  In mobile robotics and autonomous driving, it is natural to model agent
interactions as the Nash equilibrium of a noncooperative, dynamic game. These
methods inherently rely on observations from sensors such as lidars and cameras
to identify agents participating in the game and, therefore, have difficulty
when some agents are occluded. To address this limitation, this paper presents
an occlusion-aware game-theoretic inference method to estimate the locations of
potentially occluded agents, and simultaneously infer the intentions of both
visible and occluded agents, which best accounts for the observations of
visible agents. Additionally, we propose a receding horizon planning strategy
based on an occlusion-aware contingency game designed to navigate in scenarios
with potentially occluded agents. Monte Carlo simulations validate our
approach, demonstrating that it accurately estimates the game model and
trajectories for both visible and occluded agents using noisy observations of
visible agents. Our planning pipeline significantly enhances navigation safety
when compared to occlusion-ignorant baseline as well.
