金骁然

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 1 项，录用/发表论文 1 篇，投出待录用论文0篇。

patent

1. 基于人机信任的车辆控制方法、设备、介质及程序产品 朱晓俊, 金骁然, 张忠政, 潘燕, and 赵云波 2025 [Abs] [pdf]

   本申请公开了一种基于人机信任的车辆控制方法、设备、介质及程序产品，涉及智能驾驶技术领域，包括：根据获取到的当前车辆的实时超车数据，确定人机信任值；根据人机信任值，确定当前车辆的人类控制权重；基于获取到的人类控制指令、机器控制指令和人类控制权重，控制当前车辆行驶。本申请根据实时超车表现，动态调整当前车辆的自动驾驶系统的人类控制权重，避免了驾驶员突然全权接管车辆的缺陷，从而降低了通过人机混合系统控制车辆时发生交通事故的风险。

Journal Articles

1. 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.