A safe and efficient lane change decision-making strategy of autonomous driving based on deep reinforcement learning

HIGHLIGHTS

• who: Kexuan Lv and collaborators from the School of Automotive Engineering, Wuhan University of Technology, Wuhan, China have published the article: A Safe and Efficient Lane Change Decision-Making Strategy of Autonomous Driving Based on Deep Reinforcement Learning, in the Journal: Mathematics 2022, 10, x FOR PEER REVIEW of /2022/
• what: In this article, the improved DRL algorithm is proposed to manipulate the continuous actions of the AV for LC requests in a lane-decreasing highway environment. The trial ultimate goal of the agent RL is cumulative to obtainreward the optimal continuing and error. The . . .
  

   

  

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