A recognition method for driver's cognitive distraction in simulated mixed traffic environment

被引：0
|
作者：
Hua Q. [1 ]
Jin L.-S. [1 ,2 ]
Guo B.-C. [1 ]
Zhang S.-R. [1 ]
Wang Y.-H. [1 ]
机构：
[1] College of Transportation, Jilin University, Changchun
[2] School of Vehicle and Energy, Yanshan University, Qinhuangdao
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2022年 / 52卷 / 08期
关键词：
Bi-LSTM model; distracted driving; mixed traffic environment; recursive feature elimination algorithm;
D O I：
10.13229/j.cnki.jdxbgxb20210215
中图分类号：
学科分类号：
摘要：
To reduce traffic accidents in an environment where intelligent connected vehicle and non-connected vehicle are mixed, a cognitive distraction recognition model based on bi-directional long short-term memory（Bi-LSTM）with attention mechanism at unsignalized intersections was proposed in a mixed traffic environment. The driving simulator data of 60 drivers in the mixed traffic environment was collected and support vector machine recursive feature elimination algorithm（SVM-RFE）was adopted to extract the optimal feature subset as the input of the model. The results show that the recognition accuracy of the model is as high as 96.58% and the F1-scores is 96.24%. Compared with SVM and decision tree distraction recognition models, this model has the best performance in terms of accuracy, recall, the F1scores and the ROC curve. The model can be applied to the autonomous driving distraction alarm assistance system, which is of great significance to improving road safety. © 2022 Editorial Board of Jilin University. All rights reserved.
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页码：1800 / 1807
页数：7
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