Agent-aware State Estimation in Autonomous Vehicles
- URL: http://arxiv.org/abs/2108.00366v1
- Date: Sun, 1 Aug 2021 05:31:00 GMT
- Title: Agent-aware State Estimation in Autonomous Vehicles
- Authors: Shane Parr, Ishan Khatri, Justin Svegliato, and Shlomo Zilberstein
- Abstract summary: 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 show that our approach exhibits accuracy higher than that of existing traffic light-only HMM methods on a real-world autonomous vehicle data set.
- Score: 22.60566287125007
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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