Analysis of Visual Cues During Landing Phase by Using Neural Network Modeling
Analysis of Visual Cues During Landing Phase by Using Neural Network Modeling
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DOI：
10.2514/1.30208
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发表时间：
2007-11
影响因子：
2.2
通讯作者：
Ryota Mori;Shinji Suzuki;Y. Sakamoto;H. Takahara
中科院分区：
文献类型：
--
作者：
Ryota Mori;Shinji Suzuki;Y. Sakamoto;H. Takahara
提出了一种神经网络建模方法来分析运输机降落时飞行员使用视觉提示的情况。利用飞行模拟器获得的视觉提示和飞行员控制输入的时间序列可以定量估计视觉提示和飞行员控制输入之间的关系。本文对地平线、跑道形状和跑道标记等视觉线索的重要性进行了比较。通过使用飞行模拟器，在飞行员有意改变他或她对视觉线索的注意力的所有情况下获得神经网络模型。贡献率分析反映了对每个视觉线索的关注。蒙特卡罗着陆仿真表明，得到的神经网络模型鲁棒性存在差异。这证实了及时选择适当的视觉提示对于平稳安全着陆是必要的。
A neural network modeling approach has been developed to analyze the pilot's use of visual cues for landing a transport airplane. Time sequences of the visual cues and pilot control inputs obtained by using a flight simulator can be analyzed to quantitatively estimate the relationship between the visual cues and the pilot control inputs. In this paper, visual cues such as the horizon, runway shape, and runway marker are compared based on their importance. By using a flight simulator, neural network models are obtained in all cases wherein a pilot intentionally alters his or her attentiveness to the visual cues. The contribution ratios analysis reflects the attentiveness to each visual cue. The Monte Carlo landing simulation shows the difference in robustness of each obtained neural network model. It is confirmed that the timely choice of the appropriate visual cue is necessary for a smooth and safe landing.