个人信息
参与实验室科研项目
人机混合智能系统双层智能测试评估技术研究
机载座舱智能系统人机信任动态演化建模方法研究
学术成果
共撰写/参与撰写专利 2 项，录用/发表论文 1 篇，投出待录用论文0篇。
patent
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一种基于通信时延感知的远程驾驶风险预警方法
李鹏飞,
王若山,
赵云波,
刘金伟,
张雯,
and 黄康杰
2025
[Abs]
[pdf]
本发明公开了一种基于通信时延感知的远程驾驶风险预警方法，首先收集目标通信链路的相关数据，对数据进行预处理和特征提取，基于极端梯度提升XGboost算法拟合时延与多特征的非线性关系，实时预测时延的变化情况；构建通信风险评价模型，基于时延变化情况量化出通信风险值；根据车辆动力学模型和驾驶员执行指令序列，预测车辆轨迹；基于车辆轨迹，计算车道偏离风险和障碍物碰撞风险，环境风险值是两者之和；综合考虑环境风险值和通信风险值，根据设计的安全阈值判断是否触发警报。该方法在考虑通信时延的基础上重新设计环境风险评价方式，将环境风险与通信风险综合纳入预警框架，从而为远程驾驶系统提供更好的安全保障。
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无人机轨迹规划方法、装置、设备以及存储介质
朱晓俊,
黄康杰,
张忠政,
潘燕,
and 赵云波
2024
[Abs]
[pdf]
本申请公开了一种无人机轨迹规划方法、装置、设备以及存储介质，涉及人机协同轨迹规划技术领域，该无人机轨迹规划方法包括：通过独轮车模型，将接收到的针对无人机的操作动作转换为输入基元；基于无人机的当前状态和输入基元，构建运动基元树，其中，运动基元树包括多个可行轨迹；根据输入基元，从可行轨迹中，确定无人机的目标轨迹。本申请通过对输入基元的充分利用，生成符合用户意图的目标轨迹，从而在无人机基于目标轨迹飞行的过程中，减少用户对轨迹的调整，从而降低用户的操作负担，提升无人机遥操作的效率。
Journal Articles
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Enhancing Human–Machine Collaboration: A Trust-Aware Trajectory Planning Framework for Assistive Aerial Teleoperation
Qianzheng Zhuang,
Kangjie Huang,
Xiaoran Jin,
Pengfei Li,
Yunbo Zhao,
and Yu Kang
Machines
2025
[Abs]
[doi]
[pdf]
Human–machine collaboration in assistive aerial teleoperation is frequently compromised by trust imbalances, which arise from the vehicle’s complex dynamics and the operator’s constrained perceptual feedback. We introduce a novel framework that enhances collaboration by dynamically integrating a model of human trust into the unmanned aerial vehicle’s trajectory planning. We first propose a Machine-Performance-Dependent trust model, specifically tailored for aerial teleoperation, that quantifies trust based on real-time safety and visibility metrics. This model then informs a trust-aware trajectory planning algorithm, which generates smooth and adaptive trajectories that continuously align with the operator’s trust level and intent inferred from control inputs. Extensive simulations conducted in diverse forest environments validate our approach. The results demonstrate that our method achieves task efficiency comparable to that of a trust-unaware baseline while significantly reducing operator workload and improving trajectory smoothness, achieving reductions of up to 23.2% and 43.2%, respectively, in challenging dense environments. By embedding trust dynamics directly into the trajectory optimization loop, this work pioneers a more intuitive, efficient, and resilient paradigm for assistive aerial teleoperation.
博客文章