Identifying Occluded Agents in Dynamic Games with Noise-Corrupted Observations
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To provide safe and efficient services, robots must rely on observations from sensors (lidar, camera, etc.) to have a clear knowledge of the environment. In multi-agent scenarios, robots must further reason about the intrinsic motivation underlying the behavior of other agents in order to make inferences about their future behavior. Occlusions, which often occur in robot operating scenarios, make the decision-making of robots even more challenging. In scenarios without occlusions, dynamic game theory provides a solid theoretical framework for predicting the behavior of agents with different objectives interacting with each other over time. Prior work proposed an inverse dynamic game method to recover the game model that best explains observed behavior. However, an apparent shortcoming is that it does not account for agents that may be occluded. Neglecting these agents may result in risky navigation decisions. To address this problem, we propose a novel inverse dynamic game technique to infer the behavior of occluded, unobserved agents that best explains the observation of visible agents' behavior, and simultaneously to predict the agents' future behavior based on the recovered game model. We demonstrate our method in several simulated scenarios. Results reveal that our method robustly estimates agents' objectives and predicts trajectories for both visible and occluded agents from a short sequence of noise corrupted trajectory observation of only the visible agents.
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