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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    Title :

    Belief state planning for autonomously navigating urban intersections


    Contributors:


    Publication date :

    2017-06-01


    Size :

    121444 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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