Agent-aware State Estimation in Autonomous Vehicles
This addresses the challenge of reliable state estimation for autonomous vehicles in multi-agent settings, particularly under occlusions, though it appears incremental as it builds on existing estimation frameworks.
The paper tackles the problem of global state estimation in multi-agent environments by introducing agent-aware state estimation, which uses observations of other agents' behavior to infer state indirectly, and shows that a tractable variant scales linearly with the number of agents. In a traffic light classification task with simulated occlusions, it achieves higher accuracy than existing HMM methods on real-world autonomous vehicle data.
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.