دورية أكاديمية
Investigating Collision Patterns to Support Autonomous Driving Safety
| العنوان: | Investigating Collision Patterns to Support Autonomous Driving Safety |
|---|---|
| المؤلفون: | Lee, Carmen Kar Hang, Leung, Ka Ho, Tse, Ying Kei, Tsao, Yu-Chung |
| بيانات النشر: | Taylor & Francis |
| سنة النشر: | 2023 |
| المجموعة: | The University of Liverpool Repository |
| الوصف: | There is a debate on the importance of autonomous vehicles (AVs) and the methods for ensuring AV safety. This paper analyses collision reports to determine the association between risk factors and the level of damage to an AV due to collisions. Association rule mining was used to develop methodologies that can advance result interpretability, which is crucial in the transportation field. Twenty-one rules were discovered to reveal the factors that co-occur with AV damage. This study demonstrates that collision data, when analysed using appropriate machine learning algorithms, can generate useful insights that complement current policies to enhance AV safety. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | text |
| اللغة: | English |
| العلاقة: | http://livrepository.liverpool.ac.uk/3172019/1/Author%20Accepted%20Manuscrupt_EIS.pdfTest; Lee, Carmen Kar Hang, Leung, Ka Ho orcid:0000-0003-2058-0287 , Tse, Ying Kei and Tsao, Yu-Chung (2023) Investigating Collision Patterns to Support Autonomous Driving Safety. Enterprise Information Systems. |
| DOI: | 10.1080/17517575.2023.2243460 |
| الإتاحة: | https://doi.org/10.1080/17517575.2023.2243460Test http://livrepository.liverpool.ac.uk/3172019Test/ http://livrepository.liverpool.ac.uk/3172019/1/Author%20Accepted%20Manuscrupt_EIS.pdfTest |
| حقوق: | cc_by_nc_4 |
| رقم الانضمام: | edsbas.EE0AFEA0 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1080/17517575.2023.2243460 |
|---|