Citation

Zhang H, Liu Y, Wu C, Ding N, Zhang Q, Xiao Y. China Saf. Sci. J. 2023; 33(7): 24-31.

Copyright

(Copyright © 2023, China Occupational Safety and Health Association, Publisher Gai Xue bao)

DOI

10.16265/j.cnki.issn1003-3033.2023.07.1120

PMID

unavailable

Abstract

Aiming at the lack of risk degree measurement and insufficient consideration of individual differences in the driving behavior risk assessment method, the natural driving experimental data of 15 subjects were collected, and the paired T⁃test and DBSCAN (Density⁃Based Spatial Clustering of Applications with Noise) clustering were used to obtain the deviation of the indicator from the normal state in driving safety events and driver risk propensity level. The indicators were selected to quantify the severity of a single driving safety event, and the driving risk weights were corrected to construct a driving behavior risk assessment method that considered the severity of driving events and individual differences. The validity of the model was verified by using time head (TH). The results show that speed standard deviation, speed range and mean and maximum value of acceleration are more important for driving risk assessment. The risk score obtained by the optimized evaluation methods ranges from [21,42. 6], with a mean value of 32. 93 and a standard deviation of 6. 62. The driving behavior risk score in this study is closer to the actual situation than the traditional score. The above indicators can be used to evaluate the comprehensive driving behavior risk and improve the accuracy of driving risk identification. © 2023 Fine Chemicals. All rights reserved.

Language: zh