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

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.

6 pages, 3 figures

Cited by in corpus (18)

• A Survey of Deep Learning Techniques for Autonomous Driving
• Algorithmic decision-making in AVs: Understanding ethical and technical concerns for smart cities
• FT-CNN: Algorithm-Based Fault Tolerance for Convolutional Neural Networks
• A Review of Testing Object-Based Environment Perception for Safe Automated Driving
• Software Engineering for AI-Based Systems: A Survey
• Practical Solutions for Machine Learning Safety in Autonomous Vehicles
• Towards Safe Machine Learning for CPS: Infer Uncertainty from Training Data
• Cloud2Edge Elastic AI Framework for Prototyping and Deployment of AI Inference Engines in Autonomous Vehicles
• On Using Blockchains for Safety-Critical Systems
• Traceability of Deep Neural Networks
• Deep Learning in the Automotive Industry: Recent Advances and Application Examples
• Engineering problems in machine learning systems
• Foreseeing the Impact of the Proposed AI Act on the Sustainability and Safety of Critical Infrastructures
• Safety design concepts for statistical machine learning components toward accordance with functional safety standards
• DeepFault: Fault Localization for Deep Neural Networks
• A Structured Approach to Trustworthy Autonomous/Cognitive Systems
• Towards Structured Evaluation of Deep Neural Network Supervisors
• Performance Analysis of Out-of-Distribution Detection on Various Trained Neural Networks