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

来源：

2020 AIAA/IEEE 39TH DIGITAL AVIONICS SYSTEMS CONFERENCE (DASC) PROCEEDINGS | 2020年

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

页数：7

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