## 3 Dataset
The experiment began with eye tracker calibration and 5 training videos, followed by viewing 28 videos in random order. After each video, participants decided on a take-control response, rated danger, rebuilt the scene, and rated task difficulty. For predicting the SA of every experiment, paper [1] shows that 14 measures are the most useful, so the same set of 14 measures were used in this paper, and they are listed and described in Table 2.  
In addition, participants were answering the following questions after every experiment:  
* **How many cars were in the video? **
* **What is the speed of every car** (faster than the ego-car, equal speed as the ego-car, slower than the ego-car)? * **What is the distance of each car from the ego-car? **  
For calculating the ground truth of SA for each video, the researchers in [1] normalized all three error scores inversely on a scale from zero to one and assigned them equal weights. This approach generated a global SA score ranging from zero (indicating the worst SA) to one (indicating perfect SA): \begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Description** \\ \hline Driving Frequency & Driving frequency in last 12 months (everyday driving = 1, 4–6 days/week = 2, 1–3 days/week = 3, 1 month to 1 week = 4, less than 1 month = 5, never = 6) \\ \hline Age & Number of years of the participant \\ \hline Years of driving & How long the participant has experience driving \\ \hline Video length & The length of recorded video that represents the driving simulation (1 s, 3 s, 6 s, 9 s, 12 s, 20 s) \\ \hline Correct decision & The right decision that should be made according to the situation in the driving simulation (no need to take over = 1, evade left = 2, evade right = 3, evade left or right = 4, brake only = 5) \\ \hline Decision made & The decision that the participant made after watching the video (no decision = 0, no need to take over = 1, evade left = 2, evade right = 3, evade left or right = 4, brake only = 5) \\ \hline Decision time & Time that participant took to decide \\ \hline Difficulty & Likert scale on “The situation was dangerous” (from completely disagree = 0 to completely agree = 100) \\ \hline Danger & Likert scale on “The rebuilding task was difficult” (from completely disagree = 0 to completely agree = 100) \\ \hline Back mirror usage & This parameter reflects how frequently the driver checks the rear-view mirror, an indicator of awareness. The system categorizes this activity into three levels: low (none), middle (infrequent), and high (frequent) \\ \hline Road awareness & It evaluates the driver’s attention to the road, crucial for safe driving. Observations range from low (minimal attention) to high (vigilant) \\ \hline Pupil mean diameter & Changes in pupil size can indicate stress or fatigue. The system interprets these changes across a spectrum from constricted (low) to dilated (high) \\ \hline Pupil change rate & This measures the speed of pupil dilation and constriction, which might signal sudden changes in attention or emotional state \\ \hline F mean & Average fixation duration for each participant in one video \\ \hline F max & Maximum value of fixation duration of the participant in one video \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Predictor variables used in this paper. * **Absolute error in the number of cars placed--measuring the difference between the number of cars placed and the true number of surrounding cars. **
* **Total speed error--indicating the normalized sum of distance errors between placed and true cars. **