Autonomous lane change model with integrated control based on deep reinforcement learning
In order to solve the problem of fast and safe lane change of autonomous vehicle,an autonomous lane change control model based on deep reinforcement learning was proposed and improved.Firstly,the vehicle dynamic motion model was established,secondly,the depth deterministic strategy gradient(DDPG)algorithm was used to update the model,and finally,the learned control strategy was co-simulated by MATLAB/CarSim software.In order to make the model more realistic and reliable,CarSim software was integrated into the training of the agent.Meanwhile,a steering wheel angle output model based on sampling time was proposed to solve the problem that the control effect of the traditional model was not ideal in the later period of lane change.The results showed that under the speed of 60 km/h and 80 km/h,the control process from lane change to stable running of the model was smoother and faster than before the improvement,which verified that the model could realize the autonomous lane change control under the general speed,and had certain significance for the research of autonomous lane change of vehicles.
automatic driving vehicleautonomous lane change modeldeep reinforcement learningtrajectory planning and trackingdeep deterministic policy gradient(DDPG)algorithm
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