Security and Functional Safety for AI in Embedded Automotive System-A Tutorial

被引：2

作者：

Wang, Yi ^{[1
]}

Xiao, Jing ^{[2
]}

Wei, Zhengzhe ^{[3
]}

Zheng, Yuanjin ^{[3
]}

Tang, Kea-Tiong ^{[4
]}

Chang, Chip Hong ^{[3
]}

机构：

[1] Continental AG, Automot Technol Software & Cent Technol, D-30165 Hannover, Germany

[2] Continental AG, Continental Autonomous Mobil Germany GmbH, ENP Tech Off, D-30165 Hannover, Germany

[3] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore

[4] Natl Taiwan Univ, Dept Elect Engn, Taipei 10617, Taiwan

来源：

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS | 2024年 / 71卷 / 03期

关键词：

ML; functional safety; security; AI edge processor; adversarial attacks; SRAM MACRO; ATTACKS; DEFENSE; VISION;

D O I：

10.1109/TCSII.2023.3334273

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The tutorial explores key security and functional safety challenges for Artificial Intelligence (AI) in embedded automotive systems, including aspects from adversarial attacks, long life cycles of products, and limited energy resources of automotive platforms within safety-critical environments in diverse use cases. It provides a set of recommendations for how the security and safety engineering of machine learning can address these challenges. It also provides an overview of contemporary security and functional safety engineering practices, encompassing up-to-date legislative and technical prerequisites. Finally, we identify the role of AI edge processing in enhancing security and functional safety within embedded automotive systems.

引用

页码：1701 / 1707

页数：7

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