Ensuring AI Safety in Autonomous Vehicles: A Framework Based on ISO PAS 8800


Authors : Jherrod Thomas

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/3p24e93h

Scribd : https://tinyurl.com/fb5jcuef

DOI : https://doi.org/10.38124/ijisrt/25apr1584

PlumX Metrics

Semantic Scholar

ResearchGate

IJISRT Repository

Google Scholar

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 15 to 20 days to display the article.

Abstract : This study presents a structured exploration of ISO PAS 8800 as a dedicated safety framework addressing the unique challenges posed by artificial intelligence (AI) in autonomous vehicles (AVs). The research aims to establish the necessity of a distinct safety standard beyond conventional protocols, such as ISO 26262 and ISO 21448, which are insufficient to manage the probabilistic, adaptive, and opaque characteristics inherent in AI- driven systems. Employing a qualitative methodological approach grounded in standards analysis and case-based synthesis, the study evaluates the provisions of ISO PAS 8800 across multiple dimensions, risk governance, system transparency, continuous validation, and human oversight. Key findings demonstrate that ISO PAS 8800 fills critical gaps left by existing safety standards, offering AI-specific safety lifecycle processes, interpretability protocols, and robust risk management strategies. It intro- duces novel concepts such as Component Fault and Deficiency Trees (CFDTs), scenario-based validation, bounded incremental learning, and post-deployment monitoring, which are essential for certifying learning-enabled and continuously evolving AV systems. Furthermore, the framework emphasizes harmonization with cybersecurity standards (e.g., ISO/SAE 21434) to address adversarial vulnerabilities in AI pipelines. ISO PAS 8800 provides a comprehensive, adaptable, and forward-compatible framework for the governance of AI safety in autonomous driving. It facilitatesthe development of trustworthy, auditable, and socially accountable AV technologies, aligning technical innovation with emerging regulatory and ethical expectations.

Keywords : ISO PAS 8800, Autonomous Vehicles, AI Safety, Machine Learning, Risk Governance, Explainability, Functional Safety, ISO 26262, Cybersecurity, AV Certification, Over-The-Air (OTA), ISO/SAE 21434, ISO 21448.

References :

