## 2 Related Work  
Smart cars are gaining prominence across both educational and business communities [2]. The study of these vehicles intersects several fields including transportation, psychological and medical sciences, vehicle engineering, digital technologies, energy, and safety. Even as smart cars evolve towards complete automation, several hurdles remain, such as unpredictable environmental and traffic conditions, varied experiences of road users, and the intricacies of interaction between drivers and their vehicles [3]. Achieving full driving automation necessitates a more comprehensive comprehension of how drivers behave, which is critical for enhancing the efficiency and functionality of these vehicles. To provide a comprehensive overview of related work, this section summarizes key studies and analyzes their methodologies, findings, and limitations. Table 1 compares different approaches to highlight both their differences and similarities. Cognitive or mental workload describes how much of one's cognitive capacity is employed when carrying out a task [4]. This element is key in determining human performance, with higher workloads typically leading to reduced effectiveness. Numerous research efforts have focused on analyzing cognitive patterns and mental demands placed on drivers during interactions with vehicle systems while driving. Several studies have addressed cognitive workload and driver interaction in smart cars using various methods and machine learning models:  
* **Cognitive Workload Assessment via Pupil Diameter and EEG Data**: The paper [5] explores assessing cognitive workload through pupil diameter and EEG data in a simulated driving task. The study compares five machine learning models (KNN, Naive Bayes, Random Forest, SVM, and neural network) and finds that pupil diameter changes (APCPS) are a reliable metric for differentiating cognitive workload levels. The practical implication is the sufficiency of eye tracking over EEG for real-time applications in human-machine interactions. * **Estimating Mental and Perceptual Loads Using Machine Learning Frameworks**: Study [6] employs a framework using non-intrusive sensors (heart rate variability and pupil dilation) to estimate cognitive demands. The ensemble of models (KNN, LDA, and AdaBoost) improves prediction accuracy, highlighting the robustness of the approach in reducing cognitive strain and enhancing safety. * **Attention-enabled Recognition Network (ARecNet)**: The study [7] presents ARecNet, integrating EEG signals, eye movements, and vehicle states via HyperLSTM with a cross-attention mechanism. This model adapts to driving conditions and driver behavior, showing improved real-time classification of cognitive workload levels. * **Video-based Drowsiness Detection**: Research [8] introduces a deep-learning framework using facial videos, achieving high accuracy in drowsiness detection. The study also explores other methods such as EEG and eye gaze for evaluating cognitive workload. * **Environmental Factors and Psychophysiological Metrics**: Various studies [9; 10; 11; 12] examine the impact of environmental factors on driver stress and workload, utilizing metrics such as gaze, heart rate, and EEG signals. These studies emphasize the importance of monitoring driver attention in semi-automated vehicles. * **Shared Control and Decision-Making Models**: Studies [13; 14] focus on shared control algorithms and decision-making models integrating fuzzy logic with driver behavior analysis to enhance road safety and reduce accident rates under varying conditions. * **Human-Vehicle Interaction (HVI) for Situation Awareness (SA)**: Research [15; 16; 17; 18; 19; 20] investigates HVI mechanisms and interfaces designed to maintain driver awareness and readiness in automated vehicles. These studies highlight the role of multimodal alerts and adaptive interfaces in enhancing situational awareness and trust in automated systems.