Abstract
Tracking drivers’ eyes and gazes is a topic of great interest in the research of advanced driving assistance systems (ADAS). It is especially a matter of serious discussion among the road safety researchers’ community, as visual distraction is considered among the major causes of road accidents. In this paper, techniques for eye and gaze tracking are first comprehensively reviewed while discussing their major categories. The advantages and limitations of each category are explained with respect to their requirements and practical uses. In another section of the paper, the applications of eyes and gaze tracking systems in ADAS are discussed. The process of acquisition of driver’s eyes and gaze data and the algorithms used to process this data are explained. It is explained how the data related to a driver’s eyes and gaze can be used in ADAS to reduce the losses associated with road accidents occurring due to visual distraction of the driver. A discussion on the required features of current and future eye and gaze trackers is also presented.
Highlights
Reaction time is calculated by numerous measures, such as brake reaction time (BRT), detection response time (DRT), and peripheral detection time (PDT)
This paper reviewed eye and gaze tracking systems—their models and techniques, the classification of techniques, and their advantages and shortcomings
Their application in advanced driving assistance systems (ADAS) for safe and comfortable driving has been discussed in detail
Summary
The human eyes, a beautiful and interactive organ in the human body, have unique physical, photometric, and motion characteristics. The intention of this paper is to benefit researchers by offering a comprehensive framework for Organization’s reports [20,21,22], every year approximately 1–1.25 million people die and 20–50 million a basic understanding of eye and gaze tracking and their applications in ADAS. The major categories of these models and studies (e.g., [23]), it is hoped that the amount of road accidents (related to visual distraction) will be techniques, with emphasis on the literature in which these techniques were initially proposed, and reduced by 10–20% due to facial monitoring feature of ADAS Their respective benefits and limitations, are discussed. Documented in [24]. of driving performance measures and statistics are well-documented in [24]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
