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Inferring High-Level Behavior from Low-Level Sensors

  • Donald J. Patterson
  • Lin Liao
  • Dieter Fox
  • Henry Kautz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2864)

Abstract

We present a method of learning a Bayesian model of a traveler moving through an urban environment. This technique is novel in that it simultaneously learns a unified model of the traveler’s current mode of transportation as well as his most likely route, in an unsupervised manner. The model is implemented using particle filters and learned using Expectation-Maximization. The training data is drawn from a GPS sensor stream that was collected by the authors over a period of three months. We demonstrate that by adding more external knowledge about bus routes and bus stops, accuracy is improved.

Keywords

Hide Markov Model Importance Sampling Ubiquitous Computing Mode Transition Transportation Mode 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Hightower, J., Borriello, G.: Location systems for ubiquitous computing. In: Computer, vol. 34. IEEE Computer Society Press, Los Alamitos (2001)Google Scholar
  2. 2.
    Bonnifait, P., Bouron, P., Crubillé, P., Meizel, D.: Data fusion of fourABS sensors and GPS for an enhanced localization of car-like vehicles. In: Proc. of the IEEE International Conference on Robotics & Automation (2001)Google Scholar
  3. 3.
    Cui, Y., Ge, S.: Autonomous vehicle positioning with GPS in urban canyon environments. In: Proc. of the IEEE International Conference on Robotics & Automation (2001)Google Scholar
  4. 4.
    Ashbrook, D., Starner, T.: Learning significant locations and predicting user movement with gps. In: International Symposium onWearable Computing, Seattle, WA (2002)Google Scholar
  5. 5.
    Patterson, D., Etzioni, O., Fox, D., Kautz, H.: The Activity Compass. In: Proceedings of UBICOG 2002:First International Workshop on Ubiquitous Computing for Cognitive Aids (2002)Google Scholar
  6. 6.
    Kautz, H., Arnstein, L., Borriello, G., Etzioni, O., Fox, D.: The Assisted Cognition Project. In: Proceedings of UbiCog 2002: First International Workshop on Ubiquitous Computing for Cognitive Aids, Gothenberg, Sweden (2002)Google Scholar
  7. 7.
    Doucet, A., de Freitas, N., Gordon, N. (eds.): Sequential Monte Carlo in Practice. Springer, NewYork (2001)zbMATHGoogle Scholar
  8. 8.
    Liao, L., Fox, D., Hightower, J., Kautz, H., Schulz, D.: Voronoi tracking: Location estimation using sparse and noisy sensor data. In: Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (2003)Google Scholar
  9. 9.
    Bar-Shalom, Y., Li, X.R., Kirubarajan, T.: Estimation with Applications to Tracking and Navigation. John Wiley, Chichester (2001)CrossRefGoogle Scholar
  10. 10.
    Dean, T., Kanazawa, K.: Probabilistic temporal reasoning. In: Proc. of the National Conference on Artificial Intelligence (1988)Google Scholar
  11. 11.
    Murphy, K.: Dynamic Bayesian Networks: Representation, Inference and Learning. PhD thesis, UC Berkeley, Computer Science Division (2002)Google Scholar
  12. 12.
    Bar-Shalom, Y., Li, X.R.: Multitarget-Multisensor Tracking: Principles and Techniques. Yaakov Bar-Shalom (1995)Google Scholar
  13. 13.
    Del Moral, P., Miclo, L.: Branching and interacting particle systems approximations of Feynman-Kac formulae with applications to non linear filtering. In: Seminaire de Probabilites XXXIV. Lecture Notes in Mathematics, vol. 1729. Springer, Heidelberg (2000)Google Scholar
  14. 14.
    Bilmes, J.: Agentle tutorial on theEMalgorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. Technical Report ICSI-TR-97-021, University of Berkeley (1998)Google Scholar
  15. 15.
    Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE (1989); IEEE Log Number 8825949 (1989)Google Scholar
  16. 16.
    Levine, R., Casella, G.: Implementations of the Monte Carlo EM algorithm. Journal of Computational and Graphical Statistics 10 (2001)Google Scholar
  17. 17.
    Wei, G., Tanner, M.: A Monte Carlo implementation of the EM algorithm and the poor man’s data augmentation algorithms. Journal of the American Statistical Association 85 (1990)Google Scholar
  18. 18.
    County, K.: Gis (graphical information system) (2003), http://www.metrokc.gov/gis/mission.htm
  19. 19.
    Thrun, S., Langford, J., Fox, D.: Monte Carlo hidden Markov models: Learning non parametric models of partially observable stochastic processes. In: Proc. of the International Conference on Machine Learning (1999)Google Scholar
  20. 20.
    Bureau, U.C.: Census 2000 tiger/line data (2000), http://www.esri.com/data/download/census2000-tigerline/
  21. 21.
    Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)zbMATHGoogle Scholar
  22. 22.
    Anderson, C., Domingos, P., Weld, D.: Relational markov models and their application to adaptive web navigation. In: Proceedings of the Eighth International Conference on Knowledge Discovery and Data Mining, pp. 143–152. ACM Press, Edmonton (2002)CrossRefGoogle Scholar
  23. 23.
    Sanghai, S., Domingos, P., Weld, D.: Dynamic probabilistic relational models. In: Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence. Morgan Kaufmann, Acapulco (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Donald J. Patterson
    • 1
  • Lin Liao
    • 1
  • Dieter Fox
    • 1
  • Henry Kautz
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of Of WashingtonSeattleUSA

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