  1. R. Rathore, T. Nayeem, A. Agarwal, S. Kumar, and Paras, “AI System for Autonomous Vehicles,” International Journal For Multidisciplinary Research, vol. 6, no. 6, p. 28785, Nov. 2024. [Online]. Available: https://www.ijfmr.com/research-paper.php?id=28785
  2. Y. Alahmed, R. Abadla, and M. J. Al Ansari, “Enhancing Safety in Autonomous Vehicles through Advanced AI-Driven Perception and Decision-Making Systems,” in 2024 Fifth International Conference on Intelligent Data Science Technologies and Applications (IDSTA), Sep. 2024, pp. 208–217. [Online]. Available: https://ieeexplore.ieee.org/ abstract/document/10746990
  3. Y. Wang, “The Review of AI Efficiency in Autonomous Driving,” Applied and Computational Engineering, vol. 113, pp. 92–100, Dec. 2024. [Online]. Available: https://www.ewadirect.com/proceedings/ace/ article/view/18306
  4. M. Henne, A. Schwaiger, and G. Weiss, “Managing uncertainty of AI- based perception for autonomous systems.” in AISafety@ IJCAI, 2019, pp. 11–12.
  5. V. Bhardwaj, “AI-enabled autonomous driving: Enhancing safety and efficiency through predictive analytics,” International Journal of Scien- tific Research and Management (IJSRM), vol. 12, no. 02, pp. 1076–1094, 2024.
  6. S. Nageshrao, Y. Rahman, V. Ivanovic, M. Jankovic, E. Tseng, M. Hafner, and D. Filev, “Robust AI Driving Strategy for Autonomous Vehicles,” in AI-enabled Technologies for Autonomous and Connected Vehicles, Y. L. Murphey, I. Kolmanovsky, and P. Watta, Eds. Cham: Springer International Publishing, 2023, pp. 161–212. [Online]. Available: https://doi.org/10.1007/978-3-031-06780-8_7
  7. Sharique Masood Khan, “AI in Autonomous Vehicles,” International Journal of Advanced Research in Science, Communication and Technology, pp. 235–240, Jan. 2024. [Online]. Available: http://ijarsct.co.in/Paper15237.pdf
  8. Laugier, “Impact of AI on autonomous driving,” in WRC 2019-WRC 2019-IEEE world robot conference. IEEE, 2019, pp. 1–27.
  9. O. M. Kirovskii and V. A. Gorelov, “Driver assistance systems: analysis, tests and the safety case. ISO 26262 and ISO PAS 21448,” IOP Conference Series: Materials Science and Engineering, vol. 534, no. 1, p. 012019, May 2019, publisher: IOP Publishing. [Online]. Available: https://dx.doi.org/10.1088/1757-899X/534/1/012019
  10. K. Madala, C. Avalos-Gonzalez, and G. Krithivasan, “Workflow between ISO 26262 and ISO 21448 Standards for Autonomous Vehicles,” Journal of System Safety, vol. 57, no. 1, pp. 34–42, Oct. 2021, section: Articles. [Online]. Available: https://jsystemsafety.com/ index.php/jss/article/view/6
  11. K. Radlak, M. Szczepankiewicz, T. Jones, and P. Serwa, “Organization of machine learning based product development as per ISO 26262 and ISO/PAS 21448,” in 2020 IEEE 25th Pacific Rim International Symposium on Dependable Computing (PRDC), Dec. 2020, pp. 110–119, iSSN: 2473-3105. [Online]. Available: https: //ieeexplore.ieee.org/abstract/document/9320421
  12. Costantino, M. De Vincenzi, and I. Matteucci, “A Comparative Analysis of UNECE WP.29 R155 and ISO/SAE 21434,” in 2022 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), Jun. 2022, pp. 340–347, iSSN: 2768-0657. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9799351
  13. P. Iyenghar, E. Gracic, and G. Pawelke, “A Systematic Approach to Enhancing ISO 26262 With Machine Learning-Specific Life Cycle Phases and Testing Methods,” IEEE Access, vol. 12, pp. 179 600– 179 627, 2024, conference Name: IEEE Access. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10767223
  14. K. Saberi, J. Hegge, T. Fruehling, and J. F. Groote, “Beyond SOTIF: Black Swans and Formal Methods,” in 2020 IEEE International Systems Conference (SysCon), Aug. 2020, pp. 1–5, iSSN: 2472-9647. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9275888
  15. S. Abrecht, A. Hirsch, S. Raafatnia, and M. Woehrle, “Deep Learning Safety Concerns in Automated Driving Perception,” IEEE Transactions on Intelligent Vehicles, pp. 1–12, 2024, conference Name: IEEE Transactions on Intelligent Vehicles. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10599385
  16. M. Zeller, “Safety assurance of autonomous systems using machine learning: An industrial case study and lessons learnt,” INCOSE International Symposium, vol. 33, no. 1, pp. 320–333, 2023, tex.eprint: https://incose.onlinelibrary.wiley.com/doi/pdf/10.1002/iis2.13024. [On- line]. Available: https://incose.onlinelibrary.wiley.com/doi/abs/10.1002/ iis2.13024
  17. Schwalb, “Analysis of safety of the intended use (sotif),” National Highway Traffic Safety Administration, 2019. 1301  2022 , Tech. Rep., 2019.
  18. L. Fridman, B. Jenik, and B. Reimer, “Arguing machines: Percep- tioncontrol system redundancy and edge case discovery in real-world autonomous driving,” arXiv preprint arXiv:1710.04459, 2017.
