个人信息
参与实验室科研项目
复杂环境下非完全信息博弈决策的智能基础模型研究
研究课题
人机共享控制中的非线性仲裁方法研究
学术成果
共撰写/参与撰写专利 0 项，录用/发表论文 2 篇，投出待录用论文3篇。
Journal Articles
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UNA-SAC: An Uncertainty-Aware Nonlinear Arbitration Method for Human–AI Shared Control
Shuyue Jiang,
Yun-Bo Zhao ,
Yu Kang,
Fei Xie,
and Yun-Sheng Zhao
IEEE Trans. Artif. Intell.
2026
[doi]
[pdf]
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A Dual Confidence Evaluation-Based Shared Control Approach for Human-Machine Collaboration
Yaqing Zhou,
Yun-Bo Zhao ,
Pengfei Li,
Xia Tian,
Shuyue Jiang,
and Yu Kang
Neurocomputing
2026
[Abs]
[doi]
[pdf]
Shared control has become a key strategy for enhancing the safety and adaptability of human-machine collaboration systems, particularly in complex and uncertain environments. However, existing rule-based and confidence-based authority allocation approaches often suffer from limited generalizability or excessive reliance on physiological signals, which hinders their practical deployment. This paper proposes a Dual Confidence-Based Shared Control (DC-SC) approach that enables dynamic and interpretable authority allocation by quantifying the decision confidence of both humans and machines. The human confidence model is constructed through a knowledge-task matching function that measures the cognitive alignment between the operator’s expertise and task difficulty, while the machine confidence model assesses decision reliability via an uncertainty-tolerance matching mechanism. These two types of confidence indicators are jointly used to construct a shared control policy, in which the fusion weights are dynamically adjusted using environmental feedback within a policy gradient optimization framework, thereby maximizing human-machine collaborative performance. Theoretical analysis validates the soundness of the confidence models, and experiments conducted in benchmark environments such as LunarLander and UAV path planning demonstrate that DC-SC significantly outperforms both reinforcement learning baselines and traditional shared control approaches in terms of policy performance and system safety.
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