Investigating safety and liability of autonomous vehicles: Bayesian random parameter ordered probit model analysis
Purpose – This study aims to investigate the safety and liability of autonomous vehicles (AVs), and identify the contributing factors quantitatively so as to provide potential insights on safety and liability of AVs. Design/methodology/approach – The actual crash data were obtained from California D...
| Autores principales: | , , , |
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| Formato: | Artículo |
| Lenguaje: | English |
| Publicado: |
Tsinghua University Press
2022-10-01
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| Colección: | Journal of Intelligent and Connected Vehicles |
| Materias: | |
| Acceso en línea: | https://www.emerald.com/insight/content/doi/10.1108/JICV-04-2022-0012/full/pdf?title=investigating-safety-and-liability-of-autonomous-vehicles-bayesian-random-parameter-ordered-probit-model-analysis |
| _version_ | 1827365036934299648 |
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| author | Quan Yuan Xuecai Xu Tao Wang Yuzhi Chen |
| author_facet | Quan Yuan Xuecai Xu Tao Wang Yuzhi Chen |
| author_sort | Quan Yuan |
| collection | DOAJ |
| description | Purpose – This study aims to investigate the safety and liability of autonomous vehicles (AVs), and identify the contributing factors quantitatively so as to provide potential insights on safety and liability of AVs. Design/methodology/approach – The actual crash data were obtained from California DMV and Sohu websites involved in collisions of AVs from 2015 to 2021 with 210 observations. The Bayesian random parameter ordered probit model was proposed to reflect the safety and liability of AVs, respectively, as well as accommodating the heterogeneity issue simultaneously. Findings – The findings show that day, location and crash type were significant factors of injury severity while location and crash reason were significant influencing the liability. Originality/value – The results provide meaningful countermeasures to support the policymakers or practitioners making strategies or regulations about AV safety and liability. |
| first_indexed | 2024-03-08T08:21:05Z |
| format | Article |
| id | doaj.art-35ca7b672e9543baa0e81634effb2425 |
| institution | Directory Open Access Journal |
| issn | 2399-9802 |
| language | English |
| last_indexed | 2024-03-08T08:21:05Z |
| publishDate | 2022-10-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Journal of Intelligent and Connected Vehicles |
| spelling | doaj.art-35ca7b672e9543baa0e81634effb24252024-02-02T05:49:21ZengTsinghua University PressJournal of Intelligent and Connected Vehicles2399-98022022-10-015319920510.1108/JICV-04-2022-0012688165Investigating safety and liability of autonomous vehicles: Bayesian random parameter ordered probit model analysisQuan Yuan0Xuecai Xu1Tao Wang2Yuzhi Chen3State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, ChinaSchool of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, ChinaSchool of Architecture and Transportation, Guilin University of Electronic Technology, Guilin, ChinaSchool of Architecture and Transportation, Guilin University of Electronic Technology, Guilin, ChinaPurpose – This study aims to investigate the safety and liability of autonomous vehicles (AVs), and identify the contributing factors quantitatively so as to provide potential insights on safety and liability of AVs. Design/methodology/approach – The actual crash data were obtained from California DMV and Sohu websites involved in collisions of AVs from 2015 to 2021 with 210 observations. The Bayesian random parameter ordered probit model was proposed to reflect the safety and liability of AVs, respectively, as well as accommodating the heterogeneity issue simultaneously. Findings – The findings show that day, location and crash type were significant factors of injury severity while location and crash reason were significant influencing the liability. Originality/value – The results provide meaningful countermeasures to support the policymakers or practitioners making strategies or regulations about AV safety and liability.https://www.emerald.com/insight/content/doi/10.1108/JICV-04-2022-0012/full/pdf?title=investigating-safety-and-liability-of-autonomous-vehicles-bayesian-random-parameter-ordered-probit-model-analysissafetybayesian random parameter ordered probit modelliabilityautonomous vehiclesadvanced vehicle safety systems |
| spellingShingle | Quan Yuan Xuecai Xu Tao Wang Yuzhi Chen Investigating safety and liability of autonomous vehicles: Bayesian random parameter ordered probit model analysis Journal of Intelligent and Connected Vehicles safety bayesian random parameter ordered probit model liability autonomous vehicles advanced vehicle safety systems |
| title | Investigating safety and liability of autonomous vehicles: Bayesian random parameter ordered probit model analysis |
| title_full | Investigating safety and liability of autonomous vehicles: Bayesian random parameter ordered probit model analysis |
| title_fullStr | Investigating safety and liability of autonomous vehicles: Bayesian random parameter ordered probit model analysis |
| title_full_unstemmed | Investigating safety and liability of autonomous vehicles: Bayesian random parameter ordered probit model analysis |
| title_short | Investigating safety and liability of autonomous vehicles: Bayesian random parameter ordered probit model analysis |
| title_sort | investigating safety and liability of autonomous vehicles bayesian random parameter ordered probit model analysis |
| topic | safety bayesian random parameter ordered probit model liability autonomous vehicles advanced vehicle safety systems |
| url | https://www.emerald.com/insight/content/doi/10.1108/JICV-04-2022-0012/full/pdf?title=investigating-safety-and-liability-of-autonomous-vehicles-bayesian-random-parameter-ordered-probit-model-analysis |
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