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.
Belief state planning for autonomously navigating urban intersections
01.06.2017
121444 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Belief State Planning for Autonomously Navigating Urban Intersections
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