A Lateral Control Method of Autonomous Driving Based on Fuzzy Convergence and Imitative Reinforcement Learning
In view of the strong coupling of each control factor in the lateral control of autonomous vehicles,it is difficult for the control method relying on the ideal model to completely decouple and migrate from the simulation environment to the actual vehicle,and the problem that the convergence speed of the reinforcement learning method in the lateral control of autonomous vehicles is not ideal,the fuzzy inference machine and the similarity of the simulation reinforcement learning in the lateral control factors of vehicles are used to combine the two.A fuzzy inference machine is used as the initialization condition for simulated reinforcement learning,and provide guidance for the learning process,thus achieving rapid convergence of the learning process.The MATLAB/Carla simulation and vehicle test are applied to verify the control method.The results show that the method can significantly reduce the number of simulation reinforcement learning iterations,achieve better vehicle lateral control performance in 500 full path iterations,and achieve good control effect in both simulation and real environment on the basis of not relying on the ideal mathematical model and not having to carry out in-depth optimization of the fuzzy inference device.
万方数据
维普
