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Artificial Intelligence for Safety-Critical Systems in Industrial and Transportation Domains : A Survey

Authors :
Perez-Cerrolaza, Jon
Abella, Jaume
Borg, Markus
Donzella, Carlo
Cerquides, Jesús
Cazorla, Francisco J.
Englund, Cristofer
Tauber, Markus
Nikolakopoulos, George
Flores, Jose Luis
Perez-Cerrolaza, Jon
Abella, Jaume
Borg, Markus
Donzella, Carlo
Cerquides, Jesús
Cazorla, Francisco J.
Englund, Cristofer
Tauber, Markus
Nikolakopoulos, George
Flores, Jose Luis
Publication Year :
2024

Abstract

Artificial Intelligence (AI) can enable the development of next-generation autonomous safety-critical systems in which Machine Learning (ML) algorithms learn optimized and safe solutions. AI can also support and assist human safety engineers in developing safety-critical systems. However, reconciling both cutting-edge and state-of-the-art AI technology with safety engineering processes and safety standards is an open challenge that must be addressed before AI can be fully embraced in safety-critical systems. Many works already address this challenge, resulting in a vast and fragmented literature. Focusing on the industrial and transportation domains, this survey structures and analyzes challenges, techniques, and methods for developing AI-based safety-critical systems, from traditional functional safety systems to autonomous systems. AI trustworthiness spans several dimensions, such as engineering, ethics and legal, and this survey focuses on the safety engineering dimension.

Details

Database :
OAIster
Notes :
application/pdf, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1457586977
Document Type :
Electronic Resource
Full Text :
https://doi.org/10.1145.3626314