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. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Patterson, D. J., Liao, L., Fox, D., & Kautz, H. (2003). Inferring high-level behavior from low-level sensors. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2864, 73–89. https://doi.org/10.1007/978-3-540-39653-6_6
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