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Monte Carlo Simulation-Based Risk Assessment for Unmanned Ground Equipment Taxiing Guidance

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DOI: 10.23977/jeis.2024.090204 | Downloads: 19 | Views: 299

Author(s)

Qing Zhao 1, Tianxiong Zhang 1, Dezhou Yuan 1, Xinping Zhu 1

Affiliation(s)

1 Air Traffic Management College, Civil Aviation Flight University of China, No. 46, Section 4, Nanchang Road, Guanghan, Sichuan, China

Corresponding Author

Xinping Zhu

ABSTRACT

Due to the potential safety hazards such as incorrect or missed aircraft guidance caused by human-operated guiding, the introduction of unmanned guiding vehicles can effectively reduce these unsafe events. However, the risks associated with unmanned driving guiding vehicles in the process of guiding aircraft taxiing have not yet been thoroughly and quantitatively studied. This paper collects the kinematic parameters of the unmanned driving guiding vehicle during the process of guiding manned aircraft, applies Monte Carlo simulation to generate a dataset of simulated operational processes that cover the entire taxiing guidance process, and introduces three major risk assessment indicators based on the motion process between the unmanned driving guiding vehicle and the manned aircraft during the taxiing guidance process. Through the normalization function of risk evaluation indicator weights and based on the Gaussian distribution that satisfies the normal distribution, a qualitative evaluation of risk levels is conducted based on quantifiable actual operational processes. The results show that quantifiable risk assessment indicators can provide risk evaluation results with stronger real-time reference and offer operable solutions for risk avoidance.

KEYWORDS

Unmanned vehicle, Aircraft, Monte Carlo Simulation, Gaussian Distribution, Risk Assessment

CITE THIS PAPER

Qing Zhao, Tianxiong Zhang, Dezhou Yuan, Xinping Zhu, Monte Carlo Simulation-Based Risk Assessment for Unmanned Ground Equipment Taxiing Guidance. Journal of Electronics and Information Science (2024) Vol. 9: 23-33. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2024.090204.

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