Robots and autonomous vehicles must rely on sensor observations, e.g., from
lidars and cameras, to comprehend their environment and provide safe, efficient
services. In multi-agent scenarios, they must additionally account for other
agents' intrinsic motivations, which ultimately determine the observed and
future behaviors. Dynamic game theory provides a theoretical framework for
modeling the behavior of agents with different objectives who interact with
each other over time. Previous works employing dynamic game theory often
overlook occluded agents, which can lead to risky navigation decisions. To
tackle this issue, this paper presents an inverse dynamic game technique which
optimizes the game model itself to infer unobserved, occluded agents' behavior
that best explains the observations of visible agents. Our framework
concurrently predicts agents' future behavior based on the reconstructed game
model. Furthermore, we introduce and apply a novel receding horizon planning
pipeline in several simulated scenarios. Results demonstrate that our approach
offers 1) robust estimation of agents' objectives and 2) precise trajectory
predictions for both visible and occluded agents from observations of only
visible agents. Experimental findings also indicate that our planning pipeline
leads to safer navigation decisions compared to existing baseline methods