mlsanityData to 7 Oct 2026

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

arXiv 1709.02435 ↗First posted 7 Sep 2017cs.AI cs.LG cs.SEcited by 26

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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.

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

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Citing papers per year: 2021: 5; 2022: 4; 2023: 8; 2024: 5; 2025: 2; 2026: 2.

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  1. 2021Software Engineering for AI-Based Systems: A Surveycited by 68
  2. 2021Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Processcited by 56
  3. 2023A DPLL(T) Framework for Verifying Deep Neural Networkscited by 23
  4. 2021Taxonomy of Machine Learning Safety: A Survey and Primercited by 22
  5. 2023A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitionerscited by 22
  6. 2021A Review of Testing Object-Based Environment Perception for Safe Automated Drivingcited by 9
  7. 2021Performance Analysis of Out-of-Distribution Detection on Various Trained Neural Networkscited by 9
  8. 2022Performance Analysis of Out-of-Distribution Detection on Trained Neural Networkscited by 7
  9. 2024Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Drivingcited by 7
  10. 2022Monitoring of Perception Systems: Deterministic, Probabilistic, and Learning-based Fault Detection and Identificationcited by 6
  11. 2023Model Reporting for Certifiable AI: A Proposal from Merging EU Regulation into AI Developmentcited by 6
  12. 2024Evaluation of Out-of-Distribution Detection Performance on Autonomous Driving Datasetscited by 5
14 more
  1. 2024Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairnesscited by 3
  2. 2022A causal model of safety assurance for machine learningcited by 2
  3. 2022Understanding the Impact of Edge Cases from Occluded Pedestrians for ML Systemscited by 1
  4. 2023An investigation of licensing of datasets for machine learning based on the GQM modelcited by 1
  5. 2023Trustworthy, responsible, ethical AI in manufacturing and supply chains: synthesis and emerging research questionscited by 1
  6. 2023Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learningcited by 1
  7. 2023Co-Design of Out-of-Distribution Detectors for Autonomous Emergency Braking Systemscited by 1
  8. 2024CyberCortex.AI: An AI-based Operating System for Autonomous Robotics and Complex Automationcited by 1
  9. 2025Periodic Online Testing for Sparse Systolic Tensor Arrayscited by 1
  10. 2025Assuring the Safety of Reinforcement Learning Components: AMLAS-RLcited by 1
  11. 2026RISC-V Functional Safety for Autonomous Automotive Systems: An Analytical Framework and Research Roadmap for ML-Assisted Certificationcited by 1
  12. 2023AGNES: Abstraction-guided Framework for Deep Neural Networks Securitycited by 0
  13. 2024Towards Robust Training Datasets for Machine Learning with Ontologies: A Case Study for Emergency Road Vehicle Detectioncited by 0
  14. 2026Testing Between the Test Cases: Proving End-to-End Steering in Conditions You Never Drovecited by 0