您当前的位置:
首页 >
文章列表页 >
Merging guidance of exclusive lanes for connected and autonomous vehicles based on deep reinforcement learning
更新时间:2025-05-26
    • Merging guidance of exclusive lanes for connected and autonomous vehicles based on deep reinforcement learning

    • Progress has been made in the research of dedicated lanes for autonomous vehicles, effectively guiding vehicles to merge into dedicated lanes, improving traffic efficiency, and providing reference for engineering construction.
    • Journal of Jilin University(Engineering and Technology Edition)   Vol. 53, Issue 9, Pages: 2508-2518(2023)
    • 作者机构:

      1.东南大学 江苏省城市智能交通重点实验室, 南京 211189

      2.西藏大学 工学院, 拉萨 850013

      3.东南大学 交通学院, 南京 211189

    • DOI:10.13229/j.cnki.jdxbgxb.20220106    

      CLC: U491
    • Received:02 February 2022

      Published:01 September 2023

    移动端阅览

  • ZHANG Jian,LI Qing-yang,LI Dan,et al.Merging guidance of exclusive lanes for connected and autonomous vehicles based on deep reinforcement learning[J].Journal of Jilin University(Engineering and Technology Edition),2023,53(09):2508-2518. DOI: 10.13229/j.cnki.jdxbgxb.20220106.

  •  
  •  

0

Views

16

下载量

4

CSCD

Alert me when the article has been cited
提交
Tools
Download
Export Citation
Share
Add to favorites
Add to my album

Related Articles

Modeling interaction policy of autonomous vehicle and pedestrian based on deep reinforcement learning
Double⁃ring adaptive control model of intersection during intelligent and connected environment
Deep deterministic policy gradient caching method for privacy protection in Internet of Vehicles
Deep reinforcement learning optimization scheduling algorithm for continuous production line
LSTM⁃MADDPG multi⁃agent cooperative decision algorithm based on asynchronous collaborative update

Related Author

MA She-qiang
CHEN Yan-yan
YU Peng-cheng
YANG Zhen-ming
HU Wei-chao
ZHANG Jian
WU Kun-run
YANG Min

Related Institution

School of Traffic Management, People’s Public Security University of China
Research Institute for Road Safety of the Ministry of Public Security
Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology
Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies
Jiangsu Province Collaborative Innovation Center for Technology and Application of Internet of Things
0