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Reliability assessment of autonomous vehicles based on the safety control structure

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  • Feipeng Wang
  • Diana Filipa Araújo
  • Yan-Fu Li

Abstract

The recent social trends and accelerated technological progress culminated in the development of autonomous vehicles (AVs). Reliability assessment for AV systems is in high demand before its market launch. In safety-critical systems (SCSs) such as AV systems, the reliability concept should be broadened to consider more safety-related issues. In this paper, reliability is defined as the probability that the system performs satisfactorily for a given period of time under stated conditions. This paper proposes a reliability assessment framework of AV, consisting of three main stages: (i) modeling the safety control structure through the Systems-Theoretic Accident Model and Processes (STAMP); (ii) mapping the control structure and functional relationships to a directed acyclic graph (DAG); and (iii) construct a Bayesian network (BN) on DAG to assess the system reliability. The fully automated (level 5) vehicle system is shown as a numeric example to illustrate how this suggested framework works. A brief discussion on involving human factors in systems to analyze lower levels of automated vehicles is also included, demonstrating the need for further research on real case studies.

Suggested Citation

  • Feipeng Wang & Diana Filipa Araújo & Yan-Fu Li, 2023. "Reliability assessment of autonomous vehicles based on the safety control structure," Journal of Risk and Reliability, , vol. 237(2), pages 389-404, April.
    • Handle: RePEc:sae:risrel:v:237:y:2023:i:2:p:389-404
    DOI: 10.1177/1748006X211069705
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    1. Sarvesh Kolekar & Joost Winter & David Abbink, 2020. "Human-like driving behaviour emerges from a risk-based driver model," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    2. Hazel Si Min Lim & Araz Taeihagh, 2019. "Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities," Sustainability, MDPI, vol. 11(20), pages 1-28, October.
    3. Langseth, Helge & Portinale, Luigi, 2007. "Bayesian networks in reliability," Reliability Engineering and System Safety, Elsevier, vol. 92(1), pages 92-108.
    4. Lujia Wang & Qingpei Hu & Jian Liu, 2016. "Software reliability growth modeling and analysis with dual fault detection and correction processes," IISE Transactions, Taylor & Francis Journals, vol. 48(4), pages 359-370, April.
    5. Osório, António (António Miguel) & Pinto, Alberto Adrego, 2019. "Information, uncertainty and the manipulability of artifcial intelligence autonomous vehicles systems," Working Papers 2072/376028, Universitat Rovira i Virgili, Department of Economics.
    6. Sergey V. Buldyrev & Roni Parshani & Gerald Paul & H. Eugene Stanley & Shlomo Havlin, 2010. "Catastrophic cascade of failures in interdependent networks," Nature, Nature, vol. 464(7291), pages 1025-1028, April.
    7. Kalra, Nidhi & Paddock, Susan M., 2016. "Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 182-193.
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