Connected autonomous vehicles have the potential to cooperate at a higher level than human drivers, for instance to navigate busy intersections with relaxed traffic rules. This paper presents a novel system for autonomous traffic management through intersections based on distributed cooperative intelligence. In this framework each vehicle decides its own motion, rather than entrusting control of all vehicles to a central agent which then becomes a single point of failure. The motion plans are developed by reinforcement learning on simulations of intersection traffic. The developed system is shown to route vehicles to their destination in a safe and timely manner. In the near future, this system can be deployed to as a collision warning/collision avoidance aid to human drivers by selectively acting on the discrepancy between a human's behavior and the AI-determined safe behavior.


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    Title :

    A distributed, collective intelligence framework for collision-free navigation through busy intersections


    Contributors:


    Publication date :

    2016-11-01


    Size :

    309890 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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