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Yun Li - IEEE Xplore Author Profile
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Yun Li

Publications
3
Citations
5
Publications by Year
20242024
Co-Authors:
Show All Co-Authors (7)
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Yun Li

Affiliation

Graduate School of Information and Science Technology
The University of Tokyo
Tokyo, Japan

Publication Topics

Biography

Yun Li (Member, IEEE) received the B.S. degree in vehicle engineering from Jilin University in 2021 and the M.S. degree from the Department of Information and Communications Engineering, Tokyo Institute of Technology, in 2023. He is currently pursuing the Ph.D. degree with the Graduate School of Information and Science Technology, The University of Tokyo.(Based on document published on 18 September 2024).
Publications
3
Citations
5
Publications by Year
20242024
Co-Authors:
Show All Co-Authors (7)
Author's Published Works

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A Survey on Recent Advancements in Autonomous Driving Using Deep Reinforcement Learning: Applications, Challenges, and Solutions

Rui Zhao;Yun Li;Yuze Fan;Fei Gao;Manabu Tsukada;Zhenhai Gao

IEEE Transactions on Intelligent Transportation Systems
Year: 2024 | Volume: 25, Issue: 12 | Journal Article |
Cited by: Papers (1)
Autonomous driving (AD) endows vehicles with the capability to drive partly or entirely without human intervention. AD agents generate driving policies based on online perception results, which are crucial to the realization of safe, efficient, and comfortable driving behaviors, particularly in high-dimensional and stochastic traffic scenarios. Currently, deep reinforcement learning (DRL) techniqu...Show More

A Survey on Recent Advancements in Autonomous Driving Using Deep Reinforcement Learning: Applications, Challenges, and Solutions

Rui Zhao;Yun Li;Yuze Fan;Fei Gao;Manabu Tsukada;Zhenhai Gao

Year: 2024 | Volume: 25, Issue: 12 | Journal Article |

Centralized Cooperation for Connected Autonomous Vehicles at Intersections by Safe Deep Reinforcement Learning

Rui Zhao;Yun Li;Kui Wang;Yuze Fan;Fei Gao;Zhenhai Gao

IEEE Transactions on Mobile Computing
Year: 2024 | Volume: 23, Issue: 12 | Journal Article |
Connected and automated vehicles (CAVs) have the potential to transform traffic management, especially at intersections. Traditional traffic signals might become obsolete with the implementation of autonomous intersection management (AIM) systems, which aim for efficient and safe vehicle flow. Current AIM methods often rely on optimization control algorithms, which are not computationally efficien...Show More

Centralized Cooperation for Connected Autonomous Vehicles at Intersections by Safe Deep Reinforcement Learning

Rui Zhao;Yun Li;Kui Wang;Yuze Fan;Fei Gao;Zhenhai Gao

Year: 2024 | Volume: 23, Issue: 12 | Journal Article |

Multi-Agent Constrained Policy Optimization for Conflict-Free Management of Connected Autonomous Vehicles at Unsignalized Intersections

Rui Zhao;Yun Li;Fei Gao;Zhenhai Gao;Tianyao Zhang

IEEE Transactions on Intelligent Transportation Systems
Year: 2024 | Volume: 25, Issue: 6 | Journal Article |
Cited by: Papers (4)
Autonomous Intersection Management (AIM) systems present a new paradigm for conflict-free cooperation of connected autonomous vehicles (CAVs) at road intersections, the aim of which is to eliminate collisions and improve the traffic efficiency and ride comfort. Given the challenges of current centralized coordination methods in balancing high computational efficiency and robust safety assurance, t...Show More

Multi-Agent Constrained Policy Optimization for Conflict-Free Management of Connected Autonomous Vehicles at Unsignalized Intersections

Rui Zhao;Yun Li;Fei Gao;Zhenhai Gao;Tianyao Zhang

Year: 2024 | Volume: 25, Issue: 6 | Journal Article |

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