Citation

Qin Y, Li Q, Zhao P, Bao F, Xie J. China Saf. Sci. J. 2022; 32(4): 141-147.

Vernacular Title

基于多分类Adaboost算法的驾驶人风险感知倾向研究

Copyright

(Copyright © 2022, China Occupational Safety and Health Association, Publisher Gai Xue bao)

DOI

10.16265/j.cnki.issn1003-3033.2022.04.021

PMID

unavailable

Abstract

In order to prevent and reduce road traffic accidents, multi-class Adaboost SAMME algorithm was applied to identify risk perception tendency of different drivers. Firstly, perception utility of 56 drivers was quantified based on traffic conflict analysis method. Then, drivers' behavior characterization parameters in 6 risky situations were obtained through KMRTDS driving simulator. Finally, linear discriminant analysis (LDA) and Adaboost SAMME algorithm were employed to gradually construct a classification and prediction model for drivers' risk perception tendency based on driving behavior data, and k-fold cross-validation method was adopted to evaluate the model's effectiveness. The results show that the accuracy of proposed model is up to 92.9%, and it can effectively identify risk perception tendency of different drivers which are divided into three risk perception types, namely safe type, radical type, and compound type.

===

为预防和减少道路交通事故，利用多分类Adaboost SAMME算法辨识不同驾驶人的风险感知倾向。首先基于交通冲突分析方法，量化56位驾驶人的风险感知效用值；然后通过KMRTDS驾驶模拟器获取驾驶人在6个风险驾驶情境中的行为表征参数；最后运用线性判别分析（LDA）、Adaboost SAMME算法逐步构建基于驾驶行为数据的驾驶人风险感知倾向分类预测模型，并采用k折交叉验证法评估该模型的有效性。研究结果表明：所提模型预测准确率达92.9%,可以有效辨识不同驾驶人的风险感知水平，将驾驶人分为安全、激进、复合3种类型。

Language: en