Pilot selection in the era of Virtual reality: algorithms for accurate and interpretable machine learning models

2025-10-02

AI Summary

To address the challenge of efficient and cost-effective pilot selection amid rapid aviation industry growth, this study proposes a novel assessment framework integrating virtual reality (VR) with explainable machine learning. We collect eye-tracking and flight dynamic data from both professional pilots and novices using a VR-based flight simulator. A端-to-end classification model is developed, combining maximal information coefficient (MIC)-based nonlinear feature selection with support vector machine (SVM) classification. Crucially, we introduce nonlinear association modeling for behavioral features—enhancing both discriminative performance and interpretability. Experimental results demonstrate superior performance: accuracy = 0.93, AUC = 0.96, and F1-score = 0.93—outperforming existing methods across all metrics. This framework establishes a new paradigm for low-cost, high-accuracy, and auditable pilot selection and training evaluation.

Technology Category

• Machine Learning: Dimensionality Reduction/Feature Selection
• Humans and AI: Human-in-the-loop Machine Learning
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Developing machine learning models for pilot selection using VR technology
• Improving prediction accuracy and interpretability of pilot skill assessment
• Identifying discriminative features between experts and novices in aviation

Innovation

• Uses SVM and MIC for pilot selection
• Combines eye tracking with flight dynamics
• Implements VR simulation for skill assessment