Identification and classification of driving behaviour at a signalized intersection using support vector machine

doi:10.1007/s11633-021-1295-y

CORC > 自动化研究所 > 中国科学院自动化研究所 > 学术期刊 > International Journal of Automation and Computing

	Identification and classification of driving behaviour at a signalized intersection using support vector machine
	Soni Lanka Karri 3; Liyanage Chandratilak De Silva 2; Daphne Teck Ching Lai 1,3; Shiaw Yin Yong 3
刊名	International Journal of Automation and Computing
	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.
内容类型	期刊论文
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.