Decision-making Method of Economic Autonomous Vehicle Based on Improved DDPG
The existing autonomous driving technology mostly focuses on safety,but neglects its economy.To address this issue,this article proposes an economic autonomous driving decision-making method based on an improved Deep Deterministic Policy Gradient(DDPG)algorithm.This article first analyzes the power consumption factors of car driving.Design an economic reward function based on the motor efficiency of the car to optimize its driving efficiency.Secondly,by introducing expert experience guidance and a dual experience pool dynamic replay strategy,the model can flexibly and efficiently utilize experience pool data to improve convergence speed and stability.Meanwhile,improving the online critic network and designing a dual online critic network can reduce overestimation of Q value.Finally,a simula-tion environment is built in CARLA to verify the proposed algorithm.The results show that the improved algorithm is superior to the original DDPG algorithm in terms of cumulative rewards,convergence speed and stability,and effectively improves the economy and energy efficiency of autonomous vehicle.
万方数据
维普
