Adaptive Driver State Monitoring with Temporal Reasoning and Risk Estimation for Safe Transportation

Hikmat Yar^1,2, Imran Ullah Khan³, Naqqash Dilshad⁴, Weiwei Jiang⁵, Heung Soo Kim^1,*
1 Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul, Republic of Korea
2 KAIST InnoCORE PRISM-AI Center, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
3 Department of AI and SW, Gachon University, Seongnam, Republic of Korea
4 Department of Computer Science & Engineering, Sejong University, Seoul, Republic of Korea
5 School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
* Corresponding Author: Heung Soo Kim. Email: email

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.084202

Received 17 April 2026; Accepted 10 July 2026; Published online 31 July 2026

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Abstract

Transportation has become an essential component of modern daily life, with continuous advancements aimed at reducing travel time and improving mobility. However, this increased convenience has also contributed to a rise in road accidents, often caused by driver distraction, fatigue, and age-related cognitive decline. These concerns have driven growing interest in Artificial Intelligence (AI)-based real-time driver monitoring systems designed to enhance road safety. Despite recent progress, several challenges remain, including limitations in detection accuracy, inadequate temporal reasoning, and high computational complexity. To address these challenges, we utilize the EfficientNetV2-S model for backbone feature extraction due to its high performance, compact model size, and fast inference speed. The model leverages Squeeze-and-Excitation (SE) attention to enhance feature learning capabilities; however, SE attention captures only channel information and overlooks important spatial features. To overcome this limitation, our framework incorporates a Driver-Monitoring Coordinate Attention (DM-CA) mechanism with modifications that encode features along both height and width directions. Beyond frame-level classification, the proposed system integrates temporal memory and reasoning to convert frame-level predictions into behavior-level insights. A risk-aware decision module evaluates the drivers state based on duration, context, and driving conditions, enabling goal-driven adaptive interventions such as warnings or alerts, supported by a feedback adaptation mechanism. We evaluated the framework on UTKFace, Fatigue, modified Fatigue, and 100-driver datasets, demonstrating its effectiveness in understanding driver distraction. Quantitative and qualitative analyses, including state-of-the-art comparison, cross-validation, statistical analysis, and computational complexity evaluation, highlight the system’s accuracy, modularity, and suitability for real-world deployment.

Keywords

Artificial neural networks; computer vision; convolutional neural network; driver distraction detection; deep learning; temporal reasoning; road accident

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Adaptive Driver State Monitoring with Temporal Reasoning and Risk Estimation for Safe Transportation

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