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dc.contributor.authorBhat, Sanath Govinda
dc.date.accessioned2018-02-14T17:29:51Z
dc.date.available2018-02-14T17:29:51Z
dc.date.issued2017-08
dc.identifier.otherbhat_sanath_g_201708_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/bhat_sanath_g_201708_ms
dc.identifier.urihttp://hdl.handle.net/10724/37116
dc.description.abstractMost automobile manufacturers today have invested heavily in the research and design of implementing autonomy in their cars. One important and challenging problem faced by a self-driven car on highways is merging into the highway from an acceleration ramp. Successful merging needs consideration of the behaviors of cars driving in the outermost highway lane which is adjacent to the merging lane, especially, the behaviors of those cars that would potentially become the leading or following car after a successful merge. We attempt to predict the motivation for the behaviors of those cars driving on the outermost highway lanes near the merging area hypothesizing that they perform a series of tasks each of which is driven by different motivations while passing through each section of the merging area. We use a Hierarchical Bayesian model to model the preferences in each task and the priors over those preferences.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectInverse Reinforcement Learning
dc.subjectHierarchical Bayesian Model
dc.subjectMultitask
dc.subjectHighway Merging
dc.subjectNGSIM
dc.subjectLikelihood Weighting
dc.titleLearning driver preferences for freeway merging using multitask irl
dc.typeThesis
dc.description.degreeMS
dc.description.departmentComputer Science
dc.description.majorComputer Science
dc.description.advisorPrashant Doshi
dc.description.committeePrashant Doshi
dc.description.committeeGauri Datta
dc.description.committeeSuchendra Bhandarkar


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