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As trials for integrated UAVs into national airspace, new risks and safety challenges have emerged. However, there is limited research on the impact of accidents and the effectiveness of mitigation measures. This study presents a data-driven approach using an enhanced Naive Bayes network to analyze the relationships between various risk factors and evaluate mitigation strategies. By considering environmental, technological, and human factors, the study con-structs a Bayesian network to identify key risk factors and assess how different strategies reduce accident probabilities. The research highlights twelve major hazards in integrated drone operations and demonstrates how proposed measures can effectively lower these risks.

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Effectiveness Evaluation of UAV Risk Mitigation Measures Based on Bayesian Networks

  • 会议论文

  • 发表日期：

    2026年

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  • Jiameilin Lin,
  • Yucong Luo,
  • Chenglong Li,
  • Yuan Zheng

摘要

As trials for integrated UAVs into national airspace, new risks and safety challenges have emerged. However, there is limited research on the impact of accidents and the effectiveness of mitigation measures. This study presents a data-driven approach using an enhanced Naive Bayes network to analyze the relationships between various risk factors and evaluate mitigation strategies. By considering environmental, technological, and human factors, the study con-structs a Bayesian network to identify key risk factors and assess how different strategies reduce accident probabilities. The research highlights twelve major hazards in integrated drone operations and demonstrates how proposed measures can effectively lower these risks.