Unmanned Vehicles: Safety Management Systems and Safety Functions

Jharko, Elena; Abdulova, Ekaterina; Iskhakov, Andrey

doi:10.1007/978-981-16-1483-5_11

Cited by 8 publications

(1 citation statement)

References 22 publications

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“…Elmahjub E et al [14] discussed the transformative extraction of key features from videos and images obtained by computer vision algorithms, as well as improvements in traffic flow analysis methods, risk assessment, accident investigation, and road damage assessment. However, the obstacles faced by the large-scale deployment of unmanned vehicle cooperative operation technology are complex and interrelated, and strategies to overcome these obstacles and their impacts also need to be addressed [15][16][17] . In summary, while there is extensive research on vehicle and aircraft conflict detection and risk assessment methods, studies considering the mixed operation of unmanned vehicles and humanpiloted aircraft within airport environments are scarce.…”

Section: Introductionmentioning

confidence: 99%

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

2024

jeis

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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.

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Section: Introductionmentioning

confidence: 99%