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IJIEE
Articles
Published: 2026-09-30

Enhancing Real-Time Drowsiness Detection: A Connectivity-Aware Graph Neural Network Approach

Manager - Architecture Valuemomentum, Erie, PA-16506, USA
Assistant Professor Department of CSE(AI) Vemu Institute of Technology, Chittor
Chief Technology Officer Fenix Commerce Inc San Jose, California - 95129, USA
Drowsiness classification Graph Neural Network (GNN Connectivity-aware Recurrent Neural Networks (RNN) Gated Recurrent Units (GRU) XGBoost Random Forest (RF) Real-time detection Random Forest Fatigue monitoring Driver safety

Abstract

Drowsiness detection plays a critical role in enhancing driver safety and preventing accidents due to fatigue. Our approach integrates the advantages of several advanced machine learning algorithms to improve prediction accuracy and responsiveness. Specifically, we employ a Connectivity-Aware Graph Neural Network (CAGNN) to model the spatial and temporal dependencies in driver behavior, Graph Neural Network (GNN) coupled with a Recurrent Neural Network (RNN) architecture using Gated Recurrent Units (GRU) to capture long-term sequential patterns. Furthermore, the XGBoost algorithm is utilized for feature enhancement, and Random Forest (RF) is used to provide an ensemble learning framework for robust classification. The CAGNN framework is designed to dynamically adjust to real-time changes in connectivity and vehicle environment, ensuring seamless performance even in varying conditions. Experimental results demonstrate that our model significantly outperforms traditional drowsiness detection methods in terms of accuracy, latency, and adaptability to real-world conditions.

References

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How to Cite

Haranadha Reddy Busireddy Seshakagari, R Madhavi, & Sharath Chandra Parashara. (2026). Enhancing Real-Time Drowsiness Detection: A Connectivity-Aware Graph Neural Network Approach. International Journal of Interpreting Enigma Engineers (IJIEE), 3(3), 21–33. Retrieved from https://ejournal.svgacademy.org/index.php/ijiee/article/view/477

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