Risk identification method of dangerous driving behavior based on sliding window feature fusion

被引：0

作者：

Chu Y.X. ^{[1
]}

Zhang J. ^{[2
]}

Yang L. ^{[1
]}

机构：

[1] School of Electronics and Information Engineering, Sias University, Zhengzhou

[2] College of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou

来源：

Advances in Transportation Studies | 2022年 / 4卷 / Special issue期

关键词：

Characteristic multiplication; Convlstm cascade; Dangerous driving behavior; Feature fusion; Sliding window;

D O I：

10.53136/979122180276413

中图分类号：

学科分类号：

摘要：

It is of great significance to effectively identify the dangerous driving behaviors of drivers in real time. In order to improve the accuracy of driving behavior classification and the efficiency of risk identification, this paper proposes a risk identification method of road traffic dangerous driving behavior based on sliding window feature fusion. Firstly, the influencing factors of driving danger scenes are obtained, and the driving behavior characteristics are extracted according to the sliding window method; Then, the feature multiplication is used to calculate the time feature, and the spatial feature under each channel is calculated. The feature fusion method is used to achieve the spatio-temporal feature fusion; Finally, the risk identification function is constructed according to the ConvLSTM cascade method, and the risk identification result is obtained. The experimental results show that the classification accuracy of this method can reach 96.93%, the recognition accuracy is 93%, and the time consumption is less than 30s. The method of road traffic dangerous driving behavior risk identification based on sliding window feature fusion has better accuracy of driving behavior classification and better risk identification efficiency. © 2022, Aracne Editrice. All rights reserved.

引用

页码：133 / 144

页数：11

共 16 条

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