An Analysis of ISO 26262: Using Machine Learning Safely in Automotive Software

2017/09/07 by Rick Salay, Salay, Rick, Rodrigo Queiroz +3 · 2 citations
Computer Science · Engineering · #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Safety Systems Engineering in Autonomy #Software Engineering (cs.SE) #Software Testing and Debugging Techniques #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1709.02435

openalex publication_date 2017/09/07 · openalex created_date 2017/09/15 · openalex updated_date 2026/07/28

Abstract

Machine learning (ML) plays an ever-increasing role in advanced automotive functionality for driver assistance and autonomous operation; however, its adequacy from the perspective of safety certification remains controversial. In this paper, we analyze the impacts that the use of ML as an implementation approach has on ISO 26262 safety lifecycle and ask what could be done to address them. We then provide a set of recommendations on how to adapt the standard to accommodate ML.

Citations

• Debugging Machine Learning Tasks
• End to End Learning for Self-Driving Cars
• On the Sample Complexity of End-to-end Training vs. Semantic Abstraction\n Training
• Generating Visual Explanations

Cited by

• Assuring the Safety of Reinforcement Learning Components: AMLAS-RL
• Software Engineering for AI-Based Systems: A Survey

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