Artificial Intelligence-Based Drowsiness Detection System for Driver's Safety

Driver drowsiness is a road-safety concern because reduced alertness can impair attention and delay responses to unexpected events. This paperpresents a literature-based synthesis and comparative analysis of camera-based artificial-intelligence techniques for identifying visible indicatorsassociated with driver drowsiness. The study discusses facial landmark detection, Eye Aspect Ratio (EAR), Mouth Aspect Ratio (MAR),convolutional neural networks (CNNs), Support Vector Machine (SVM) classification, transfer learning, and temporal decision logic. Itcompares geometric feature-based, conventional machine-learning, deep-learning, sequence-based, and multimodal approaches, and proposes asystem pipeline from frame acquisition to warning generation. Evaluation is framed around precision, recall, F1-score, false-alarm behaviour,detection delay, processing latency, and robustness across lighting, eyewear, head pose, and unseen drivers. Since no new model training or roadtrial is claimed, experimental metrics are presented as evaluation requirements; published figures remain attributed to their original studies. Theanalysis indicates that combining interpretable facial features with learned image representations and temporal evidence is a useful researchdirection, but reliable deployment requires subject-independent validation, transparent failure handling, privacy safeguards, and realistic testing.