aerialvehicles {bnRep} | R Documentation |
aerialvehicles Bayesian Network
Description
Analysis and assessment of risks to public safety from unmanned aerial vehicles using fault tree analysis and Bayesian network.
Format
A discrete Bayesian network to analyze critical risks associated with unmanned aerial vehicles. Probabilities were given within the referenced paper. The vertices are:
- X1
Mechanical failures (yes, no);
- X2
Battery failures (yes, no);
- X3
Flight control system failures (yes, no);
- X4
Gust (yes, no);
- X5
Rain and snow (yes, no);
- X6
Thunderstorm (yes, no);
- X7
Visibility (yes, no);
- X8
Communication link failures (yes, no);
- X9
GPS failures (yes, no);
- X10
Ostacles (yes, no);
- X11
Route planning issues (yes, no);
- X12
Unclear airspace division (yes, no);
- X13
Unqualified knowledge and skills (yes, no);
- X14
Weak safety awareness (yes, no);
- X15
Lack of experience (yes, no);
- X16
Careless (yes, no);
- X17
Fatigue (yes, no);
- X18
Violations (yes, no);
- X19
Lack of legal awareness (yes, no);
- X20
Psychological problems (yes, no);
- X21
Undefined subject of supervision responsibility (yes, no);
- X22
Lack of unified industry standard (yes, no);
- X23
Unclear airworthiness certification procedures (yes, no);
- X24
Long flight approval cycle (yes, no);
- X25
Weak laws and regulations (yes, no);
- X26
Inadequate training system (yes, no);
- X27
Lack of supervision system (yes, no);
Value
An object of class bn.fit
. Refer to the documentation of bnlearn
for details.
References
Xiao, Q., Li, Y., Luo, F., & Liu, H. (2023). Analysis and assessment of risks to public safety from unmanned aerial vehicles using fault tree analysis and Bayesian network. Technology in Society, 73, 102229.