エージェント認識型状態推定：他エージェントの挙動から間接的に状態を推定する枠組み
Agent-aware State Estimation in Autonomous Vehicles
他エージェントの観測からグローバル状態を間接推定する枠組みを提案し、信号機の直接観測が失われた状況で車両挙動から信号状態を高精度に分類できることを実車データで示した。
著者: Shane Parr, Ishan Khatri, Justin Svegliato, Shlomo Zilberstein
分類: cs.RO, cs.AI, cs.MA
原文アブストラクト
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.
