Autonomous systems often operate in environments where the behavior of multiple agents is coordinated by a shared global state. Reliable estimation of the global state is thus critical for successfully operating in a multi-agent setting. We introduce agent-aware state estimation -- a framework for calculating indirect estimations of state given observations of the behavior of other agents in the environment. We also introduce transition-independent agent-aware state estimation -- a tractable class of agent-aware state estimation -- and show that it allows the speed of inference to scale linearly with the number of agents in the environment. As an example, we model traffic light classification in instances of complete loss of direct observation. By taking into account observations of vehicular behavior from multiple directions of traffic, our approach exhibits accuracy higher than that of existing traffic light-only HMM methods on a real-world autonomous vehicle data set under a variety of simulated occlusion scenarios.

中文翻译：

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自动驾驶汽车中的智能体感知状态估计

自治系统通常在多个代理的行为由共享的全局状态协调的环境中运行。因此，对全局状态的可靠估计对于在多代理设置中成功运行至关重要。我们引入了智能体感知状态估计——一个框架，用于计算对环境中其他智能体行为的观察的间接状态估计。我们还介绍了与转移无关的代理感知状态估计——一种易于处理的代理感知状态估计——并表明它允许推理速度与环境中代理的数量成线性比例。例如，我们在完全失去直接观察的情况下对交通灯分类进行建模。通过考虑来自多个交通方向的车辆行为的观察，