  The ability to estimate human intentions and interact with human drivers
intelligently is crucial for autonomous vehicles to successfully achieve their
objectives. In this paper, we propose a game theoretic planning algorithm that
models human opponents with an iterative reasoning framework and estimates
human latent cognitive states through probabilistic inference and active
learning. By modeling the interaction as a partially observable Markov decision
process with adaptive state and action spaces, our algorithm is able to
accomplish real-time lane changing tasks in a realistic driving simulator. We
compare our algorithm's lane changing performance in dense traffic with a
state-of-the-art autonomous lane changing algorithm to show the advantage of
iterative reasoning and active learning in terms of avoiding overly
conservative behaviors and achieving the driving objective successfully.
