Open AccessSafety assessment of vehicle behaviour based on the improved D–S evidence theory
Author(s) -
Cheng Xin,
Zhou Jingmei,
Zhao Xiangmo
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2019.0737
Subject(s) - active safety , reliability (semiconductor) , vehicular ad hoc network , vehicle to vehicle , warning system , collision , computer science , road traffic safety , identification (biology) , dempster–shafer theory , transport engineering , vehicle information and communication system , wireless ad hoc network , automotive engineering , engineering , computer security , reliability engineering , road traffic , data mining , computer network , telecommunications , power (physics) , physics , botany , quantum mechanics , biology , wireless
Vehicle dangerous behaviour warning plays an important role to improve road traffic safety and efficiency, so a safety assessment method of vehicle behaviour based on the improved Dempster–Shafer (D–S) evidence theory is proposed. Firstly, through analysis of vehicle collision accident mechanism, some factors closely related to vehicle safety are extracted. Also, multiple sensors are synthetically utilised to collect information, which realises the awareness of vehicle state, road attribute, driving environment etc. Then vehicle behaviour identification is accomplished according to the parameter information of the vehicle‐mounted sensors, as well as the related data of adjacent vehicles in vehicular ad hoc networks (VANET). Finally, a sequential type of weighted correction method based on evidence variance is used to integrate different levels of multi‐source heterogeneous information and to achieve safety assessment of vehicle behaviour. The experimental results show that the improved D–S evidence theory reduces the evidence conflict, increasing the accuracy and reliability of vehicle behaviour safety assessment. The study solves the fundamental core problem of active safety warning in VANET and provides a new means of traffic accident warning for the road traffic management department.