基于双估计强化学习结合前向预测控制的自动驾驶运动控制研究

Motion control research is an important part to achieve the goal of autonomous driving. To solve the problem of suboptimal control sequence due to the limitation of single-step decision in traditional reinforcement learning algorithm,a motion control framework based on the combination of double estimator reinforcement learning algorithm and forward predictive control method(DEQL-FPC)is proposed. In this framework,double estimators are introduced to solve the problem of action overestimation of traditional reinforcement learning methods and improve the speed of optimization. The forward predictive multi-step decision making method is designed to replace the single step decision making of traditional reinforcement learning so as to effectively improve the performance of global control strategies. Through virtual driving environment simulation,the superiority of the control framework applied in path tracking and safe obstacle avoidance of autonomous vehicles is proved,and the accuracy,safety,rapidity and comfort of motion control are guaranteed.