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.

翻译：在移动机器人学和自动驾驶领域，将智能体间的交互建模为非合作动态博弈的纳什均衡是一种自然的方法。这些方法本质上依赖于激光雷达和摄像头等传感器的观测来识别参与博弈的智能体，因此当部分智能体被遮挡时便面临困难。为应对这一局限，本文提出了一种遮挡感知的博弈论推断方法，用于估计可能被遮挡智能体的位置，并同时推断可见与被遮挡智能体的意图，从而最优地解释可见智能体的观测数据。此外，我们提出了一种基于遮挡感知应急博弈的滚动时域规划策略，旨在能够在存在潜在遮挡智能体的场景中进行导航。蒙特卡洛仿真验证了我们的方法，证明其能够利用可见智能体的噪声观测，准确估计博弈模型以及可见与被遮挡智能体的轨迹。与忽略遮挡的基线方法相比，我们的规划流程也显著提升了导航安全性。