Multimodal features for detection of driver stress and fatigue: review

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15210%2F20%3A73600827" target="_blank" >RIV/61989592:15210/20:73600827 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216305:26220/20:PU135930

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9031734/media#media" target="_blank" >https://ieeexplore.ieee.org/document/9031734/media#media</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TITS.2020.2977762" target="_blank" >10.1109/TITS.2020.2977762</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multimodal features for detection of driver stress and fatigue: review

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Multimodal features for detection of driver stress and fatigue: review

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

<a href="/cs/project/TL01000216" target="_blank" >TL01000216: Komplexní fyziologické monitorování řidiče s ohledem na psychologické faktory ovlivňující chování při jízdě</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

ISSN

1524-9050

e-ISSN

—

Svazek periodika

2020

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

20

Strana od-do

1-20

Kód UT WoS článku

000658360600001

EID výsledku v databázi Scopus

2-s2.0-85107408203