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The Changing Anything Changes Everything (CACE)' Principle: Underestimated Challenges in Applying AI/ML to Automotive Safety-Critical Systems General Motors Research and Development
- Format:
- Book
- Conference/Event
- Author/Creator:
- Tong, Wei, author.
- Conference Name:
- WCX SAE World Congress Experience (2025-04-08 : Detroit, Michigan, United States)
- Language:
- English
- Physical Description:
- 1 online resource cm
- Place of Publication:
- Warrendale, PA SAE International 2025
- Summary:
- The integration of artificial intelligence (AI) and machine learning (ML) into automotive safety-critical systems presents unique challenges, particularly the "changing anything changes everything" (CACE) property inherent in many AI/ML models. CACE highlights the high degree of interdependence within AI/ML systems, where even minor adjustments can have significant, unforeseen impacts on system behavior, posing risks in safety-critical applications. This paper examines the intricate nature of the CACE principle and its implications for the development cycle of AI/ML-based applications. Through case studies and theoretical analysis, we highlight CACE-related challenges and discuss strategies to mitigate these risks in safety-critical environments. Our analysis aims to raise awareness of this often-overlooked challenge, offering insights for safer, more robust AI/ML deployment in the automotive industry
- Notes:
- Vendor supplied data
- Publisher Number:
- 2025-01-8110
- Access Restriction:
- Restricted for use by site license
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