Real-time driver's drowsiness detection using Ml

Information Technology and ML

Significant advancements in computing technologies and artificial intelligence over the past decade have greatly improved driver monitoring systems. Many experimental studies have gathered actual driver drowsiness data, employing various AI algorithms and combinations of features to enhance system performance in real-time. This paper provides a comprehensive review of driver drowsiness detection systems developed in the last ten years. It examines recent methods that use different indicators to monitor and detect drowsiness, categorizing each system based on the type of data utilized. Detailed descriptions of the features, classification algorithms, and datasets used by these systems are also provided. Additionally, the paper evaluates these systems in terms of classification accuracy, sensitivity, and precision. Challenges in the field of driver drowsiness detection are discussed, along with an analysis of the practicality and reliability of each system type. Lastly, future trends in this area are outlined.

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