Driver Response Time and Behavior Profiles, Extracted from Sugeno Fuzzy Models by the Louvain Network Clustering
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10254682" target="_blank" >RIV/61989100:27240/22:10254682 - isvavai.cz</a>
Výsledek na webu
<a href="https://link.springer.com/chapter/10.1007/978-3-031-14627-5_41" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-14627-5_41</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-14627-5_41" target="_blank" >10.1007/978-3-031-14627-5_41</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Driver Response Time and Behavior Profiles, Extracted from Sugeno Fuzzy Models by the Louvain Network Clustering
Popis výsledku v původním jazyce
One of the essential parameters of car driving is the constant monitoring of the surrounding traffic. The driver's attention is affected by the number of external distractions and, of course, by the driver's health and fatigue. In modern vehicles, driver attention assistants are often installed to provide early warning of loss of attention or micro-sleep. In this paper, we focus on analyzing the driver's behavior in a simulated environment and his/her reaction during a sudden change of direction due to an external event. A model of each driver's behavior is based on a Sugeno-type fuzzy system created from each driver's measured data. Then, for a group of 38 drivers, clustering their fuzzy models is performed to find drivers with similar behavior, reaction time, and vehicle stabilization time in the lane. (C) 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Název v anglickém jazyce
Driver Response Time and Behavior Profiles, Extracted from Sugeno Fuzzy Models by the Louvain Network Clustering
Popis výsledku anglicky
One of the essential parameters of car driving is the constant monitoring of the surrounding traffic. The driver's attention is affected by the number of external distractions and, of course, by the driver's health and fatigue. In modern vehicles, driver attention assistants are often installed to provide early warning of loss of attention or micro-sleep. In this paper, we focus on analyzing the driver's behavior in a simulated environment and his/her reaction during a sudden change of direction due to an external event. A model of each driver's behavior is based on a Sugeno-type fuzzy system created from each driver's measured data. Then, for a group of 38 drivers, clustering their fuzzy models is performed to find drivers with similar behavior, reaction time, and vehicle stabilization time in the lane. (C) 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
10200 - Computer and information sciences
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2022
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Lecture Notes in Networks and Systems. Volume 527
ISBN
978-3-031-14626-8
ISSN
2367-3370
e-ISSN
2367-3389
Počet stran výsledku
10
Strana od-do
403-412
Název nakladatele
Springer
Místo vydání
Cham
Místo konání akce
Sanda
Datum konání akce
7. 9. 2022
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
—