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arXiv · 1704.04322

Belief State Planning for Autonomously Navigating Urban Intersections

Maxime Bouton·Akansel Cosgun·Mykel J. Kochenderfer
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Abstract

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.

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Maxime Bouton, Akansel Cosgun, Mykel J. Kochenderfer. 2017-04-14. Belief State Planning for Autonomously Navigating Urban Intersections. https://arxiv.org/abs/1704.04322

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