Transactions of Nanjing University of Aeronautics & Astronautics
Cause Analysis of Consumer-Grade UAV Accidents Based on Grounded Theory-Bayesian Network
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Abstract:
In order to reduce the accident rate of consumer-grade unmanned aerial vehicles （UAVs） in daily use scenarios， the accident causes are analyzed based on the accident cases of consumer-grade UAVs. By extracting accident causing factors based on the Grounded theory， the relationship between these factors is analyzed. The Bayesian network for consumer-grade UAV accidents is constructed. With the Grounded theory-Bayesian network， the probability of four types of accidents is inferred： fall， air collision， disappearance， and personal injury. With the posterior probability of each factor being reversely reasoned， the causal chain with the maximum probability of each accident is obtained. After the sensitivity of each factor is analyzed， the key nodes in the network accordingly are inferred. Then the causing factors of consumer-grade UAV accidents are analyzed. The results show that the probability of fall accident is the highest， the fall accident is associated with the probabilistic maximum causal chain of personal injury， and the sensitivity analysis results of each type of accident as the result node are inconsistent.
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This work was supported by the Fundamental Research Funds for the Central Universities （No.3122022103）.
Clc Number:
V279
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