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This paper introduces a robust prediction- and cost-function based algorithm for autonomous freeway driving. A prediction engine is built so that the autonomous vehicle is able to estimate human drivers' intentions. A cost function library is used to help behavior planners generate the best strategies. Finally, the algorithm is tested in a real-time vehicle simulation platform used by the Tartan Racing Team for the DARPA Urban Challenge 2007.