  Predicting the behavior of surrounding vehicles is a critical problem in
automated driving. We present a novel game theoretic behavior prediction model
that achieves state of the art prediction accuracy by explicitly reasoning
about possible future interaction between agents. We evaluate our approach on
the NGSIM vehicle trajectory data set and demonstrate lower root mean square
error than state-of-the-art methods.
