Research on vehicle cruise control based on online asynchronous supervised reinforcement learning

Abstract

This paper proposes a vehicle cruise control strategy based on asynchronous supervised reinforcement learning, which improves the driver’s reception in the process of vehicle deceleration and following. The control strategy takes actor-critic network as the basic control unit, which guides the control strategy to achieve driver-likely cruise effect by adding real driver cruising data into the supervision network. Simultaneously, combined with the driver online feedback mechanism for real-time training, it realizes offline training and online updating of network parameters. The simulation results show that the asynchronous supervised reinforcement learning algorithm can quickly update the parameters of the control network, and constantly update the control strategy by online learning combined with the actual driving data to better simulate the driver’s driving characteristics.