Clustering and deduction of typical dangerous scenarios between passenger vehicles and two-wheelers at crossroads

doi:10.16265/j.cnki.issn1003-3033.2020.04.016

Abstract

Abstract: In order to provide an effective scenario deduction and construction scheme for research and development of intelligent vehicles, statistical analysis was conducted on data of accident scenarios between passenger vehicles and two-wheelers at crossroads from National Automobile Accident In-depth Investigation System (NAIS) database, and two types of basic scenarios with high proportion were obtained. Then, seven scenario correlation variables were selected for cluster analysis on static characteristics of these two types of scenarios. Finally, a kinematics deduction model was established, and furthermore, considering actual threshold of dynamic parameters of five types of typical dangerous scenarios, speed-distance danger models of them were constructed. The results show that through methods of statistical classification, cluster analysis and kinematic model superposition deduction, five kinds of typical hazard scenes of passenger cars and two-wheeled vehicles at intersections can be obtained in conformity with traffic situation in China, and moreover five sets of dangerous scenarios are obtained.

Key words: crossroads, passenger vehicles, two-wheeler, typical dangerous scenarios, cluster analysis, deduction

CLC Number:

X951

ZHOU Hua, ZHANG Qiaoyu, MU Yaoyao, TAN Zhengping, SUN Qing, ZHANG Daowen. Clustering and deduction of typical dangerous scenarios between passenger vehicles and two-wheelers at crossroads[J]. China Safety Science Journal, 2020, 30(4): 100-107.

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