1. Multimodal Features for Detection of Driver Stress and Fatigue: Review
- Author
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Veronika Svozilova, Tomáš Dominik, Martin Bilík, Radim Kolar, Pavel Maxera, Andrea Nemcova, Michal Belák, Martin Mezl, Martin Seitl, Branislav Hesko, Katerina Bucsuhazy, Marek Semela, Radovan Smisek, and Matúš Šucha
- Subjects
050210 logistics & transportation ,Third party ,Computer science ,Mechanical Engineering ,05 social sciences ,Context (language use) ,physiological signals ,Field (computer science) ,Computer Science Applications ,driver stress ,Human–computer interaction ,Mental state ,traffic accident ,0502 economics and business ,Automotive Engineering ,Stress (linguistics) ,Vehicle safety ,Relevance (information retrieval) ,multimodal features ,driver fatigue ,Reliability (statistics) - 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.
- Published
- 2021