  19. M. Pitale, A. Abbaspour, and D. Upadhyay, “Inherent Diverse Redundant Safety Mechanisms for AI-based Software Elements in Automotive Applications,” Feb. 2024, arXiv:2402.08208 [cs]. [Online]. Available: http://arxiv.org/abs/2402.08208
  20. N. Moradloo, I. Mahdinia, and A. J. Khattak, “Safety in higher level automated vehicles: Investigating edge cases in crashes of vehicles equipped with automated driving systems,” Accident Analysis & Prevention, vol. 203, p. 107607, Aug. 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0001457524001520
  21. J. Jaspar, E. Viennet, D. Gualandris, J.-L. Sauvaget, and F. Fogelman- Soulié, “Using Synthetic Images to Improve and Test Object Detection in the Context of the Autonomous Vehicle,” in 2024 12th European Workshop on Visual Information Processing (EUVIP), Sep. 2024, pp. 1–6, iSSN: 2471-8963. [Online]. Available: https: //ieeexplore.ieee.org/abstract/document/10772859
  22. Chen, Z. Zhang, Y. Liu, and X. T. Yang, “INSIGHT: Enhancing Autonomous Driving Safety through Vision-Language Models  on  Context-Aware  Hazard  Detection  and  Edge Case Evaluation,” Jan. 2025, publication Title: arXiv e- prints ADS Bibcode: 2025arXiv250200262C. [Online]. Available: https://ui.adsabs.harvard.edu/abs/2025arXiv250200262C.
  23. Karunakaran, S. Worrall, and E. Nebot, “Efficient statistical validation with edge cases to evaluate Highly Automated Vehicles,” in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), Sep. 2020, pp. 1–8. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9294590
  24. O. Chae, J. Kim, J. Jang, H. Yun, and S. Lee, “Development of risk-situation scenario for autonomous vehicles on expressway using topic modeling,” Journal of Advanced Transportation, vol. 2022, no. 1, p. 6880310, 2022, tex.eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1155/2022/6880310. [On- line]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1155/2022/ 6880310
  25. S. Majumdar and S. E. Kirkley, “A Strategic Framework for Reducing Decision Bias in Driverless Car Object Detection,” in 2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), Jul. 2024, pp. 217–223, iSSN: 2834-8249. [Online]. Available: https://ieeexplore.ieee.org/abstract/ document/10617839
  26. Katare, N. Kourtellis, S. Park, D. Perino, M. Janssen, and A. Y. Ding, “Bias Detection and Generalization in AI Algorithms on Edge for Autonomous Driving,” in 2022 IEEE/ACM 7th Symposium on Edge Computing (SEC), Dec. 2022, pp. 342–348. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9996662
  27. N. Jaipuria, K. Stevo, X. Zhang, M. L. Gaopande, I. C. Garcia, J. Jain, and V. N. Murali, “deepPIC: Deep Perceptual Image Clustering For Identifying Bias In Vision Datasets,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Jun. 2022, pp. 4792–4801, iSSN: 2160-7516. [Online]. Available: https://ieeexplore.ieee.org/document/9857264
  28. S. Jamthe, Y. Viswanath, and S. Lokiah, “Inclusive ethical AI in human–computer interaction in autonomous vehicles,” Journal of AI, Robotics & Workplace Automation, vol. 1, no. 3, pp. 294–307, Jan. 2022.
  29. Ntoutsi, “Bias in AI-systems: A multi-step approach,” in 2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence, J. M. Alonso and A. Catala, Eds. Dublin, Ireland: Association for Computational Linguistics, Nov. 2020, pp. 3–4. [Online]. Available: https://aclanthology.org/2020.nl4xai-1.2/
  30. M. Kattnig, A. Angerschmid, T. Reichel, and R. Kern, “Assessing trustworthy AI: Technical and legal perspectives of fairness in AI,” Computer Law & Security Review, vol. 55, p. 106053, Nov. 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/ S0267364924001195
  31. Collecchia, “[Let’s open the black box: eXplainable Artificial Intelligence (XAI).” Recenti progressi in medicina, vol. 112, no. 11, pp. 709–710, Nov. 2021. [Online]. Available: https://doi.org/10.1701/ 3696.36848
  32. Omeiza, H. Webb, M. Jirotka, and L. Kunze, “Explanations in Autonomous Driving: A Survey,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 10 142–10 162,  Aug.  2022,  conference  Name:  IEEE Transactions on Intelligent Transportation Systems. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9616449
  33. V. Chamola, V. Hassija, A. R. Sulthana, D. Ghosh, D. Dhingra, and B. Sikdar, “A Review of Trustworthy and Explainable Artificial Intelligence (XAI),” IEEE Access, vol. 11, pp. 78 994–79 015, 2023, conference Name: IEEE Access. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10188681
  34. S. Nazat, O. Arreche, and M. Abdallah, “On Evaluating Black- Box Explainable AI Methods for Enhancing Anomaly Detection in Autonomous Driving Systems,” Sensors, vol. 24, no. 11, p. 3515, Jan. 2024, number: 11 Publisher: Multidisciplinary Digital Publishing Institute. [Online]. Available: https://www.mdpi.com/1424-8220/24/11/ 3515
