# Agent-aware State Estimation in Autonomous Vehicles

arXiv: https://arxiv.org/abs/2108.00366 · HTML: https://mlsanity.com/paper/2108.00366/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 1 Aug 2021.

## Builds on (most cited first, 6 of 6)

- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [The Complexity of Decentralized Control of Markov Decision Processes](https://mlsanity.com/paper/1301.3836/) (2013)
- [Pomegranate: fast and flexible probabilistic modeling in python](https://mlsanity.com/paper/1711.00137/) (2017)
- [Waymo's Safety Methodologies and Safety Readiness Determinations](https://mlsanity.com/paper/2011.00054/) (2020)
- [Learning to Optimize Autonomy in Competence-Aware Systems](https://mlsanity.com/paper/2003.07745/) (2020)

## Built on by (most cited first, 1 of 1)

- [Fast and Robust State Estimation and Tracking via Hierarchical Learning](https://mlsanity.com/paper/2306.17267/) (2023)

## Abstract (arXiv)

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.