## 3 Dataset  
Based on the detailed insights and methodologies derived from paper [1], we continued our exploration by accessing the same dataset utilized in this study [24]. The database  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Method** & **Data Types** & **Models Used** & **Key Findings** & **Limitations** \\ \hline \multirow{3}{*}{[5]} & Cognitive Workload & Pupil Diameter, & KNN, Naive Bayes, & Pupil diameter & \multirow{3}{*}{Limited to simulated} \\  & Assessment & EEG & Random Forest, & & \\  & & EEG & SVM, neural & metric, eye tracking & \\  & & network & sufficiency & & \\
[6] & Mental Load & Heart Rate, Pupil & KNN, LDA, & Improved prediction & Non-intrusive sensors, \\  & Estimation & Dilation & AdaBoost & accuracy with & may not capture all \\  & ARecNet for & EEG, Eye & & & & \\  & Workload & Movements, & Cross-Attention & & & \\  & Recognition & Vehicle States & &  
standardized SA scores between zero and one by integrating three performance metrics (placement, distance, and speed estimation of vehicles relative to the ego-vehicle) across simulated driving scenarios. The database is described in this section. Forty participants took part in an experiment for an MSc-level course at Delft University of Technology, a common practice in automated driving research. After excluding participants who did not adhere to instructions or had insufficient driving experience, 32 participants were included in the analysis. The majority were male, aged between 22 and 29 years, with varying levels of driving frequency and experience. Half of the participants were Dutch, while the others were mostly from China and India [24]. The videos range from 1 to 20 s in length. Each video presents a three-lane highway, with the ego-car positioned in the middle lane. All videos feature six cars including the ego-car, except for the 20 s videos which include seven cars. The study describes a simulation involving an ego-car and surrounding cars in three lanes, each with varying speeds (80, 100, and 120 km/h). The simulation aimed to mimic real-world speed variations encountered on roads, with cars randomly selected by color and model. By allowing cars driving at 120 km/h to use all lanes, the design aimed to eliminate the participants' expectations regarding overtaking regulations in different countries. The simulation did not involve any lane changes, highlighting the speed and positional variations that may occur in real-world driving scenarios. One driving environment is presented in Figure 1. A front view from the ego-car is given and the view from three mirrors. This environment consists of five cars. The ego-vehicle is in the middle lane, two vehicles are in front of the ego-vehicle and two are behind. In front of the ego-vehicle, one vehicle is in the right lane and one vehicle is in the left lane. Also, there are two more vehicles behind the ego-vehicle, one in the middle lane and one in the left lane, which can be seen in mirrors. This video is called a non-hazard video because there is no car in front of the ego-car in the same lane. The videos were simulating driving scenarios, including non-hazard situations where all cars drove at a constant speed, and hazard situations where a collision needed to be avoided. The hazard situations involved a car decelerating in front of the ego-car, with variations in lane changes and braking maneuvers to avoid collisions. Additionally,  
Figure 1: The driving environment. In this non-hazard video, in front of the ego-vehicle, one car is in the left lane and one car is in the right lane, and there are also two cars behind the ego-vehicle. In total, there are four surrounding cars with no cars in front of the ego-car in the same lane. training videos were created with surrounding cars but no hazards. In total, 33 videos were produced, including non-hazard, hazard, and training scenarios, each varying in length and complexity. Participants signed a consent form, completed a questionnaire, and adjusted their seating.