A Survey on State-of-the-Art Drowsiness Detection Techniques

Abstract

Drowsiness or fatigue is a major cause of road accidents and has significant implications for road safety. Several deadly accidents can be prevented if the drowsy drivers are warned in time. A variety of drowsiness detection methods exist that monitor the drivers' drowsiness state while driving and alarm the drivers if they are not concentrating on driving. The relevant features can be extracted from facial expressions such as yawning, eye closure, and head movements for inferring the level of drowsiness. The biological condition of the drivers' body, as well as vehicle behavior, is analyzed for driver drowsiness detection. This paper presents a comprehensive analysis of the existing methods of driver drowsiness detection and presents a detailed analysis of widely used classification techniques in this regard. First, in this paper, we classify the existing techniques into three categories: behavioral, vehicular, and physiological parameters-based techniques. Second, top supervised learning techniques used for drowsiness detection are reviewed. Third, the pros and cons and comparative study of the diverse method are discussed. In addition, the research frameworks are elaborated in diagrams for better understanding. In the end, overall research findings based on the extensive survey are concluded which will help young researchers for finding potential future work in the relevant field.

A Framework for Instantaneous Driver Drowsiness Detection Based on Improved HOG Features and Naïve Bayesian Classification

“…In this section, various experimental results are presented and discussed, which are aimed at verifying the superiority of the proposed framework for driver drowsiness detection over competing state-of-the-art approaches in the literature. According to previously reported approaches [ 32 , 33 , 34 ], there are very few public datasets currently available for comprehensive performance evaluations of different approaches for driver drowsiness detection, particularly those with driver attention information from real-world driving scenarios [ 35 ]. On the other hand, it is especially difficult and most dangerous to build a realistic dataset for driver drowsiness detection in real situations that can be used to train the proposed framework comprehensively.…”

HCF: A Hybrid CNN Framework for Behavior Detection of Distracted Drivers

“…Yan et al [22] proposed a CNN-based model that used unsupervised learning and transfer learning to classify activities of normal driving, answering a cellphone call, eating, and smoking. Yan et al [23] proposed using a hierarchical classification approach and treating driving behavior in spatiotemporal reference frame terms rather than as a static image. The overall prediction accuracy for this method reached 89.62%.…”

Drowsiness, Fatigue and Poor Sleep’s Causes and Detection: A Comprehensive Study

“…However, in some cases, among which, obviously, is car driving, drowsiness/sleepiness leads to devastating results. A recently published article [133] have summarized some of methods frequently used for drowsiness detection and possible applications with certain classifiers. In this article we have targeted brain signals and their respective response changes during drowsy feeling in addition to separately analyzing each method and corresponding pros and cons.…”