Publication:
Toward safer highways: predicting driver stress in varying conditions on habitual routes

Thumbnail Image
Identifiers
URI: https://hdl.handle.net/10016/31863
ISSN: 1556-6072
DOI: https://doi.org/10.1109/MVT.2017.2692059
UXXI: AR/0000026369
Files
toward_IEEE-VTM_ps_2017.pdf (618.26 KB)
Publication date
2017-12-01
Defense date
Authors
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Impact
Google Scholar
Export
Research Projects
Organizational Units
Journal Issue
Abstract
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.
Description
Keywords
Prediction algorithms, Stress measurement, Heart rate variability, Acceleration, Vehicle safety, Human factors
Bibliographic citation
IEEE Vehicular Technology Magazine, (2017), 12(4), pp.: 69 - 76.
Collections
DIT - GAST - Artículos de Revistas