In this paper, we study the global traffic density and emergent traffic behavior of several hundreds of intelligent vehicles, as a function of V2V communication (for the ego vehicle to perceive traffic) and path-finding heuristics (for the ego vehicle to reach its destination), in urban environments. Ideal/realistic/no V2V communication modes are crossed with straight-line/towards-most-crowded/towards-least-crowded path-finding heuristics to measure the average trip speed of each vehicle. The behaviours of intelligent vehicles are modelled by a finite state automaton. The V2V communication model is also built based on signal propagation models in an intersection scenario and a Markov-chain based MAC model. Our experiments in simulation over up to 400 vehicles exhibit attractive insights: 1) communication's impact is positive for the performance of the emergent vehicles' behaviour, however, 2) the path-finding heuristics may not obtain their expected collective behaviour due to the communications errors in realistic road environment.


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    Title :

    Emergent behaviors and traffic density among heuristically-driven intelligent vehicles using V2V communication


    Contributors:


    Publication date :

    2014-11-01


    Size :

    2365745 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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