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Rui Zhao

Publications
10
Citations
26
Publications by Year
20192025
Co-Authors:
Show All Co-Authors (22)
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Rui Zhao

Affiliation

College of Automotive Engineering
Jilin University
Changchun, China

Publication Topics

Biography

Rui Zhao (Member, IEEE) was born in Liaoyuan, Jilin, China, in 1986. She received the B.S. degree in computer science and technology from Northeast Normal University, in 2009, and the Ph.D. degree in computer science and technology from Jilin University, Changchun, China, in 2017.,She is currently an Associate Professor with the College of Automotive Engineering, Jilin University. She has authored about 30 journal articles and ten patents in China. She authored the monograph “Cyber Security Technology for Intelligent Automotive.” Her research interests include cooperative control, functional safety, cybersecurity, and safety reinforcement learning for connected and automated vehicles.,Prof. Zhao is a member of the Society of Automotive Engi...
Publications
10
Citations
26
Publications by Year
20192025
Co-Authors:
Show All Co-Authors (22)
Author's Published Works

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Constrained Reinforcement-Learning-Enabled Policies With Augmented Lagrangian for Cooperative Intersection Management

Zhenhai Gao;Hesheng Hao;Fei Gao;Rui Zhao

IEEE Internet of Things Journal
Year: 2025 | Volume: 12, Issue: 5 | Journal Article |
Traffic control at signal-free intersections is extensively studied to facilitate cooperative traffic for connected and autonomous vehicles (CAVs). Reinforcement learning (RL) techniques have proven effective for cooperative intersection management (CIM) challenges, but often involves unsafe states due to the arbitrary exploration of trial-and-error mechanism. To tackle the safety challenges assoc...Show More

Constrained Reinforcement-Learning-Enabled Policies With Augmented Lagrangian for Cooperative Intersection Management

Zhenhai Gao;Hesheng Hao;Fei Gao;Rui Zhao

Year: 2025 | Volume: 12, Issue: 5 | Journal Article |

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 |

Constraints Driven Safe Reinforcement Learning for Autonomous Driving Decision-Making

Fei Gao;Xiaodong Wang;Yuze Fan;Zhenhai Gao;Rui Zhao

IEEE Access
Year: 2024 | Volume: 12 | Journal Article |
Although reinforcement learning (RL) methodologies exhibit potential in addressing decision-making and planning problems in autonomous driving, ensuring the safety of the vehicle under all circumstances remains a formidable challenge in practical applications. Current RL methods are predominantly driven by singular reward mechanisms, frequently encountering difficulties in balancing multiple sub-r...Show More

Constraints Driven Safe Reinforcement Learning for Autonomous Driving Decision-Making

Fei Gao;Xiaodong Wang;Yuze Fan;Zhenhai Gao;Rui Zhao

Year: 2024 | Volume: 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 |

Leveraging Monte Carlo Dropout for Uncertainty Quantification in Real-Time Object Detection of Autonomous Vehicles

Rui Zhao;Kui Wang;Yang Xiao;Fei Gao;Zhenhai Gao

IEEE Access
Year: 2024 | Volume: 12 | Journal Article |
Cited by: Papers (4)
With the recent advancements in machine learning technology, the accuracy of autonomous driving object detection models has significantly improved. However, due to the complexity and variability of real-world traffic scenarios, such as extreme weather conditions, unconventional lighting, and unknown traffic participants, there is inherent uncertainty in autonomous driving object detection models, ...Show More

Leveraging Monte Carlo Dropout for Uncertainty Quantification in Real-Time Object Detection of Autonomous Vehicles

Rui Zhao;Kui Wang;Yang Xiao;Fei Gao;Zhenhai Gao

Year: 2024 | Volume: 12 | Journal Article |

Collision Risk Assessment for Intelligent Vehicles Considering Multi-Dimensional Uncertainties

Zhenhai Gao;Mingxi Bao;Taisong Cui;Fangyuan Shi;Xianqing Chen;Wenhao Wen;Fei Gao;Rui Zhao

IEEE Access
Year: 2024 | Volume: 12 | Journal Article |
Cited by: Papers (3)
To ensure the reliability of autonomous driving, the system must be capable of potential hazard identification and appropriate response to prevent accidents. This involves the prediction of possible developments in traffic situations and an evaluation of the potential danger of future scenarios. Precise Collision Risk Assessment (CRA) faces complex challenges due to uncertainties inherent in vehic...Show More

Collision Risk Assessment for Intelligent Vehicles Considering Multi-Dimensional Uncertainties

Zhenhai Gao;Mingxi Bao;Taisong Cui;Fangyuan Shi;Xianqing Chen;Wenhao Wen;Fei Gao;Rui Zhao

Year: 2024 | Volume: 12 | Journal Article |

An Enhanced Vehicle Trajectory Prediction Model Leveraging LSTM and Social-Attention Mechanisms

Senyao Qiao;Fei Gao;Jianghang Wu;Rui Zhao

IEEE Access
Year: 2024 | Volume: 12 | Journal Article |
Cited by: Papers (5)
Accurate trajectory prediction for multiple vehicles in complex social interaction environments is essential for ensuring the safety of autonomous vehicles and improving the quality of their planning and control. The social interactions between vehicles significantly influence their future trajectories. However, traditional trajectory prediction models based on Recurrent Neural Networks (RNN) or C...Show More

An Enhanced Vehicle Trajectory Prediction Model Leveraging LSTM and Social-Attention Mechanisms

Senyao Qiao;Fei Gao;Jianghang Wu;Rui Zhao

Year: 2024 | Volume: 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 (2)
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 |

Mixpred: Video Prediction Beyond Optical Flow

Jie Yan;Guihe Qin;Rui Zhao;Yanhua Liang;Qianyi Xu

IEEE Access
Year: 2019 | Volume: 7 | Journal Article |
Cited by: Papers (3)
Video prediction is a meaningful task for it has a wide range of application scenarios. And it is also a challenging task since it needs to learn the internal representation of a given video for both appearance and motion dynamics. The existing methods regard this problem as a spatiotemporal sequence forecasting problem and try to resolve it in a one-shot fashion, which causes the prediction resul...Show More

Mixpred: Video Prediction Beyond Optical Flow

Jie Yan;Guihe Qin;Rui Zhao;Yanhua Liang;Qianyi Xu

Year: 2019 | Volume: 7 | Journal Article |

Security-Aware Scheduling for TTEthernet-Based Real-Time Automotive Systems

Rui Zhao;Guihe Qin;Ying Lyu;Jie Yan

IEEE Access
Year: 2019 | Volume: 7 | Journal Article |
Cited by: Papers (8)
TTEthernet is a deterministic, congestion-free, and high-bandwidth communication protocol based on the Ethernet standard that provides a powerful network solution for developing safety-critical distributed real-time automotive systems. With the development of intelligence and networking of vehicles, such systems are becoming increasingly connected to external environments; thus, security has becom...Show More

Security-Aware Scheduling for TTEthernet-Based Real-Time Automotive Systems

Rui Zhao;Guihe Qin;Ying Lyu;Jie Yan

Year: 2019 | Volume: 7 | Journal Article |

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