  35. R. K. Thaker, “Explainable ai in autonomous systems: Understanding the reasoning behind decisions for safety and trust,” International Journal For Multidisciplinary Research, 2022. [Online]. Available: https://api.semanticscholar.org/CorpusID:273723424
  36. V. Khandelwal, “Building trustworthy AI systems: Developing explainable models for transparent decision-making in autonomous vehicles,” Journal of Sustainable Solutions, 2024. [Online]. Available: https://api.semanticscholar.org/CorpusID:273334814
  37. T. Cai, Y. Liu, Z. Zhou, H. Ma, S. Z. Zhao, Z. Wu, and J. Ma, “Driving with Regulation: Interpretable Decision-Making for Autonomous Vehicles with Retrieval-Augmented Reasoning via LLM,” Mar. 2025, arXiv:2410.04759 [cs]. [Online]. Available: http://arxiv.org/abs/2410.04759
  38. M. Cunneen, M. , Martin, , and F. Murphy, “Autonomous Vehicles and Embedded Artificial Intelligence: The Challenges of Framing Machine Driving Decisions,” Applied Artificial Intelligence, vol. 33, no. 8, pp. 706–731, Jul. 2019, publisher: Taylor & Francis. [Online]. Available: https://www.tandfonline.com/doi/full/10.1080/08839514.2019.1600301
  39. A. Tahir, W. Alayed, W. U. Hassan, and A. Haider, “A Novel Hybrid XAI Solution for Autonomous Vehicles: Real- Time Interpretability Through LIME–SHAP Integration,” Sensors, vol. 24, no. 21, p. 6776, Jan. 2024, number: 21 Publisher: Multidisciplinary Digital Publishing Institute. [Online]. Available: https://www.mdpi.com/1424-8220/24/21/6776
  40. J. Jagannathan, K. Dr.AgrawalRajesh, D. N. Labhade-Kumar, R. Rastogi, M. V. Unni, and K. K. Baseer, “Developing interpretable models and techniques for explainable AI in decision-making,” The Scientific Temper, 2023. [Online]. Available: https://api.semanticscholar.org/ CorpusID:267315550
  41. Garikapati and S. S. Shetiya, “Autonomous Vehicles: Evolution of Artificial Intelligence and Learning Algorithms,” Feb. 2024, arXiv:2402.17690 [cs]. [Online]. Available: http://arxiv.org/abs/2402.17690
  42. B. Liu, S. Mazumder, E. Robertson, and S. Grigsby, “AI autonomy: Self-initiation, adaptation and continual learning,” ArXiv, vol. abs/2203.08994, 2022. [Online]. Available: https://api.semanticscholar.org/CorpusID:247519121
  43. Rudolph, S. Voget, and J. Mottok, “A consistent safety case argumentation for artificial intelligence in safety related automotive systems,” in 9th European Congress on Embedded Real Time Software and Systems (ERTS 2018), ser. 9th European Congress on Embedded Real Time Software and Systems (ERTS 2018), Toulouse, France, Jan. 2018. [Online]. Available: https://hal.science/hal-02156048
  44. S. Khokha, “From Standards to Implementation: Functional Safety and Cybersecurity in Modern Autonomous and Electric Vehicles,” in 2024 International Conference on Cybernation and Computation (CYBERCOM), Nov. 2024, pp. 52–56. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10803155
  45. Siddiqui, R. Khan, S. Y. Tasdemir, H. Hui, B. Sonigara, S. Sezer, and K. McLaughlin, “Cybersecurity Engineering: Bridging the Security Gaps in Advanced Automotive Systems and ISO/SAE 21434,” in 2023 IEEE 97th Vehicular Technology Conference (VTC2023- Spring), Jun. 2023, pp. 1–6, iSSN: 2577-2465. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10200490
  46. Fischer, J.-P. Tolvanen, and R. T. Kolagari, “Automotive Cybersecurity Engineering with Modeling Support,” in 2024 19th Conference on Computer Science and Intelligence Systems (FedCSIS), Sep. 2024, pp. 319–329. [Online]. Available: https://ieeexplore.ieee.org/ abstract/document/10736032
  47. Costantino, M. De Vincenzi, and I. Matteucci, “In-Depth Exploration of ISO/SAE 21434 and Its Correlations with Existing Standards,” IEEE Communications Standards Magazine, vol. 6, no. 1, pp. 84–92, Mar. 2022, conference Name: IEEE Communications Standards Magazine. [Online]. Available: https://ieeexplore.ieee.org/ abstract/document/9762839
  48. B. Boi, T. Gupta, M. Rinhel, I. Jubea, R. Khondoker, C. Esposito, and B. M. Sousa, “Strengthening Automotive Cybersecurity: A Comparative Analysis of ISO/SAE 21434-Compliant Automatic Collision Notification (ACN) Systems,” Vehicles, vol. 5, no. 4, pp. 1760–1802, Dec. 2023, number: 4 Publisher: Multidisciplinary Digital Publishing Institute. [Online]. Available: https://www.mdpi.com/2624- 8921/5/4/96
  49. T. Myklebust, T. Stålhane, and G. D. Jenssen, “Autonomous Vehicles
  50. B. Bhavani, S. S., T. V., and S. S., “Defense against adversarial ai,” Journal of Cognitive Human-Computer Interaction, 2024. [Online]. Available: https://api.semanticscholar.org/CorpusID:269004189.
  51. Chahe, C. Wang, A. Jeyapratap, K. Xu, and L. Zhou, “Dynamic Adversarial Attacks on Autonomous Driving Systems,” Dec. 2024, arXiv:2312.06701 [cs]. [Online]. Available: http://arxiv.org/abs/ 2312.06701
