# An Image Processing-Based Method to Analyze Driver Visual Behavior Using Eye-Tracker Data
## 2 Literature Review
[PERSON] (2019) [33] assessed the gaze behavior during actual trips by test subjects utilizing a measurement vehicle equipped with an exact \"Smart Eye Pro\" remote eye-tracking system using corneal reflection. The researchers plotted the fixation distributions in straight portions and the right and left curves. The study concludes that driver stress is higher in bends than on straight stretches, resulting in increased attention. Specific patterns distinguish the gaze behavior on the left and right horizontal curves. On straight lines, the driver's sight is nearly uniformly spread throughout the whole width of the road. Compared to straight stretches, a reduced gaze dispersion might be achieved when driving through bends. Curves, on the other hand, have a more intense quest for information. The study revealed that horizontal curves cause higher eye stress and concentration than straight segments. Due to their inexperience and limited field of vision, inexperienced drivers are more likely to misunderstand the information they gather while driving. The results showed that, compared to experienced drivers, novice drivers displayed a higher dwell time on traffic signals, pedestrians, and passing cars, as well as a longer fixation duration on the dashboard and navigation system [34]. Previous studies have revealed that beginner and expert drivers differ in various ways. In general, novice drivers have less developed vehicle control abilities and fewer spare attentional capacities than expert drivers [35; 36]. For instance, crash rates are high in the first few months after obtaining a driver's license and decline as drivers gain experience [37; 38]. Furthermore, rookie drivers have a worse capacity to perceive and predict traffic hazards than experienced drivers [39; 40]. Additionally, inexperienced drivers tend to fixate on the local area more frequently, depend less on peripheral vision for vehicle control, and adjust their visual search less successfully to the environmental conditions [41; 42; 43; 44]. The research by [PERSON] et al. (2015) [45] examined how 52 inexperienced drivers' driving abilities and gaze patterns changed as they gained experience in the simulator. The participants drove as close as possible to the middle of the right lane over four sessions of six to eight minutes each on a rural road with several 90-degree turns. The eye-tracking data revealed that participants stretched their visual search more and reduced gaze tunneling. Gaze tunneling involves focusing on a single point instead of scanning the surroundings and happens when drivers are stressed or engaged in tasks such as navigating or reading signs. This behavior is dangerous, as it can lead to missed hazards and a higher likelihood of collisions. In the study by [PERSON] (2019) [33], the researchers used the parameter \"scan route length\" to measure gaze tunneling by determining the length of the driver's gaze path. Moreover, the results show that from the first to the fourth session, the standard deviation of lateral position (SDLP) and steering activity dramatically reduced, gaze variation rose, and self-reported workload decreased. [PERSON] et al. (2004) [46] presented a vision-based real-time driver weariness monitoring system for safe driving. Image processing methods, such as the HSI color model, were employed in this study to detect faces in input photos, and the Sobel edge operator was used to estimate eye positions and produce eye images as dynamic templates for eye tracking. In the study, the system's eye recognition and tracking had an average accuracy rate of 99.1%, correctly detecting all instances of human-marked fatigue. Regarding identifying driver fatigue, the system has an accuracy rate of up to 88.9%. [PERSON] and [PERSON] (2014) [47] employed image processing algorithms to establish a vision-based system for categorizing hand motions that uses a combined RGB and depth descriptor. The method was being investigated for use in vehicles for human-machine interaction.