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

arXiv: https://arxiv.org/abs/1709.02435 · HTML: https://mlsanity.com/paper/1709.02435/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2017.

## Built on by (most cited first, 26 of 26)

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- [Performance Analysis of Out-of-Distribution Detection on Trained Neural Networks](https://mlsanity.com/paper/2204.12378/) (2022)
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- [Model Reporting for Certifiable AI: A Proposal from Merging EU Regulation into AI Development](https://mlsanity.com/paper/2307.11525/) (2023)
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- [CyberCortex.AI: An AI-based Operating System for Autonomous Robotics and Complex Automation](https://mlsanity.com/paper/2409.01241/) (2024)
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- [Assuring the Safety of Reinforcement Learning Components: AMLAS-RL](https://mlsanity.com/paper/2507.08848/) (2025)
- [RISC-V Functional Safety for Autonomous Automotive Systems: An Analytical Framework and Research Roadmap for ML-Assisted Certification](https://mlsanity.com/paper/2604.17391/) (2026)
- [Testing Between the Test Cases: Proving End-to-End Steering in Conditions You Never Drove](https://mlsanity.com/paper/2609.10951/) (2026)
- [AGNES: Abstraction-guided Framework for Deep Neural Networks Security](https://mlsanity.com/paper/2311.04009/) (2023)
- [Towards Robust Training Datasets for Machine Learning with Ontologies: A Case Study for Emergency Road Vehicle Detection](https://mlsanity.com/paper/2406.15268/) (2024)

## Abstract (arXiv)

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.