Platoon Control of Automatic Vehicles Based on Deep Deterministic Policy Gradient

This paper studies the platoon control of automatic vehicles based on deep deterministic policy gradient (DDPG). The main control goal of the platoon control of automatic vehicles is to control the acceleration of the following vehicles. In this paper to meet the requirement of the convergence quickly for the following automatic vehicles. Therefore this paper introduces the platoon control based on the DDPG algorithm. Through the reasonable design of the reward function in the DDPG algorithm can solve the speed following optimization problem, and modular design of reward function which avoids the existing optimization problems some dangerous driving behaviors such as collision and reversing do not occur during the train. The simulation results show that following vehicles can achieve a quickly learning process under the deep deterministic policy gradient method, and can also track the speed of the leading vehicle with security.