TY  - EJOU
AU  - Yar, Hikmat 
AU  - Khan, Imran Ullah 
AU  - Dilshad, Naqqash 
AU  - Jiang, Weiwei 
AU  - Kim, Heung Soo 

TI  - Adaptive Driver State Monitoring with Temporal Reasoning and Risk Estimation for Safe Transportation
T2  - Computer Modeling in Engineering \& Sciences

PY  - 
VL  - 
IS  - 
SN  - 1526-1506

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

DO  - 10.32604/cmes.2026.084202