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The ability to estimate human intentions and interact with human driversintelligently is crucial for autonomous vehicles to successfully achieve theirobjectives. In this paper, we propose a game theoretic planning algorithm thatmodels human opponents with an iterative reasoning framework and estimateshuman latent cognitive states through probabilistic inference and activelearning. By modeling the interaction as a partially observable Markov decisionprocess with adaptive state and action spaces, our algorithm is able toaccomplish real-time lane changing tasks in a realistic driving simulator. Wecompare our algorithm's lane changing performance in dense traffic with astate-of-the-art autonomous lane changing algorithm to show the advantage ofiterative reasoning and active learning in terms of avoiding overlyconservative behaviors and achieving the driving objective successfully.

Game Theoretic Decision Making by Actively Learning Human Intentions Applied on Autonomous Driving