학술논문
Multimodal Features for Detection of Driver Stress and Fatigue: Review
Document Type
Periodical
Author
Source
IEEE Transactions on Intelligent Transportation Systems IEEE Trans. Intell. Transport. Syst. Intelligent Transportation Systems, IEEE Transactions on. 22(6):3214-3233 Jun, 2021
Subject
Language
ISSN
1524-9050
1558-0016
1558-0016
Abstract
Driver fatigue and stress significantly contribute to higher number of car accidents worldwide. Although, different detection approaches have been already commercialized and used by car producers (and third party companies), research activities in this field are still needed in order to increase the reliability of these alert systems. Also, in the context of automated driving, the driver mental state assessment will be an important part of cars in future. This paper presents state-of-the-art review of different approaches for driver fatigue and stress detection and evaluation. We describe in details various signals (biological, car and video) and derived features used for these tasks and we discuss their relevance and advantages. In order to make this review complete, we also describe different datasets, acquisition systems and experiment scenarios.