Toward Safer Highways Predicting Driver Stress in Varying Conditions on Habitual Routes

被引：6

作者：

Corcoba Magana, Victor ^{[1
]}

Munoz-Organero, Mario ^{[2
,3
,4
]}

机构：

[1] Univ Oviedo, Telemat Engn, Asturias, Spain

[2] Carlos III Univ Madrid, Telemat Engn, Madrid, Spain

[3] TU1305 European Cooperat Sci & Technol, Brussels, Belgium

[4] IEEE, Piscataway, NJ USA

来源：

IEEE VEHICULAR TECHNOLOGY MAGAZINE | 2017年 / 12卷 / 04期

关键词：

D O I：

10.1109/MVT.2017.2692059

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Driver stress is a growing problem in the transportation industry. It causes a deterioration of cognitive skills, resulting in poor driving and an increase in the likelihood of traffic accidents. Prediction models allow us to avoid or at least minimize the negative consequences of stress. In this article, an algorithm based on deep learning is proposed to predict driver stress. This type of algorithm detects complex relationships among variables. At the same time, it avoids overfitting. The prediction of the upcoming stress level is made by taking into account driving behavior (acceleration, deceleration, speed) and the previous stress level. © 2005-2012 IEEE.

引用

页码：69 / 76

页数：8