Advanced Search
DSpace@MIT

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

Research and Teaching Output of the MIT Community

Show simple item record

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


Files in this item

Name Size Format Description
Aoude_How_SVM_BF.pdf 1.219Mb PDF

The following license files are associated with this item:

This item appears in the following Collection(s)

Show simple item record

MIT-Mirage