THE USE OF AI TO DETECT DROWSINESS IN DRIVERS

Abstract

Driver distraction and drowsiness are critical concerns leading to road accidents, causing significant harm in Malaysia and worldwide. This study addresses the pressing need for effective driver distraction detection systems to enhance road safety. We propose a comprehensive methodology comprising data preprocessing, data augmentation, and a novel Driver Drowsiness and Monitoring System. This system incorporates a lightweight model, EfficientNetB0, coupled with a Channel Attention (CA) mechanism, demonstrating superior performance. In the experimentation phase, we evaluate our model on two benchmark datasets: State Farm Distracted Driver Detection (SFD3) and AUC Distracted Driver (AUCD2). The results indicate that our model achieves remarkable accuracy, precision, recall, and F1-score, surpassing existing state-of-the-art models. Moreover, our model exhibits exceptional time efficiency, making it suitable for real-time applications and resource-constrained devices. This research contributes to mitigating distracted driving's adverse effects, ultimately reducing accidents and promoting safer driving practices. The proposed methodology and results showcase the potential for deploying such systems to enhance road safety and reduce road accidents.