Paper Info
Title
Driving Risk Classification Methodology For Intelligent Drive In Real Traffic Event
Abstract
To solve the problem that existing driving data cannot correlate to the large number of vehicles in terms of driving risks, is the functionality of intelligent driving algorithm should be improved. This paper deeply explores driving data to build a link between massive driving data and a large number of sample vehicles for driving risk analysis. It sorted out certain driving behavior parameters in the driving data, and extracted some parameters closely related to the driving risk; it further utilized the principal component analysis and factor analysis in spatio-temporal data to integrate certain extracted parameters into factors that are clearly related to the specific driving risks; then, it selected factor scores of driving behaviors as indexes for hierarchical clustering, and obtained multi-level clustering results of the driving risks of corresponding vehicles; in the end, it interpreted the clustering results of the vehicle driving risks. According to the results, it is found that cluster for different risks proposed in this paper for driving behaviors is effective in the hierarchical cluster for typical driving behaviors and it also offers a solution for risk analyses between driving data and large sample vehicles. The results provide the basis for training on safe driving for the key vehicles, and the improvement of advanced driver assistance system, which shows a wide application prospect in the field of intelligent drive.
Year
DOI
Venue
2019
10.1142/S0218001419500149
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Keywords
DocType
Volume
Vehicle driving risk, driving data, classification methodology, intelligent drive
Journal
33
Issue
ISSN
Citations 
9
0218-0014
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Chuan Sun143.54
Bijun Li203.04
Yicheng Li323.45
Zhenji Lu411.04