Accident Case Study Analysis of Developmental Automated Driving System Collison

Sanjeev K. Appicharla

doi:10.65351/hsi24.006

Abstract

The aim of the Case study is to produce a list of accident causal factors using the Cybernetic risk management model with the hybrid Swiss Cheese Model (SCM) and Management Oversight & Risk Tree (MORT) Methodology. The hybrid SCM/MORT methodology incorporates Jens Rasmussen’s risk management framework (RMF) and is augmented by including the Heuristics & Biases approach to make the methodology capable of identifying latent failures conditions at all levels of the socio-technical system that control the system of interest (SOI). The desk top study included collection of information and data that is publicly available to represent all relevant viewpoints to ensure completeness. The results raise awareness of latent causal factors in the form of biases that have impact on Risk management, Decision making, Assurance and wider Human factors concerns that are relevant and applicable to Artificial Intelligence/ Machine Learning (AI/ML) domain. It is hoped the Case Study will contribute to reflection on the part of systems engineers to help them plan, design, develop and operate safer automated vehicle systems.

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Citation

BibTeX citation:

@inproceedings{k._appicharla2024,
  author = {K. Appicharla, Sanjeev},
  editor = {Masson, Dimitri},
  publisher = {INCOSE},
  title = {Accident {Case} {Study} {Analysis} of {Developmental}
    {Automated} {Driving} {System} {Collison}},
  booktitle = {Proceedings International Conference on Human Systems
    Integration},
  volume = {3},
  date = {2024-08-29},
  url = {https://hsi-wg.github.io/conferences/HSI2024/proceedings/accident-case-study-analysis-of-developmental-automated-driving-system-collison.html},
  doi = {10.65351/hsi24.006},
  langid = {en},
  abstract = {The aim of the Case study is to produce a list of accident
    causal factors using the Cybernetic risk management model with the
    hybrid Swiss Cheese Model (SCM) and Management Oversight \& Risk
    Tree (MORT) Methodology. The hybrid SCM/MORT methodology
    incorporates Jens Rasmussen’s risk management framework (RMF) and is
    augmented by including the Heuristics \& Biases approach to make the
    methodology capable of identifying latent failures conditions at all
    levels of the socio-technical system that control the system of
    interest (SOI). The desk top study included collection of
    information and data that is publicly available to represent all
    relevant viewpoints to ensure completeness. The results raise
    awareness of latent causal factors in the form of biases that have
    impact on Risk management, Decision making, Assurance and wider
    Human factors concerns that are relevant and applicable to
    Artificial Intelligence/ Machine Learning (AI/ML) domain. It is
    hoped the Case Study will contribute to reflection on the part of
    systems engineers to help them plan, design, develop and operate
    safer automated vehicle systems.}
}

For attribution, please cite this work as:

K. Appicharla, Sanjeev. 2024. “Accident Case Study Analysis of Developmental Automated Driving System Collison.” In Proceedings International Conference on Human Systems Integration, edited by Dimitri Masson. Vol. 3. INCOSE. https://doi.org/10.65351/hsi24.006.