  • Catherine McDonald
  • Avelino J. Gonzalez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8765)


Motor vehicle crashes are the leading cause of death for U.S. teens, accounting for more than one in three deaths in this age group and claiming the lives of about eight teenagers a day, according to the 2010 report by the Center for Disease Control and Prevention. In order to inform new training methods and new technology to accelerate learning and reduce teen crash risk, a more complete understanding of this complex driving behavior was needed. In this application paper we present our first step towards deploying case-based techniques to model teenage driver behavior and skill level. Specifically, we present our results in using case-based reasoning (CBR) to model both the vehicle control behavior and the skill proficiency of teen drivers by using data collected in a high-fidelity driving simulator. In particular, we present a new similarity measure to compare behavioral data based on feature selection methods, which achieved good results in predicting behavior and skill.


Driving behavior similarity assessment feature selection 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cordier, A., Mascret, B., Mille, A.: Extending case-based reasoning with traces. In: Grand Challenges for Reasoning from Experiences, Workshop at IJCAI, vol. 9, p. 31 (2009)Google Scholar
  2. 2.
    Curry, A.E., Hafetz, J., Kallan, M.J., Winston, F.K., Durbin, D.R.: Prevalence of teen driver errors leading to serious motor vehicle crashes. Accident Analysis & Prevention 43(4), 1285–1290 (2011)CrossRefGoogle Scholar
  3. 3.
    Das, D., Zhou, S., Lee, J.D.: Differentiating alcohol-induced driving behavior using steering wheel signals. IEEE Transactions on Intelligent Transportation Systems 13(3), 1355–1368 (2012)CrossRefGoogle Scholar
  4. 4.
    Fernlund, H.K.G., Gonzalez, A.J., Georgiopoulos, M., DeMara, R.F.: Learning tactical human behavior through observation of human performance. IEEE Transactions on Systems, Man, and Cybernetics, Part B 36(1), 128–140 (2006)CrossRefGoogle Scholar
  5. 5.
    Floyd, M.W., Esfandiari, B.: Learning state-based behaviour using temporally related cases. In: Proceedings of the Sixteenth UK Workshop on Case-Based Reasoning (2011)Google Scholar
  6. 6.
    Floyd, M.W., Esfandiari, B., Lam, K.: A case-based reasoning approach to imitating robocup players. In: Proceedings of the Twenty-First International Florida Artificial Intelligence Research Society (FLAIRS), pp. 251–256 (2008)Google Scholar
  7. 7.
    Foss, R.D., Martell, C.A., Goodwin, A.H., O’Brien, N.P.: Measuring changes in teenage driver crash characteristics during the early months of driving. In: AAA Foundation for Traffic Safety (2011)Google Scholar
  8. 8.
    Keogh, E.J., Pazzani, M.J.: Scaling up dynamic time warping for datamining applications. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 285–289. ACM (2000)Google Scholar
  9. 9.
    Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Statist. 22(1), 79–86 (1951)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Lamontagne, L., Rugamba, F., Mineau, G.: Acquisition of cases in sequential games using conditional entropy. In: ICCBR 2012 Workshop on TRUE: Traces for Reusing Users’ Experience (2012)Google Scholar
  11. 11.
    Macadam, C.C.: Understanding and modeling the human driver. Vehicle System Dynamics 40(1-3), 101–134 (2003)CrossRefGoogle Scholar
  12. 12.
    Markkula, G., Benderius, O., Wolff, K., Wahde, M.: A review of near-collision driver behavior models. Human Factors: The Journal of the Human Factors and Ergonomics Society 54(6), 1117–1143 (2012)CrossRefGoogle Scholar
  13. 13.
    McDonald, C.C., Tanenbaum, J.B., Lee, Y.-C., Fisher, D.L., Mayhew, D.R., Winston, F.K.: Using crash data to develop simulator scenarios for assessing novice driver performance. Transportation Research Record: Journal of the Transportation Research Board 2321(1), 73–78 (2012)CrossRefGoogle Scholar
  14. 14.
    McKnight, A.J., McKnight, A.S.: Young novice drivers: careless or clueless? Accident Analysis & Prevention 35(6), 921–925 (2003)CrossRefGoogle Scholar
  15. 15.
    Nechyba, M.C., Xu, Y.: Stochastic similarity for validating human control strategy models. IEEE Transactions on Robotics and Automation 14(3), 437–451 (1998)CrossRefGoogle Scholar
  16. 16.
    Ontañón, S., Floyd, M.W.: A comparison of case acquisition strategies for learning from observations of state-based experts. In: Proceedings of FLAIRS 2013 (2013)Google Scholar
  17. 17.
    Ontañón, S., Mishra, K., Sugandh, N., Ram, A.: On-line case-based planning. Computational Intelligence Journal 26(1), 84–119 (2010)CrossRefGoogle Scholar
  18. 18.
    Ontañón, S., Montaña, J.L., Gonzalez, A.J.: A dynamic-bayesian network framework for modeling and evaluating learning from observation. Expert Systems with Applications 41(11), 5212–5226 (2014)CrossRefGoogle Scholar
  19. 19.
    Pomerleau, D.: Alvinn: An autonomous land vehicle in a neural network. In: Touretzky, D. (ed.) Advances in Neural Information Processing Systems 1. Morgan Kaufmann (1989)Google Scholar
  20. 20.
    Rabiner, L., Juang, B.-H.: An introduction to hidden markov models. IEEE ASSP Magazine 3(1), 4–16 (1986)CrossRefGoogle Scholar
  21. 21.
    Rakthanmanon, T., Campana, B., Mueen, A., Batista, G., Westover, B., Zhu, Q., Zakaria, J., Keogh, E.: Searching and mining trillions of time series subsequences under dynamic time warping. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 262–270. ACM (2012)Google Scholar
  22. 22.
    Rubin, J., Watson, I.: On combining decisions from multiple expert imitators for performance. In: IJCAI, pp. 344–349 (2011)Google Scholar
  23. 23.
    Wess, S., Althoff, K.D., Derwand, G.: Using k-d trees to improve the retrieval step in case-based reasoning. In: Wess, S., Richter, M., Althoff, K.-D. (eds.) EWCBR 1993. LNCS, vol. 837, pp. 167–181. Springer, Heidelberg (1994)CrossRefGoogle Scholar
  24. 24.
    Wettschereck, D., Aha, D.W., Mohri, T.: A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artificial Intelligence Review 11(1-5), 273–314 (1997)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Santiago Ontañón
    • 1
  • Yi-Ching Lee
    • 2
  • Sam Snodgrass
    • 1
  • Dana Bonfiglio
    • 2
  • Flaura K. Winston
    • 2
  • Catherine McDonald
    • 3
  • Avelino J. Gonzalez
    • 4
  1. 1.Drexel UniversityPhiladelphiaUSA
  2. 2.Children’s Hospital of Philadelphia (CHOP)PhiladelphiaUSA
  3. 3.University of PennsylvaniaPhiladelphiaUSA
  4. 4.University of Central FloridaOrlandoUSA

Personalised recommendations