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Explores safety protocols in AI-driven vehicles, suggesting methods for improving compliance and reliability.
With the increasing presence of autonomous SAE level 3 and level 4, which incorporate artificial intelligence software, along with the complex technical challenges they present, it is critical to maintain a strong focus on functional safety and robust software design. This paper explores the necessary safety architecture and systematic approach for automotive software and hardware, including fail-soft handling of automotive safety integrity level (ASIL) D (highest level of safety integrity), adoption of machine learning (ML), and artificial intelligence (AI) in automotive safety architecture. By addressing the unique challenges presented by increasing AI-based automotive software, we proposed various techniques, such as mitigation strategies and safety failure analysis, to enforce safety compliance and reliability of automotive software, as well as the role of AI in software reliability throughout the data lifecycle.
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Vyas et al. (2024) studied this question.