Nonparametric Bayesian behavior modeling
Name
310370097-MIT.pdf
Description
Full printable version
Size
18.37 MB
Format
Adobe PDF
Checksum (MD5)
471637f002198450a594894b3a5df6f4
Author(s)
Joseph, Joshua Mason
Advisor(s)
Nicholas Roy and Adam Rzepniewski.
Date Issued
2008
Publisher
Massachusetts Institute of Technology
Abstract
As autonomous robots are increasingly used in complex, dynamic environments, it is crucial that the dynamic elements are modeled accurately. However, it is often difficult to generate good models due to either a lack of domain understanding or the domain being intractably large. In many domains, even defining the size of the model can be a challenge. While methods exist to cluster data of dynamic agents into common motion patterns, or "behaviors," assumptions of the number of expected behaviors must be made. This assumption can cause clustering processes to under-fit or over-fit the training data. In a poorly understood domain, knowing the number of expected behaviors a priori is unrealistic and in an extremely large domain, correctly fitting the training data is difficult. To overcome these obstacles, this thesis takes a Bayesian approach and applies a Dirichlet process (DP) prior over behaviors, which uses experience to reduce the likelihood of over-fitting or under-fitting the model complexity. Additionally, the DP maintains a probability mass associated with a novel behavior and can address countably infinite behaviors. This learning technique is applied to modeling agents driving in an urban setting. The learned DP-based driver behavior model is first demonstrated on a simulated city. Building on successful simulation results, the methodology is applied to GPS data of taxis driving around Boston. Accurate prediction of future vehicle behavior from the model is shown in both domains.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008.
Includes bibliographical references (p. 91-94).
Subjects
Aeronautics and Astronautics.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
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