###### Abstract  
This paper investigates the application of fuzzy logic to enhance situational awareness in Advanced Driver Assistance Systems (ADAS). Situational awareness is critical for drivers to respond appropriately to dynamic driving scenarios. As car automation increases, monitoring situational awareness ensures that drivers can effectively take control of the vehicle when needed. Our study explores whether fuzzy logic can accurately assess situational awareness using a set of 14 critical predictors categorized into time decision, criticality, eye-related metrics, and driver experience. We based our work on prior research that used machine learning (ML) models to achieve high accuracy. Our proposed fuzzy logic system aims to match the predictive accuracy of ML models while providing additional benefits in terms of interpretability and robustness. This approach emphasizes a fresh perspective on situational awareness within ADAS, potentially improving safety and efficiency in real-world driving scenarios. autonomous vehicles; driver monitoring; human-machine interface; cognitive workload; machine learning; fuzzy logic; situational awareness; decision-making in autonomous driving +
Footnote †: journal: Medical  
for the training phase. These models are computationally intensive, requiring substantial processing power and time, which can limit their real-time applicability in critical driving situations. Fuzzy logic, on the other hand, offers a flexible and efficient alternative. It is well-suited for handling uncertainty and imprecision, making it an ideal choice for modeling human behavior and environmental factors in driving scenarios. Fuzzy logic systems can potentially process information more quickly than traditional ML models, providing faster responses in real-time applications. Additionally, fuzzy logic models are more interpretable, allowing for easier understanding and adjustment of the decision-making rules based on expert knowledge. This study aims to investigate whether fuzzy logic can be effectively applied to assess situational awareness, using a database that has been previously explored with ML approaches. By comparing the performance of fuzzy logic with established ML models, this research seeks to highlight the potential advantages of fuzzy logic in terms of its robustness. The research objectives are:  
* **Evaluate the Feasibility of Fuzzy Logic for Situational Awareness**: This study explores the use of fuzzy logic to model situational awareness based on 14 critical predictors categorized into four groups: time-decision, criticality, eye-related metrics, and driver experience. * **Compare with Machine Learning Models**: The performance of the fuzzy logic model is benchmarked against ML models, particularly focusing on predictive accuracy (RMSE and MAE) and computational efficiency. The contributions of this research are:
* **Fuzzy Logic Model in Predicting SA**: The study introduces a fuzzy logic model tailored to predict situational awareness, providing a fresh perspective within the ADAS domain. * **Performance Comparison**: It offers a comparison between fuzzy logic and ML models, highlighting better interpretability and comparable accuracy. * **Real-World Implications:** The findings suggest that the fuzzy logic system can enhance the safety and efficiency of ADAS by improving real-time situational awareness prediction. In summary, this study will demonstrate that the fuzzy logic system achieves prediction accuracy comparable to advanced ML methods, offers real-time applicability, and enhances interpretability and robustness, particularly in data-limited scenarios. The remainder of this paper is organized as follows: Section 2 reviews related work, providing an overview of existing approaches to situational awareness prediction in ADAS. Section 3 details the dataset and all features that are measured. Section 4 outlines the methodology used in this study, including the development of the fuzzy logic model.