A Crash Injury Model Involving Autonomous Vehicle: Investigating of Crash and Disengagement Reports

Sinha, A; Vu, V; Chand, S; Wijayaratna, K; Dixit, V

A Crash Injury Model Involving Autonomous Vehicle: Investigating of Crash and Disengagement Reports

Sinha, A Vu, V Chand, S Wijayaratna, K Dixit, V

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Sustainability, 2021, 13, (14)
Issue Date:: 2021-07-16

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Field	Value	Language
dc.contributor.author	Sinha, A
dc.contributor.author	Vu, V
dc.contributor.author	Chand, S
dc.contributor.author	Wijayaratna, K
dc.contributor.author	Dixit, V
dc.date.accessioned	2022-02-20T23:39:02Z
dc.date.available	2022-02-20T23:39:02Z
dc.date.issued	2021-07-16
dc.identifier.citation	Sustainability, 2021, 13, (14)
dc.identifier.issn	2071-1050
dc.identifier.issn	2071-1050
dc.identifier.uri	http://hdl.handle.net/10453/154706
dc.description.abstract	Autonomous vehicles (AVs) are being extensively tested on public roads in several states in the USA, such as California, Florida, Nevada, and Texas. AV utilization is expected to increase into the future, given rapid advancement and development in sensing and navigation technologies. This will eventually lead to a decline in human driving. AVs are generally believed to mitigate crash frequency, although the repercussion of AVs on crash severity is ambiguous. For the data-driven and transparent deployment of AVs in California, the California Department of Motor Vehicles (CA DMV) commissioned AV manufacturers to draft and publish reports on disengagements and crashes. This study performed a comprehensive assessment of CA DMV data from 2014 to 2019 from a safety standpoint, and some trends were discerned. The results show that decrement in automated disengagements does not necessarily imply an improvement in AV technology. Contributing factors to the crash severity of an AV are not clearly defined. To further understand crash severity in AVs, the features and issues with data are identified and discussed using different machine learning techniques. The CA DMV accident report data were utilized to develop a variety of crash AV severity models focusing on the injury for all crash typologies. Performance metrics were discussed, and the bagging classifier model exhibited the best performance among different candidate models. Additionally, the study identified potential issues with the CA DMV data reporting protocol, which is imperative to share with the research community. Recommendations are provided to enhance the existing reports and append new domains
dc.language	English
dc.publisher	MDPI AG
dc.relation.ispartof	Sustainability
dc.relation.isbasedon	10.3390/su13147938
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	12 Built Environment and Design
dc.title	A Crash Injury Model Involving Autonomous Vehicle: Investigating of Crash and Disengagement Reports
dc.type	Journal Article
utslib.citation.volume	13
utslib.for	12 Built Environment and Design
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2022-02-20T23:38:59Z
pubs.issue	14
pubs.publication-status	Published
pubs.volume	13
utslib.citation.issue	14

Abstract:

Autonomous vehicles (AVs) are being extensively tested on public roads in several states in the USA, such as California, Florida, Nevada, and Texas. AV utilization is expected to increase into the future, given rapid advancement and development in sensing and navigation technologies. This will eventually lead to a decline in human driving. AVs are generally believed to mitigate crash frequency, although the repercussion of AVs on crash severity is ambiguous. For the data-driven and transparent deployment of AVs in California, the California Department of Motor Vehicles (CA DMV) commissioned AV manufacturers to draft and publish reports on disengagements and crashes. This study performed a comprehensive assessment of CA DMV data from 2014 to 2019 from a safety standpoint, and some trends were discerned. The results show that decrement in automated disengagements does not necessarily imply an improvement in AV technology. Contributing factors to the crash severity of an AV are not clearly defined. To further understand crash severity in AVs, the features and issues with data are identified and discussed using different machine learning techniques. The CA DMV accident report data were utilized to develop a variety of crash AV severity models focusing on the injury for all crash typologies. Performance metrics were discussed, and the bagging classifier model exhibited the best performance among different candidate models. Additionally, the study identified potential issues with the CA DMV data reporting protocol, which is imperative to share with the research community. Recommendations are provided to enhance the existing reports and append new domains

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/154706