# An Image Processing-Based Method to Analyze Driver Visual Behavior Using Eye-Tracker Data
## 1 Introduction  
One of the most important contributing factors to traffic crashes is driver behavior, which has been the subject of numerous investigations. This activity may now be accomplished while considering various potential danger conditions in a perfectly safe driving environment, thanks to driving simulators. Examining drivers' eye movements is critical as a sign of their focus and readiness to operate a motor vehicle. The number of vehicles is continuously rising, and road crashes have increased in frequency and severity, becoming one of the world's leading causes of mortality [1]. Despite advances in improving the regulation of important risk factors, road crashes remain a primary cause of death and serious injury in many developed and developing nations. According to the World Health Organization (2018) [2], road traffic crashes cost the lives of over 1.35 million people globally each year. Road traffic injuries are the eighth biggest cause of mortality for people of all ages. Human error is a common cause of traffic crashes, which becomes more likely as a driver's cognitive ability deteriorates [1]. According to the European Commission's road safety manual (European Commission) [3], motor vehicle crashes are caused by a combination of human, vehicle, and environmental factors. Mental workload (MWL) is one indicator that may be used to evaluate human-machine interaction, and it is an essential design paradigm for investigating human-technology interactions [4]. Pupil diameter change [5], blink duration [6], horizontal gaze dispersion [7], blink frequency [8], gaze duration, saccade number [9], standard deviation (SD) of horizontal eyeball rotation [8], and variations in gaze points [10] have been proposed as powerful indicators of MWL among the numerous eye-tracking measurements. Moreover, driver distraction is \"the diversion of attention away from activities vital to safe driving and toward a competing activity\" [11]. It is also becoming recognized as acontributing cause of many vehicle crashes [12; 13; 14; 15]. Eye tracking technology is valuable for measuring mental workload and driver distraction by examining visual attention and gaze behavior. Changes in gaze behavior have been observed to reflect varying cognitive demands, offering insights into the mental demands of a task. Similarly, longer fixation durations and less frequent fixations have been observed in distracted drivers performing secondary tasks. These changes can be used to develop objective measures of driver distraction and to design safer human-machine interfaces [16; 17]. According to data analysis from the 100-car naturalistic driving study of adult drivers, the probability of road crashes or near-crashes rose with eye glances away from the forward roadway lasting more than two seconds. The data from this naturalistic investigation were subjected to further analysis. The results showed a correlation between the likelihood of a crash and the total or cumulative duration of eye movements away from the forward roadway [18]. In another study, using advanced gravitational force detection techniques, 42 new teenage drivers had equipment installed in their cars to monitor crashes and near-crash events (CNCs). Video footage from the six seconds before each CNC and randomly selected non-CNC road segments were coded to determine the length of eye glances off the front roadway and the existence of secondary task engagement. A more reliable indicator of a distraction than the single most prolonged glance was the total duration the eyes were off the forward roadway. Generally, the longer the distraction, regardless of activity, the greater the CNC risk [19]. To enhance road safety, it is essential to investigate drivers' behavior and identify their weaknesses. This will help to identify critical situations when drivers are distracted by secondary tasks or gaze-related activities. This study aims to develop a method for analyzing gaze and determining where drivers look at the road simulation scenario rather than just on the simulation's screen.