Abstract
Several hypovigilance detection systems (HDx) were developed to avoid road-side accidents due to driver fatigue. They have suffered from several limitations. Notably many of these are focused on center-head position to define an area of interest (often referred to as PERCLOS (percentage eye closure)) without considering the face occlusion problem, light illumination, and suffer poor response time. These HDx systems mostly depend on image processing, vision-based, and multisensor-based features. To address these problems, the author utilized vision, sensors, environmental, and vehicular-based features that integrated together by fusion to predict multistage of HDx. Lately, few studies have utilized the combination of multimodal features and deep learning (DL) architectures. Those multimodal-based features (M-HDx) were feasible to predict stages of driver fatigue (multi-stage). However, there is a need to critically measure the performance of these M-HDx by carrying out a comparative analysis to recognize multi-stage of fatigue in terms of hardware-based benchmarks. Moreover, it is important to evaluate the M-HDx systems using different features-set with respect to traditional and advanced machine learning techniques. Therefore, the primary aim of this work is in algorithm and feature modeling, then compare the advantages and differences with other work. In this paper, a different study is conducted compare to state-of-the-art survey articles by statistically measuring the performance. After experiments on M-HDx systems, this paper concludes that there is still a research gap to real-time development of multistage M-HDx systems. In the end, the paper summarizes the directions, challenges, and applications in the development of HDx systems to assist other researchers for further research.