Authors :
Jherrod Thomas
Volume/Issue :
Volume 9 - 2024, Issue 1 - January
Google Scholar :
http://tinyurl.com/uekc66hv
Scribd :
http://tinyurl.com/24x8395n
DOI :
https://doi.org/10.5281/zenodo.10670478
Abstract :
The incorporation of Machine Learning (ML)
and Artificial Intelligence (AI) technologies in automotive
safety systems poses significant opportunities and
challenges for com-plying with the ISO 26262 standard, a
critical framework for ensuring functional safety in road
vehicles. This paper investigates the potential of ML and
AI to enhance ISO 26262 compliance, examining both the
perks and the perils inherent in this endeavor. It provides
a comprehensive overview of the ISO 26262 standard, its
evolution, framework, and practical applications. It also
elucidates the diverse categories and levels of ML and AI,
the principles and features of Generative Pre-trained
Transformer (GPT) models and their variants, and their
real-world applications in various domains. Furthermore,
it discusses the implications of ML and AI for ISO 26262
compliance in various phases and aspects, such as design,
testing, validation, and operation. It also addresses the
ethical and societal considerations of applying ML and AI
in this context. The paper concludes by synthesizing the
findings, summarizing the main insights, and proposing
avenues for future research. The paper aims to contribute
to the ongoing discussion on integrating cutting-edge
technologies in automotive safety and to pave the way for
more robust, efficient, and reliable safety systems in the
automotive industry.
Keywords :
ISO 26262, Automotive Safety, Machine Learning, Artificial Intelligence, Compliance, Electrical and Electronic Systems, Risk Assessment, Technological Integration, Hazard Analysis, Automotive Industry Standards.
The incorporation of Machine Learning (ML)
and Artificial Intelligence (AI) technologies in automotive
safety systems poses significant opportunities and
challenges for com-plying with the ISO 26262 standard, a
critical framework for ensuring functional safety in road
vehicles. This paper investigates the potential of ML and
AI to enhance ISO 26262 compliance, examining both the
perks and the perils inherent in this endeavor. It provides
a comprehensive overview of the ISO 26262 standard, its
evolution, framework, and practical applications. It also
elucidates the diverse categories and levels of ML and AI,
the principles and features of Generative Pre-trained
Transformer (GPT) models and their variants, and their
real-world applications in various domains. Furthermore,
it discusses the implications of ML and AI for ISO 26262
compliance in various phases and aspects, such as design,
testing, validation, and operation. It also addresses the
ethical and societal considerations of applying ML and AI
in this context. The paper concludes by synthesizing the
findings, summarizing the main insights, and proposing
avenues for future research. The paper aims to contribute
to the ongoing discussion on integrating cutting-edge
technologies in automotive safety and to pave the way for
more robust, efficient, and reliable safety systems in the
automotive industry.
Keywords :
ISO 26262, Automotive Safety, Machine Learning, Artificial Intelligence, Compliance, Electrical and Electronic Systems, Risk Assessment, Technological Integration, Hazard Analysis, Automotive Industry Standards.
