Agent-Aware State Estimation in Autonomous Vehicles

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.

Authors

• Shane Parr
• Ishan Khatri
• Justin Svegliato
• Shlomo Zilberstein

Keywords

• Hidden Markov models
• Reliability
• State estimation
• Autonomous vehicles
• Intelligent robots
• Multi-agent systems
• Direct Observation
• Hidden Markov Model
• Real-world Data
• Global Status
• Behavioral Observations
• Number Of Agents
• Traffic Light
• Behavior Of Agents
• Environmental Agents
• Factorization
• State Space
• Local State
• Light Signal
• Set Of Observations
• State Observer
• Markov Decision Process
• Local Observations
• Transition Function
• Dynamic Bayesian Network
• External Observer
• Factor Graph
• Direction Of Travel
• Traffic Scenarios
• Worst-case Time Complexity
• State Estimation Problem
• Exact Inference
• Traffic Detection