Challenges in Using Neural Networks in Safety-Critical Applications

被引:3
|
作者
Forsberg, Hakan [1 ]
Linden, Joakim [2 ]
Hjorth, Johan [1 ]
Manefjord, Torbjom [3 ]
Daneshtalab, Masoud [1 ]
机构
[1] Malardalen Univ, Sch Innovat Design & Engn, Div Intelligent Future Technol, S-72123 Vasteras, Sweden
[2] Saab Aeronaut, Gripen CD, Jarfalla, Sweden
[3] Saab, Avion Syst, Huskvarna, Sweden
关键词
avionics; safety-critical; machine learning; deep neural networks;
D O I
10.1109/dasc50938.2020.9256519
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
In this paper, we discuss challenges when using neural networks (NNs) in safety-critical applications. We address the challenges one by one, with aviation safety in mind. We then introduce a possible implementation to overcome the challenges. Only a small portion of the solution has been implemented physically and much work is considered as future work. Our current understanding is that a real implementation in a safety-critical system would be extremely difficult. Firstly, to design the intended function of the NN, and secondly, designing monitors needed to achieve a deterministic and fail-safe behavior of the system. We conclude that only the most valuable implementations of NNs should be considered as meaningful to implement in safety-critical systems.
引用
收藏
页数:7
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