Dynamic collaborative driving involves the motion coordination of multiple vehicles using shared information from vehicles instrumented to perceive their surroundings in order to improve road usage and safety. A basic requirement of any vehicle participating in dynamic collaborative driving is longitudinal control. Without this capability, higher-level coordination is not possible. This paper focuses on the problem of longitudinal motion control. A detailed nonlinear longitudinal vehicle model which serves as the control system design platform is used to develop a longitudinal adaptive control system based on Monte Carlo Reinforcement Learning. The results of the reinforcement learning phase and the performance of the adaptive control system for a single automobile as well as the performance in a multi-vehicle platoon is presented.


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    Title :

    Reinforcement learning of adaptive longitudinal vehicle control for dynamic collaborative driving


    Contributors:


    Publication date :

    2008-06-01


    Size :

    437431 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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