用虚拟现实与机器学习结合，精准识别飞行员能力差异。
Pilot selection in the era of Virtual reality: algorithms for accurate and interpretable machine learning models
- 融合眼动与飞行数据，用SVM+MIC算法建模
- 准确率93%、AUC达96%，优于其他算法
- 首次实现基于眼动的飞行员智能筛选
随着航空业快速发展，亟需高效选拔大量飞行机组。本研究招募了23名中国东航飞行员和23名清华大学的飞行新手，采用结合机器学习与虚拟现实技术的新方法，区分不同飞行技能群体的特征。结果表明，SVM配合MIC特征选择方法在各项指标上表现最优：准确率0.93，AUC为0.96，F1得分为0.93，显著优于四种其他分类器及两种特征选择方法。MIC方法可识别与标签具有非线性关系的特征，而非简单过滤。该SVM+MIC新方案超越现有所有飞行员选拔算法，是首个基于眼动追踪与飞行动力学数据的实现。研究构建的VR仿真平台与算法可用于飞行员选拔与训练。
原文摘要 · Abstract (English)
With the rapid growth of the aviation industry, there is a need for a large number of flight crew. How to select the right pilots in a cost-efficient manner has become an important research question. In the current study, twenty-three pilots were recruited from China Eastern Airlines, and 23 novices were from the community of Tsinghua University. A novel approach incorporating machine learning and virtual reality technology was applied to distinguish features between these participants with different flight skills. Results indicate that SVM with the MIC feature selection method consistently achieved the highest prediction performance on all metrics with an Accuracy of 0.93, an AUC of 0.96, and an F1 of 0.93, which outperforms four other classifier algorithms and two other feature selection methods. From the perspective of feature selection methods, the MIC method can select features with a nonlinear relationship to sampling labels, instead of a simple filter-out. Our new implementation of the SVM + MIC algorithm outperforms all existing pilot selection algorithms and perhaps provides the first implementation based on eye tracking and flight dynamics data. This study's VR simulation platforms and algorithms can be used for pilot selection and training.
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