An Efficient Drowsiness Detection Framework for Improving Driver Safety Through Supervised Learning Models

Nowadays, we live in the smart mobility era in which vehicles are equipped with small sensing devices to collect various road information. With such sensors, we are able to provide an overview of what is happening on the road and offer an efficient solution for transport problems such as congestion, accidents, avoiding traffic lights, fuel consumption, etc. Particularly, driver drowsiness is one of the most important problems that transportation systems face and mostly leads to severe accidents, injuries, and deaths. In order to overcome such a problem, a set of sensor devices has been integrated into vehicles to monitor driver and driving behaviors, and then to evaluate the driver’s situation, e.g., drowsy or awake. Unfortunately, most of the proposed drowsiness detection techniques are dedicated to analyzing one behavior type, but not both, which may affect the accuracy rate of the detection. In this paper, we propose an efficient drowsiness detection framework (RDDF) that may analyze one behavior or be adapted to both of them in order to increase the accuracy of drowsiness detection. Mainly, RDDF periodically monitors the driver and driving behaviors, extracts important patterns, and then uses and compares a set of supervised learning models to detect drowsy drivers. After that, RDDF proposes a modified version of the K-nearest neighbors (KNN) model called Jaccard-KNN (JKNN) that increases drowsiness detection accuracy and overcomes several challenges imposed by traditional models. The proposed framework has been preliminarily validated through real sensor data, and we show the effectiveness of our framework in detecting real-time drowsy drivers with an accuracy rate of up to 99%.