Inferring High-Level Behavior from Low-Level Sensors
Lecture notes in computer sciencePublished 1 January 2003
Donald J. Patterson, Lin Liao, Dieter Fox, Henry Kautz
Citations517
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
A method of learning a Bayesian model of a traveler moving through an urban environment that simultaneously learns a unified model of the traveler’s current mode of transportation as well as his most likely route, in an unsupervised manner is presented.
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.
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