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.
@article{arxiv.1704.04322,
title = {Belief State Planning for Autonomously Navigating Urban Intersections},
author = {Maxime Bouton and Akansel Cosgun and Mykel J. Kochenderfer},
journal= {arXiv preprint arXiv:1704.04322},
year = {2017}
}