Deep reinforcement learning-based low-speed vehicle following decision-making method

The invention discloses a deep reinforcement learning-based low-speed vehicle following decision-making method, which is implemented in the following manner: at first, receiving position, speed and acceleration information of front and back vehicles as an environmental state in real time through the Internet of vehicles, and expressing a present state and behavior of an unmanned vehicle; then, constructing an Actor-Critic framework-based deep reinforcement learning structure; and finally, selecting, by Actor, an appropriate action according to the present environmental state, and continuouslyperforming training and learning through an evaluation made by Critic, thereby obtaining an optimal control strategy to ensure that the unmanned vehicle can be kept at a certain safe distance away from the front and back vehicles and implement automatic low-speed running of the vehicle following the front vehicle under an urban congestion working condition. According to the deep reinforcement learning-based low-speed vehicle following decision-making method, the driving comfort is improved, the traffic safety is also ensured, and the clarity of a congested lane is further improved.