  52. Z. Xiong, H. Xu, W. Li, and Z. Cai, “Multi-Source Adversarial Sample Attack on Autonomous Vehicles,” IEEE Transactions on Vehicular Technology, vol. 70, no. 3, pp. 2822–2835, Mar. 2021, conference Name: IEEE Transactions on Vehicular Technology. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9360457
  53. Q. Sun, A. A. Rao, X. Yao, B. Yu, and S. Hu, “Counteracting adversarial attacks in autonomous driving,” in Proceedings of the 39th International Conference on Computer-Aided Design, ser. ICCAD ’20. New York, NY, USA: Association for Computing Machinery, Dec. 2020, pp. 1–7. [Online]. Available: https://doi.org/10.1145/3400302.3415758
  54. Gan and C. Liu, “An autoencoder based approach to defend against adversarial attacks for autonomous vehicles,” 2020 International Conference on Connected and Autonomous Driving (MetroCAD), pp. 43–44, 2020. [Online]. Available: https://api.semanticscholar.org/ CorpusID:220607428
  55. M. Girdhar, J. Hong, and J. Moore, “Cybersecurity of Autonomous Vehicles: A Systematic Literature Review of Adversarial Attacks and Defense Models,” IEEE Open Journal of Vehicular Technology, vol. 4, pp. 417–437, 2023, conference Name: IEEE Open Journal of Vehicular Technology. [Online]. Available: https://ieeexplore.ieee.org/ abstract/document/10097455
  56. T. Ali, A. Eleyan, and T. Bejaoui, “Detecting Conventional and Adversarial Attacks Using Deep Learning Techniques: A Systematic Review,” in 2023 International Symposium on Networks, Computers and Communications (ISNCC), Oct. 2023, pp. 1–7, iSSN: 2768- 0940. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/ 10323872
  57. N. Lekota, “Governance considerations of adversarial attacks on AI systems,” in International Conference on AI Research, 2024. [Online]. Available: https://api.semanticscholar.org/CorpusID:274538830
  58. R. Patel and P. Liggesmeyer, “Machine Learning Based Dynamic Risk Assessment for Autonomous Vehicles,” in 2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC), Nov. 2021, pp. 73–77. [Online]. Available: https://ieeexplore.ieee.org/ abstract/document/9644270
  59. S. Ghosh, A. Zaboli, J. Hong, and J. Kwon, “Object-focused Risk Evaluation of AI-driven Perception Systems in Autonomous Vehicles,” in 2024 IEEE Transportation Electrification Conference and Expo (ITEC), Jun. 2024, pp. 1–5, iSSN: 2473-7631. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10599086
  60. S. Fleck, B. May, G. Daniel, C. Davies, B. May, G. Daniel, and C. Davies, “Data driven degradation of automotive sensors and effect analysis,” Electronic Imaging, vol. 33, pp. 1–8, Jan. 2021, publisher: Society for Imaging Science and Technology. [Online]. Available: https://library.imaging.org/ei/articles/33/17/art00010
  61. Pourdanesh, T. Q. Dinh, F. Tagliabo, and P. Whiffin, “Failure Safety Analysis of Artificial Intelligence Systems for Smart/Autonomous Vehicles,” in 2021 24th International Conference on Mechatronics Technology (ICMT), Dec. 2021, pp. 1–6. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9687283
  62. Campean, U. Yildirim, A. Korsunovs, and A. Doikin, “Extending the function failure modes taxonomy for intelligent systems with embedded AI components,” Proceedings of the Design Society, vol. 4, pp. 1949– 1958, May 2024. [Online]. Available: https://www.cambridge.org/ core/journals/proceedings-of-the-design-society/article/extending-the-function-failure-modes-taxonomy-for-intelligent-systems-with- embedded-ai-components/94335963BF0A6F0128774CC477584B80
  63. T. Ishigooka, S. Otsuka, K. Serizawa, R. Tsuchiya, and F. Narisawa, “Graceful Degradation Design Process for Autonomous Driving Sys- tem,” in Computer Safety, Reliability, and Security, A. Romanovsky, Troubitsyna, and F. Bitsch, Eds. Cham: Springer International Publishing, 2019, pp. 19–34.
  64. Hsiang, K.-C. Chen, and Y.-Y. Chen, “Development of Simulation- Based Testing Scenario Generator for Robustness Verification of Autonomous Vehicles,” in 2022 5th International Conference on Advanced Systems and Emergent Technologies (IC_ASET), Mar. 2022, pp. 210–215. [Online]. Available: https://ieeexplore.ieee.org/abstract/ document/9765910
  65. M. Kim and W. Saad, “Analysis of the Memorization and Generalization Capabilities of AI Agents: are Continual Learners Robust?” in ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr. 2024, pp. 6840–6844, iSSN: 2379-190X. [Online]. Available: https://ieeexplore.ieee.org/abstract/ document/10447575
  66. M. Keser, Y. Shoeb, and A. Knoll, “How Could Generative AI Support Compliance with the EU AI Act? A Review for Safe Automated Driving Perception,” Aug. 2024, arXiv:2408.17222 [cs]. [Online]. Available: http://arxiv.org/abs/2408.17222
