Multimodal Features for Detection of Driver Stress and Fatigue: Review

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.

Keywords

driver fatigue, driver stress, traffic accident, physiological signals, multimodal features

Citation

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS. 2021, vol. 22, issue 6, p. 3214-3233.
https://ieeexplore.ieee.org/document/9031734

Document type

Peer-reviewed

Document version

Accepted version

Language of document

en

Document licence

(C) IEEE