Abstract

This paper proposed a data-driven reinforcement learning control method to achieve optimal control of trajectory tracking for the centralized connected vehicle platoon model. It minimized distance and velocity error and improved fuel effi-ciency, where autonomous vehicles transmit and receive each other's vehicle status data via wireless vehicle-to-vehicle (V2V) communication device. Adaptive dynamic programming techniques are used to obtain optimal tracking control strategy in the presence of unknown system dynamics. The effectiveness of the proposed method is verified by an online learning control simulation of connected vehicles.

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