Using Support Vector Machines and Bayesian Filtering for Classifying Agent Intentions at Road Intersections

dc.contributor.author	Aoude, Georges S.
dc.contributor.author	How, Jonathan P.
dc.date.accessioned	2009-09-15T22:21:04Z
dc.date.available	2009-09-15T22:21:04Z
dc.date.issued	2009-09-15T22:21:04Z
dc.identifier.uri	http://hdl.handle.net/1721.1/46720
dc.description.abstract	Classifying other agents’ intentions is a very complex task but it can be very essential in assisting (autonomous or human) agents in navigating safely in dynamic and possibly hostile environments. This paper introduces a classification approach based on support vector machines and Bayesian filtering (SVM-BF). It then applies it to a road intersection problem to assist a vehicle in detecting the intention of an approaching suspicious vehicle. The SVM-BF approach achieved very promising results.	en
dc.description.sponsorship	Ford Motor Company, Le Fonds Quebecois de la Recherche sur la Nature et les Technologies (FQRNT)	en
dc.language.iso	en_US	en
dc.relation.ispartofseries	;ACL09-02
dc.subject	SVM	en
dc.subject	intersection safety	en
dc.subject	support vector machines	en
dc.subject	classification	en
dc.title	Using Support Vector Machines and Bayesian Filtering for Classifying Agent Intentions at Road Intersections	en
dc.type	Technical Report	en

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