# Belief State Planning for Autonomously Navigating Urban Intersections

arXiv: https://arxiv.org/abs/1704.04322 · HTML: https://mlsanity.com/paper/1704.04322/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2017.

## Built on by (most cited first, 11 of 11)

- [Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning](https://mlsanity.com/paper/2102.03119/) (2021)
- [Occlusion-Aware Crowd Navigation Using People as Sensors](https://mlsanity.com/paper/2210.00552/) (2022)
- [Learning to Navigate Intersections with Unsupervised Driver Trait Inference](https://mlsanity.com/paper/2109.06783/) (2021)
- [Multi-task Safe Reinforcement Learning for Navigating Intersections in Dense Traffic](https://mlsanity.com/paper/2202.09644/) (2022)
- [Robust Driving Policy Learning with Guided Meta Reinforcement Learning](https://mlsanity.com/paper/2307.10160/) (2023)
- [Intention-Aware Navigation in Crowds with Extended-Space POMDP Planning](https://mlsanity.com/paper/2206.10028/) (2022)
- [Generalizing Decision Making for Automated Driving with an Invariant Environment Representation using Deep Reinforcement Learning](https://mlsanity.com/paper/2102.06765/) (2021)
- [Strategic Decisions Survey, Taxonomy, and Future Directions from Artificial Intelligence Perspective](https://mlsanity.com/paper/2210.12373/) (2022)
- [Efficient Sampling in POMDPs with Lipschitz Bandits for Motion Planning in Continuous Spaces](https://mlsanity.com/paper/2106.04206/) (2021)
- [Risk-Constrained Interactive Safety under Behavior Uncertainty for Autonomous Driving](https://mlsanity.com/paper/2102.03053/) (2021)
- [Decision Making for Autonomous Driving in Interactive Merge Scenarios via Learning-based Prediction](https://mlsanity.com/paper/2303.16821/) (2023)

## Abstract (arXiv)

Urban intersections represent a complex environment for autonomous vehicles with many sources of uncertainty. The vehicle must plan in a stochastic environment with potentially rapid changes in driver behavior. Providing an efficient strategy to navigate through urban intersections is a difficult task. This paper frames the problem of navigating unsignalized intersections as a partially observable Markov decision process (POMDP) and solves it using a Monte Carlo sampling method. Empirical results in simulation show that the resulting policy outperforms a threshold-based heuristic strategy on several relevant metrics that measure both safety and efficiency.