Real-time anti-sleep alert algorithm to prevent road accidents to ensure road safety

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F29142890%3A_____%2F25%3A00052432" target="_blank" >RIV/29142890:_____/25:00052432 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.frontiersin.org/journals/future-transportation/articles/10.3389/ffutr.2025.1545411/full" target="_blank" >https://www.frontiersin.org/journals/future-transportation/articles/10.3389/ffutr.2025.1545411/full</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3389/ffutr.2025.1545411" target="_blank" >10.3389/ffutr.2025.1545411</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Real-time anti-sleep alert algorithm to prevent road accidents to ensure road safety

Popis výsledku v původním jazyce

When we travel from one place to another, the first priority during our journey is that, we all wish to reach safely at our destination. Ensuring driver wakefulness is crucial for road safety, as drowsiness is a leading cause of fatal accidents, resulting in physical injuries, financial losses, and loss of life. This paper proposes an anti-sleep driver detection algorithm designed specifically for four-wheelers and larger vehicles to mitigate accidents caused by driver drowsiness. The proposed algorithm leverages deep learning (DL) models, including InceptionV3, VGG16, and MobileNetV2, for real-time detection and classification of driver drowsiness. The models were trained and evaluated using comprehensive performance metrics, such as accuracy, precision, recall, F1 score, and confusion matrix. The proposed method outperforms the traditional approaches such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Haar Cascade Classifiers, and other DL architectures like Xception and VGG16, in terms of accuracy and efficiency. Among the tested models, InceptionV3 demonstrated superior performance, achieving an accuracy of 99.18%, a validation loss of 0.85%, and execution time of 0.2 s on Raspberry Pi platform. The results suggest that the proposed algorithm provides a robust and effective solution for real-time driver drowsiness detection thereby contributing towards enhanced safety.

Název v anglickém jazyce

Real-time anti-sleep alert algorithm to prevent road accidents to ensure road safety

Popis výsledku anglicky

When we travel from one place to another, the first priority during our journey is that, we all wish to reach safely at our destination. Ensuring driver wakefulness is crucial for road safety, as drowsiness is a leading cause of fatal accidents, resulting in physical injuries, financial losses, and loss of life. This paper proposes an anti-sleep driver detection algorithm designed specifically for four-wheelers and larger vehicles to mitigate accidents caused by driver drowsiness. The proposed algorithm leverages deep learning (DL) models, including InceptionV3, VGG16, and MobileNetV2, for real-time detection and classification of driver drowsiness. The models were trained and evaluated using comprehensive performance metrics, such as accuracy, precision, recall, F1 score, and confusion matrix. The proposed method outperforms the traditional approaches such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Haar Cascade Classifiers, and other DL architectures like Xception and VGG16, in terms of accuracy and efficiency. Among the tested models, InceptionV3 demonstrated superior performance, achieving an accuracy of 99.18%, a validation loss of 0.85%, and execution time of 0.2 s on Raspberry Pi platform. The results suggest that the proposed algorithm provides a robust and effective solution for real-time driver drowsiness detection thereby contributing towards enhanced safety.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20104 - Transport engineering

Projekt

—

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Frontiers in Future Transportation

ISSN

2673-5210

e-ISSN

—

Svazek periodika

—

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

14

Strana od-do

1-14

Kód UT WoS článku

001451644900001

EID výsledku v databázi Scopus

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