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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20104 - Transport engineering

Project

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Continuities

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Publication year

2025

Confidentiality

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

Name of the periodical

Frontiers in Future Transportation

ISSN

2673-5210

e-ISSN

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Volume of the periodical

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Issue of the periodical within the volume

6

Country of publishing house

CH - SWITZERLAND

Number of pages

14

Pages from-to

1-14

UT code for WoS article

001451644900001

EID of the result in the Scopus database

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