Using Support Vector Machines and Bayesian Filtering for Classifying Agent Intentions at Road Intersections
Author(s)Aoude, Georges S.; How, Jonathan P.
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.
SVM, intersection safety, support vector machines, classification
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