This work presents a supervised reinforcement learning (SRL)-based framework for longitudinal vehicle dynamics' control of the cooperative adaptive cruise control (CACC) system. By incorporating a supervised network trained by real driving data into the actor-critic framework, the training success rate is improved, and the driver characteristics can be learned by the actor to achieve a human-like CACC controller.
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