# Situational Awareness Assessment of Drivers Boosted by Eye-Tracking Metrics: A Literature Review
## 3 Results
### Experimental Environment: Naturalistic or Driving Simulation
Further, reference [51] analyzed the driver's emotional state and physiological feelings using OpenDS driving simulation software in the Ergonomics lab of the University of Missouri (Columbia). The simulation can contribute to advanced driver assistance systems in the future; nevertheless, it remains subjective and does not capture the feelings induced in the driver by simulated surrounding road environments in real time. Research conducted in authentic, real-world environments is regarded optimal for validating study variables. While it can be posited that such an approach incurs high costs and necessitates more extensive investment in the formulation of ethical, safety, and data collection protocols, the advantages are notable. The advantages include the capacity to capture the nuanced dynamics of the environment and the real-time analysis of driver emotions, reactions, attentional patterns, and perceptions of the contextual elements. In doing so, this approach counters the potential subjectivity inherent in simulations, while also examining the prospective trajectory of research within the majority of studies included in this review. Amid the array of naturalistic studies incorporated within the scope of this review, reference [18] evaluated SA in diverse industrial vehicles in naturalistic driving scenarios. The vehicles considered include a forestry harvester, a wheel loader, an excavator, a dump truck, a wheeled excavator, and a mobile crane. The study evaluates how eye tracking can be used to evaluate the attention of operators. The major activities evaluated and measured were cutting, unloading, reversing, and lifting. These activities were evaluated using cameras and sensors to range visualization around the vehicle. The study highlights the need for more studies focused on the industrial environment since such studies are rare; there are considerable advantages of being able to use real data without interfering with the work being carried out. Furthermore, ref. [37] studied the visual load and the associated loss of SA by examining the takeover and driving NDRT performance. A relevant conclusion of the study validates that in the real world, drivers' responses are primarily instinctive and swifter than those observed in simulators, whereas physical and cognitive loads may not have a significant impact on reaction times, as suggested in some simulated studies. On the other hand, ref. [27] evaluated the SA and perceived sleepiness of 10 professional truck drivers who worked with a two-vehicle platooning system. One of the discussions deals with the number of participants since, although the experiment was conducted under naturalistic conditions, it also dealt with a smaller sample compared to laboratory experiments, and the eye-tracking measurement was less reliable than in laboratory settings because of variable light conditions. In a notable study by [61], emphasis was placed on the examination of natural gaze patterns within street intersections. The primary objective was to glean insights into the capacity of gaze behavior to serve as an indicator of driver awareness, thereby facilitating predictions of SA. They examined the potential utility of gaze metrics in relation to distinct road users while also highlighting the significance of obtaining pertinent information of the entirety of the visual field. The study underscored the challenges associated with recreating the dynamic real-world environment and authentic AOIs within experimental methodologies. Likewise, ref. [33] found that the relevance of data collection from experiments in real environments relapses; for instance, in the design of future advanced driver assistance systems (ADAS), if the SA of the driver is known, the ADAS will administer critical information to the driver in risky situations or when required. Thus, the driver's SA can be regained more rapidly and confidently.