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

Author

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  • Amolika Sinha

    (Research Centre for Integrated Transport Innovation (rCITI), School of Civil and Environmental Engineering, UNSW Sydney, Sydney, NSW 2052, Australia)

  • Vincent Vu

    (Research Centre for Integrated Transport Innovation (rCITI), School of Civil and Environmental Engineering, UNSW Sydney, Sydney, NSW 2052, Australia)

  • Sai Chand

    (Research Centre for Integrated Transport Innovation (rCITI), School of Civil and Environmental Engineering, UNSW Sydney, Sydney, NSW 2052, Australia)

  • Kasun Wijayaratna

    (School of Civil and Environmental Engineering, University of Technology Sydney (UTS), Sydney, NSW 2007, Australia)

  • Vinayak Dixit

    (Research Centre for Integrated Transport Innovation (rCITI), School of Civil and Environmental Engineering, UNSW Sydney, Sydney, NSW 2052, Australia
    IAG of Risk in Smart Cities, UNSW Sydney, Sydney, NSW 2052, Australia)

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.

Suggested Citation

  • Amolika Sinha & Vincent Vu & Sai Chand & Kasun Wijayaratna & Vinayak Dixit, 2021. "A Crash Injury Model Involving Autonomous Vehicle: Investigating of Crash and Disengagement Reports," Sustainability, MDPI, vol. 13(14), pages 1-22, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:14:p:7938-:d:595318
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    References listed on IDEAS

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    1. Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
    2. Vinayak V Dixit & Sai Chand & Divya J Nair, 2016. "Autonomous Vehicles: Disengagements, Accidents and Reaction Times," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-14, December.
    3. Araz Taeihagh & Hazel Si Min Lim, 2019. "Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks," Transport Reviews, Taylor & Francis Journals, vol. 39(1), pages 103-128, January.
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    Cited by:

    1. Mohamed Abdel-Aty & Shengxuan Ding, 2024. "A matched case-control analysis of autonomous vs human-driven vehicle accidents," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. Ehsan Kohanpour & Seyed Rasoul Davoodi & Khaled Shaaban, 2024. "Analyzing Autonomous Vehicle Collision Types to Support Sustainable Transportation Systems: A Machine Learning and Association Rules Approach," Sustainability, MDPI, vol. 16(22), pages 1-41, November.

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