Key Safety Design Overview in AI-driven Autonomous Vehicles

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I whakaputaina i:	arXiv.org (Dec 12, 2024), p. n/a
Kaituhi matua:	Vyas, Vikas
Ētahi atu kaituhi:	Xu, Zheyuan
I whakaputaina:	Cornell University Library, arXiv.org
Ngā marau:	Software reliability Computer program integrity Advanced driver assistance systems Failure analysis Performance evaluation Artificial intelligence Machine learning Electronic control Automotive electronics Control equipment
Urunga tuihono:	Citation/Abstract Full text outside of ProQuest
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

Whakaahuatanga
Whakarāpopotonga:	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 essential to maintain a high level of 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), integration of artificial intelligence (AI), and machine learning (ML) 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 ensure the safety and reliability of automotive software, as well as the role of AI in software reliability throughout the data lifecycle. Index Terms Safety Design, Automotive Software, Performance Evaluation, Advanced Driver Assistance Systems (ADAS) Applications, Automotive Software Systems, Electronic Control Units.
ISSN:	2331-8422
Puna:	Engineering Database