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

Ashraf Labib; Yoskue Nagase; Sara Hadleigh-Dunn

doi:10.1007/978-3-030-50943-9_28

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

Ashraf Labib, Yoskue Nagase, Sara Hadleigh-Dunn

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

227 Downloads (Pure)

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 language	English
Title of host publication	Advances in Human Aspects of Transportation. AHFE 2020
Publisher	Springer
Pages	221-228
Number of pages	8
ISBN (Electronic)	978-3-030-50943-9
ISBN (Print)	978-3-030-50942-2
DOIs	https://doi.org/10.1007/978-3-030-50943-9_28
Publication status	Published - 1 Jul 2020
Event	International Conference on Applied Human Factors and Ergonomics - San Diego, United States Duration: 16 Jul 2020 → 20 Jul 2020

Publication series

Name	Advances in Intelligent Systems and Computing
Publisher	Springer
Volume	1212
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	International Conference on Applied Human Factors and Ergonomics
Country/Territory	United States
City	San Diego
Period	16/07/20 → 20/07/20

Keywords

Learning from failures
Self-driving car
Accident analysis

Access to Document

10.1007/978-3-030-50943-9_28

LABIB_2020_cright_Conf_Analysis of Human Factors Failures
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: http://dx.doi.org/10.1007/978-3-030-50943-9_28.
Accepted author manuscript (Post-print), 610 KB

Cite this

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title = "Analysis of human factors failures in an incident of self-driving car accident",

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. ",

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author = "Ashraf Labib and Yoskue Nagase and Sara Hadleigh-Dunn",

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Labib, A, Nagase, Y & Hadleigh-Dunn, S 2020, Analysis of human factors failures in an incident of self-driving car accident. in Advances in Human Aspects of Transportation. AHFE 2020., 28, Advances in Intelligent Systems and Computing, vol. 1212, Springer, pp. 221-228, International Conference on Applied Human Factors and Ergonomics, San Diego, California, United States, 16/07/20. https://doi.org/10.1007/978-3-030-50943-9_28

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AU - Nagase, Yoskue

AU - Hadleigh-Dunn, Sara

N1 - Figure and Table numbers in AAM are odd, but none missing.

PY - 2020/7/1

Y1 - 2020/7/1

N2 - 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.

AB - 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.

KW - Learning from failures

KW - Self-driving car

KW - Accident analysis

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

Abstract

Publication series

Conference

Keywords

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Cite this