  67. T.  Tiedemann,  L.  Schwalb,  M.  Kasten,  R.  Grotkasten,  and S. Pareigis, “Miniature Autonomy as Means to Find New Approaches in Reliable Autonomous Driving AI Method Design,” Frontiers in Neurorobotics, vol. 16, Jul. 2022, publisher: Frontiers. [Online]. Available: https://www.frontiersin.org/journals/neurorobotics/ articles/10.3389/fnbot.2022.846355/full
  68. C. Brogle, C. Zhang, K. L. Lim, and T. Bräunl, “Hardware- in-the-Loop Autonomous Driving Simulation Without Real-Time Constraints,” IEEE Transactions on Intelligent Vehicles, vol. 4, no. 3, pp. 375–384, Sep. 2019, conference Name: IEEE Transactions on Intelligent Vehicles. [Online]. Available: https://ieeexplore.ieee.org/ abstract/document/8723564
  69. P. Trentsios, M. Wolf, and D. Gerhard, “Overcoming the Sim-to-Real Gap in Autonomous Robots,” Procedia CIRP, vol. 109, pp. 287–292, Jan. 2022. [Online]. Available: https://www.sciencedirect.com/science/ article/pii/S2212827122007004
  70. C. Johnson, E. Graupe, and M. Kassel, “A Literature Review of Simulation Fidelity for Autonomous-Vehicle Research and Development,” SAE International Journal of Aerospace, vol. 16, no.  3,  pp.  253–261,  May  2023,  number:  01-16-03-0021. [Online]. Available: https://www.sae.org/publications/technical-papers/ content/01-16-03-0021/
  71. T. Mihalj, D. Nalic, S. Arefnezhad, and A. Eichberger, “Hazards Identification Using Scenario-Based Testing with Respect to ISO PAS 21448 and ISO 26262,” in 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Sep. 2023, pp. 5764–5770, iSSN: 2153-0017. [Online]. Available: https: //ieeexplore.ieee.org/abstract/document/10421992
  72. B. P. Singh and A. Joshi, “Ethical Considerations in AI Development,” in The Ethical Frontier of AI and Data Analysis. IGI Global Scientific Publishing, 2024, pp. 156–179. [Online]. Available: https://www.igi- global.com/chapter/ethical-considerations-in-ai-development/www.igi- global.com/chapter/ethical-considerations-in-ai-development/341192
  73. P. Iyenghar, “Clever Hans in the Loop? A Critical Examination of ChatGPT in a Human-in-the-Loop Framework for Machinery Functional Safety Risk Analysis,” Eng, vol. 6, no. 2, p. 31, Feb. 2025, number: 2 Publisher: Multidisciplinary Digital Publishing Institute. [Online]. Available: https://www.mdpi.com/2673-4117/6/2/31
  74. X. Chen, X. Wang, and Y. Qu, “Constructing Ethical AI Based on the “Human-in-the-Loop” System,” Systems, vol. 11, no. 11, p. 548, Nov. 2023, number: 11 Publisher: Multidisciplinary Digital Publishing Institute. [Online]. Available: https://www.mdpi.com/2079-8954/11/11/ 548
  75. Z. Huang, Z. Sheng, C. Ma, and S. Chen, “Human as AI mentor: Enhanced human-in-the-loop reinforcement learning for safe and efficient autonomous driving,” Communications in Transportation Research, vol. 4, p. 100127, Dec. 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2772424724000106
  76. Wu, Z. Huang, C. Huang, Z. Hu, P. Hang, Y. Xing, and C. Lv, “Human-in-the-Loop Deep Reinforcement Learning with Application to Autonomous Driving,” Apr. 2021, arXiv:2104.07246 [cs]. [Online]. Available: http://arxiv.org/abs/2104.07246
  77. B. Shanker and Neyigapula, “Ethical considerations in AI development: Balancing autonomy and accountability,” Journal of Advances in Artificial Intelligence, 2024. [Online]. Available: https://api.semanticscholar.org/CorpusID:270769733
  78. Chukwunweike, O. A. Lawal, J. B. Arogundade, and B. A. e, “Navigating ethical challenges of explainable ai in autonomous systems,” International Journal of Science and Research Archive, 2024. [Online]. Available: https://api.semanticscholar.org/CorpusID:273141471
  79. B. S. Miguel, A. Naseer, and H. Inakoshi, “Putting accountability of AI systems into practice,” in International joint conference on artificial intelligence, 2020. [Online]. Available: https://api.semanticscholar.org/ CorpusID:220484673
  80. S. Cameron and B. Hamidzadeh, “Preserving paradata for accountability of semi-autonomous AI agents in dynamic environments: An archival perspective,” Telematics and Informatics Reports, vol. 14, p. 100135, Jun. 2024. [Online]. Available: https://www.sciencedirect.com/science/ article/pii/S2772503024000215.
  81. X. Xie, J. Song, Z. Zhou, F. Zhang, and L. Ma, “Mosaic: Model- based Safety Analysis Framework for AI-enabled Cyber-Physical Systems,” May 2023, arXiv:2305.03882 [cs]. [Online]. Available: http://arxiv.org/abs/2305.03882
  82. Nouri, C. Berger, and F. Törner, “An Industrial Experience Report about Challenges from Continuous Monitoring, Improvement, and Deployment for Autonomous Driving Features,” in 2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Aug. 2022, pp. 358–365. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10011483
