Publication Date: 2024/02/16
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.
DOI: https://doi.org/10.5281/zenodo.10670478
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24JAN444.pdf
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