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)


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.


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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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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