  83. M. Adnan, Y. Ioannou, C.-Y. Tsai, A. Galloway, H. R. Tizhoosh, and G. W. Taylor, “Monitoring Shortcut Learning using Mutual Information,” Jun. 2022, arXiv:2206.13034 [cs]. [Online]. Available: http://arxiv.org/abs/2206.13034
  84. N. Hochgeschwender, “Adaptive Deployment of Safety Monitors for Autonomous Systems,” in Computer Safety, Reliability, and Security, Romanovsky, E. Troubitsyna, I. Gashi, E. Schoitsch, and F. Bitsch, Eds. Cham: Springer International Publishing, 2019, pp. 346–357.
  85. S. Verma, P. Pali, M. Dhanwani, and S. Jagwani, “Ethical AI: Developing frameworks for responsible deployment in autonomous systems,” International Journal of Multidisciplinary Research in Science, Engineering and Technology, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:272001084
  86. V. Iyieke, H. Jadidbonab, A. Rakib, J. Bryans, D. Dhaliwal, and O. Kosmas, “An adaptable security-by-design approach for ensuring a secure Over the Air (OTA) update in modern vehicles,” Computers & Security, vol. 150, p. 104268, Mar. 2025. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0167404824005741
  87. Y.-H. Chou and W.-W. Li, “Enhancing OTA Update Security in Zonal Architecture for Automobiles,” in 2023 IEEE 12th Global Conference on Consumer Electronics (GCCE), Oct. 2023, pp. 761– 762, iSSN: 2693-0854. [Online]. Available: https://ieeexplore.ieee.org/ abstract/document/10315400
  88. Subas¸i and M. Mercımek, “Attack Path Analysis and Security Concept Design for OTA Enabled Electric Power Steering System,” in 2024 Innovations in Intelligent Systems and Applications Conference (ASYU), Oct. 2024, pp. 1–7, iSSN: 2770-7946. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10756981
  89. Mathew, “Secure over-the-air (OTA) update mechanisms for ADAS,” International Research Journal of Innovations in Engineering and Technology, 2024. [Online]. Available: https://api.semanticscholar.org/ CorpusID:269404859
  90. S. S. Raghavan, “Blockchain-based framework for secure OTA updates in autonomous vehicles,” International Journal of Scientific Research and Engineering Trends, 2022. [Online]. Available: https://api.semanticscholar.org/CorpusID:275814369
  91. N. M. Istiak Chowdhury and R. Hasan, “How Trustworthy are Over-The-Air (OTA) Updates for Autonomous Vehicles (AV) to Ensure Public Safety?: A Threat Model-based Security Analysis,” in 2024 IEEE World Forum on Public Safety Technology (WFPST), May 2024, pp. 87–92. [Online]. Available: https://ieeexplore.ieee.org/ abstract/document/10607113
  92. S. Yeasmin and A. Haque, “Collaborative DDoS Attack Defense for OTA Updates in CAVs using Hyperledger Fabric Blockchain,” in 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), Jul. 2023, pp. 1–6. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10215881.
  93. Jedliková, “Ethical considerations in Risk management of autonomous and intelligent systems,” Ethics & Bioethics, vol. 14, pp. 80 – 95, 2024. [Online]. Available: https://api.semanticscholar.org/ CorpusID:270331630
  94. R. Maier and J. Mottok, “Causality and Functional Safety - How Causal Models Relate to the Automotive Standards ISO 26262, ISO/PAS 21448, and UL 4600,” in 2022 International Conference on Applied Electronics (AE), Sep. 2022, pp. 1–6, iSSN: 1805-9597. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9920053
  95. Dong, S. Chen, M. Miralinaghi, T. Chen, P. Li, and S. Labi, “Why did the AI make that decision? Towards an explainable artificial intelligence (XAI) for autonomous driving systems,” Transportation Research Part C: Emerging Technologies, vol. 156, p. 104358, Nov. 2023. [Online]. Available: https://www.sciencedirect.com/science/ article/pii/S0968090X23003480
  96. B. Gyevnar, S. Droop, T. Quillien, S. B. Cohen, N. R. Bramley, C. G. Lucas, and S. V. Albrecht, “People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior: Insights from Cognitive Science for Explainable AI,” Feb. 2025, arXiv:2403.08828 [cs]. [Online]. Available: http://arxiv.org/abs/2403.08828
  97. Economic Times, “Bill Gates makes alarming prediction: AI will replace teachers and doctors within 10 years, warns humans may become obsolete for most tasks,” 2025, publisher: The Economic Times. [Online]. Available: https://economictimes.indiatimes.com/news/ international/us/bill-gates-makes-alarming-prediction-ai-will-replace-teachers-and-doctors-within-10-years-warns-humans-may-become-obsolete-for-most-tasks/articleshow/119654157.cms?from=mdr
  98. Brown, “Bill Gates says AI will replace doctors, teachers within 10 years — and claims humans won’t be needed ‘for most things’,” 2025, publisher: New York Post. [Online]. Avail- able: https://nypost.com/2025/03/27/business/bill-gates-said-ai-will-replace-doctors-teachers-within-10-years/?utm_source=chatgpt.com

