A General DRL-Based Framework Using Mode-Selection Tangent Time Projection for Mixed On-Ramp Merging-Reference-Cited by-同舟云学术

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A General DRL-Based Framework Using Mode-Selection Tangent Time Projection for Mixed On-Ramp Merging

Published:2023-09-24 Issue: Volume: Page:3207-3214
ISSN:
Container-title:2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)
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Author:

Ye Linhui¹,Wen Zoutao²,Zhang Hailong³,Ran Bin¹

Affiliation:

1. University of Wisconsin-Madison,Department of Civil and Environment Engineering,Madison,USA

2. Advanced Research Institute of Multidisciplinary Sciences, Beijing Institute of Technology,Beijing,China

3. School of Mechanical and Electrical Engineering, North University of China,Taiyuan,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10420842/10420843/10422296.pdf?arnumber=10422296

Reference28 articles.

1. A Survey on the Coordination of Connected and Automated Vehicles at Intersections and Merging at Highway On-Ramps

2. An Intelligent V2I-Based Traffic Management System

3. A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning

4. Formulation of deep reinforcement learning architecture toward autonomous driving for on-ramp merge

5. Combining Reinforcement Learning with Model Predictive Control for On-Ramp Merging

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An End-to-End HRL-based Framework with Macro-Micro Adaptive Layer for Mixed On-Ramp Merging;2024 IEEE Intelligent Vehicles Symposium (IV);2024-06-02

2. A Deep Reinforcement Learning Based Ramp Metering Control Method Considering Ramp Outflow;IFAC-PapersOnLine;2024

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