Personalized car following control model based on deep reinforcement learning
In order to solve the following control problem of autonomous vehicle,a personalized following control model based on deep reinforcement learning was proposed and improved.The vehicle safety time interval model was established and integrated into the vehicle kinematics model.The depth deterministic strategy gradient(DDPG)algorithm was used to train the model.Through MATLAB and CarSim,the learned control strategy was verified by joint simulation.In order to make the training results more real and reliable,CarSim was integrated into the training process of the agent.The personalization module was introduced into the model,so that the model could get different driving styles by changing the parameters.The experimental results showed that the model could control the vehicle to drive safely at a certain speed under the condition of acceleration or deceleration of the vehicle in front,and could realize personalized control by changing the training parameters,which had certain guiding significance for the researched of the following process of the autonomous vehicle.
self-driving carspersonalized car following modeldeep reinforcement learningdeep deterministic policy gradient(DDPG)algorithm
NETL
NSTL
万方数据
维普
