# Situational Awareness Assessment of Drivers Boosted by Eye-Tracking Metrics: A Literature Review
## 3 Results
### Experimental Environment: Naturalistic or Driving Simulation  
Among the diverse factors contributing to the potential variance in assessing SA in drivers based on eye tracking, the contextual environment within which the experiment was conducted assumes pronounced importance. In this review, studies were categorized into driving simulation and naturalistic driving. Of the total number of studies reviewed, 76% were simulated driving experiments, and 24% were naturalistic driving experiments. Table 3 lists the driving simulators or simulation software used in the studies. Of the studies that used a driving simulator, 94% were simulated driving scenarios comprising video-based laboratory experiments, and 6% were a mix of virtual reality (VR) for instance [44], augmented reality (AR), and driving simulator experiments [30; 56; 57]. For the simulations, the authors used different types of software to recreate simulated driving scenarios with videos in testing laboratories. Similarly, ref. [12] tested 32 simulated driving scenarios with six lengths for durations of 1-20 s using PreScan 8.0.0. The purpose of their study was to create a model that allowed real-time monitoring and predicting of SA in automated driving. It is significant that the study recommends conducting the study in high-fidelity driving simulators or naturalistic driving based on data collection in a low-fidelity setup [47]. Moreover, ref. [32] conducted a video-based laboratory experiment to evaluate the SA and its effects on working memory. In all, 18 traffic scenarios and three practice videos were used; the total duration was 7000 ms, and the scenarios were created by Vicom Editor. Dynamic traffic scenarios were designed, and participants were trained to memorize the information and interpret the situation. Nonetheless, the study suggests that further \begin{table}
\begin{tabular}{l l} \hline \hline
**Author** & **Driving Simulator or Simulation Software** \\ \hline
[12] & Prescan 8.0.0 \\
[32] & VicomEditor \\
[36] & AVSimulation \\
[50] & Lab CARRS-Q. QUT \\
[47] & National Advanced Driving Simulator—NADS, miniSimTM \\
[39] & STISIM Drive 3 software (Version 2.8) \\
[51] & Eagonomics lab of the University of Missouri-Columbia, OpenDS driving simulation \\
[52] & CARLA simulator \\
[53] & Vicom Editor® \\
[23] & Prescan 8.0.0 \\
[34] & IniSim (NADS) \\
[54] & Not present in the report \\
[55] & Not present in the report \\
[56] & Honda Research Institute, augmented reality (AR) head-up display (AR HUD) \\
[48] & TISIM Drive system \\
[30] & CAVE-like room \\
[57] & AR and pedestrian collision warnings \\
[58] & ST-Software platform \\
[49] & Not present in the report \\
[42] & Not present in the report \\
[46] & PreScan 7.0 \\
[24] & University of Leeds Driving Simulator, Jaguar S-type cab \\
[59] & MODATS \\
[60] & Motion system of the WIVW \\
[25] & Not present in the report \\
[43] & 3D Max software 2023 \\
[44] & A3 motion simulator \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Driving simulators or simulation software used in the studies. studies can be realized in a dynamic situation. Likewise, ref. [50] performed a high-fidelity simulator study in the Advanced Driving Simulator of the Center of Accident Research and Road Safety, Queensland (CARRS-Q, QUT). They evaluated three interventions, namely, uncertainty display, interruption of non-driving-related tasks (NDRTs), and a combination of both, to evaluate the interfaces that address the difficulty of missing the SA or being out-of-the-loop. Reference [47] used a medium-fidelity fixed-base driving simulator of the National Advanced Driving Simulator (NADS), miniSim(tm), to evaluate the relation of the pre-takeover state of drivers in the context of NDRTs and to evaluate how this state affects level 3 SA. The study highly recommends validating the results in an automated driving vehicle in real time in more naturalistic environments.