Citation

Munoz-Organero M, Corcoba-Magaña V. IEEE Trans. Intel. Transp. Syst. 2017; 18(7): 1802-1811.

Copyright

(Copyright © 2017, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2016.2618424

PMID

unavailable

Abstract

The levels of stress while driving affect the way we drive and have an impact on the likelihood of having an accident. Different types of sensors, such as heart rate or skin conductivity sensors, have been previously used to measure stress related features. Estimated stress levels could be used to adapt the driver's environment to minimize distractions in high cognitive demanding situations and to promote stress-friendly driving behaviors. The way we drive has an impact on how stressors affect the perceived cognitive demands by drivers, and at the same time, the perceived stress has an impact on the actions taken by the driver. In this paper, the authors evaluate how effectively upcoming stress levels can be predicted considering current stress levels, current driving behavior, and the shape of the road. The authors use features, such as the positive kinetic energy and severity of curves on the road to estimate how stress levels will evolve in the next minute. Different machine learning techniques are evaluated and the results for both intra and inter-city driving and for both intra and inter driver data are presented. The authors have used data from four different drivers with three different car models and a motorbike and more than 220 test drives.

RESULTS show that upcoming stress levels can be accurately predicted for a single user (correlation r = 0.99 and classification accuracy 97.5%) but prediction for different users is more limited (correlation r = 0.92 and classification accuracy 46.9%).

Language: en

Keywords

Driving; Human factors; Motor vehicles; Stress (Psychology); Algorithms; Classification; Machine learning; Cognition; Mathematical prediction; Stress (Physiology)