This study presents a structured exploration of ISO PAS 8800 as a dedicated safety framework addressing the unique challenges posed by artificial intelligence (AI) in autonomous vehicles (AVs). The research aims to establish the necessity of a distinct safety standard beyond conventional protocols, such as ISO 26262 and ISO 21448, which are insufficient to manage the probabilistic, adaptive, and opaque characteristics inherent in AI- driven systems. Employing a qualitative methodological approach grounded in standards analysis and case-based synthesis, the study evaluates the provisions of ISO PAS 8800 across multiple dimensions, risk governance, system transparency, continuous validation, and human oversight. Key findings demonstrate that ISO PAS 8800 fills critical gaps left by existing safety standards, offering AI-specific safety lifecycle processes, interpretability protocols, and robust risk management strategies. It intro- duces novel concepts such as Component Fault and Deficiency Trees (CFDTs), scenario-based validation, bounded incremental learning, and post-deployment monitoring, which are essential for certifying learning-enabled and continuously evolving AV systems. Furthermore, the framework emphasizes harmonization with cybersecurity standards (e.g., ISO/SAE 21434) to address adversarial vulnerabilities in AI pipelines. ISO PAS 8800 provides a comprehensive, adaptable, and forward-compatible framework for the governance of AI safety in autonomous driving. It facilitatesthe development of trustworthy, auditable, and socially accountable AV technologies, aligning technical innovation with emerging regulatory and ethical expectations.

Keywords : ISO PAS 8800, Autonomous Vehicles, AI Safety, Machine Learning, Risk Governance, Explainability, Functional Safety, ISO 26262, Cybersecurity, AV Certification, Over-The-Air (OTA), ISO/SAE 21434, ISO 21448.

Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe