Identification and classification of driving behaviour at a signalized intersection using support vector machine
文献类型：期刊论文
| 作者 | Soni Lanka Karri3; Liyanage Chandratilak De Silva2; Daphne Teck Ching Lai1,3; Shiaw Yin Yong3 |
| 刊名 | International Journal of Automation and Computing
 |
| 出版日期 | 2021 |
| 卷号 | 18期号:3页码:480-491 |
| 关键词 | Signalized intersection driving behaviour machine learning support vector machine (SVM) road accidents |
| ISSN号 | 1476-8186 |
| DOI | 10.1007/s11633-021-1295-y |
| 英文摘要 | When the drivers approaching signalized intersections (onset of yellow signal), the drivers would enter into a zone, where they will be in uncertain mode assessing their capabilities to stop or cross the intersection. Therefore, any improper decision might lead to a right-angle or back-end crash. To avoid a right-angle collision, drivers apply the harsh brakes to stop just before the signalized intersection. But this may lead to a back-end crash when the following driver encounters the former′s sudden stopping decision. This situation gets multifaceted when the traffic is heterogeneous, containing various types of vehicles. In order to reduce this issue, this study′s primary objective is to identify the driving behaviour at signalized intersections based on the driving features (parameters). The secondary objective is to classify the outcome of driving behaviour (safe stopping and unsafe stopping) at the signalized intersection using a support vector machine (SVM) technique. Turning moments are used to identify the zones and label them accordingly for further classification. The classification of 50 instances is identified for training and testing using a 70%−30% rule resulted in an accuracy of 85% and 86%, respectively. Classification performance is further verified by random sampling using five cross-validation and 30 iterations, which gave an accuracy of 97% and 100% for training and testing. These results demonstrate that the proposed approach can help develop a pre-warning system to alert the drivers approaching signalized intersections, thus reducing back-end crash and accidents. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/44296]  |
| 专题 | 自动化研究所_学术期刊_International Journal of Automation and Computing |
| 作者单位 | 1.Institute of Applied Data Analytics, Universiti Brunei Darussalam, Bandar Seri Begawan BE 1410, Brunei Darussalam 2.Faculty of Integrated Technologies, Universiti Brunei Darussalam, Bandar Seri Begawan BE 1410, Brunei Darussalam 3.Faculty of Science, Universiti Brunei Darussalam, Bandar Seri Begawan BE 1410, Brunei Darussalam |
| 推荐引用方式 GB/T 7714 | Soni Lanka Karri,Liyanage Chandratilak De Silva,Daphne Teck Ching Lai,et al. Identification and classification of driving behaviour at a signalized intersection using support vector machine[J]. International Journal of Automation and Computing,2021,18(3):480-491. |
| APA | Soni Lanka Karri,Liyanage Chandratilak De Silva,Daphne Teck Ching Lai,&Shiaw Yin Yong.(2021).Identification and classification of driving behaviour at a signalized intersection using support vector machine.International Journal of Automation and Computing,18(3),480-491. |
| MLA | Soni Lanka Karri,et al."Identification and classification of driving behaviour at a signalized intersection using support vector machine".International Journal of Automation and Computing 18.3(2021):480-491. |
入库方式： OAI收割
来源：自动化研究所
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