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

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Abstract

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
PublisherSpringer
Pages221-228
Number of pages8
ISBN (Electronic)978-3-030-50943-9
ISBN (Print)978-3-030-50942-2
DOIs
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
PublisherSpringer
Volume1212
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

ConferenceInternational Conference on Applied Human Factors and Ergonomics
CountryUnited States
CitySan Diego
Period16/07/2020/07/20

Keywords

  • Learning from failures
  • Self-driving car
  • Accident analysis

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