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Analysis of human factors failures in an incident of self-driving car accident

Research output: Chapter in Book/Report/Conference proceedingConference contribution

There are always submerged risks involved with advanced technology; therefore, it is necessary for policymakers, inventors and technology companies to scrutinise potential risks when they consider implementing new technology. This paper attempts to extract generic lessons from a failure relevant to autonomous transport systems. We use fault tree analysis (FTA), a reliability block diagram (RBD) approach and failure mode and effect analysis (FMEA), for analysing a fatal pedestrian accident caused by a level-3 self-driving car in 2018. The work highlights the importance of prematurity of test driving selfdriving cars on public roads and the potential of an insightful analysis method that can capture human factors. In this work we theorise accident reporting systems, and provide a framework for triple loop learning.
Original languageEnglish
Title of host publicationAdvances in Human Aspects of Transportation. AHFE 2020
Number of pages8
ISBN (Electronic)978-3-030-50943-9
ISBN (Print)978-3-030-50942-2
Publication statusPublished - 1 Jul 2020
EventInternational Conference on Applied Human Factors and Ergonomics - San Diego, United States
Duration: 16 Jul 202020 Jul 2020

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


ConferenceInternational Conference on Applied Human Factors and Ergonomics
CountryUnited States
CitySan Diego


  • LABIB_2020_cright_Conf_Analysis of Human Factors Failures

    Rights statement: This is a post-peer-review, pre-copyedit version of an article published in Stanton N. (eds) Advances in Human Aspects of Transportation. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1212. The final authenticated version is available online at:

    Accepted author manuscript (Post-print), 610 KB, PDF document

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