## 7 Conclusions  
This study presents a fuzzy logic system for enhancing situational awareness (SA) and decision-making in automated driving scenarios. Our results demonstrate that the proposed model achieves performance comparable to advanced machine learning (ML) methods, offering several distinct advantages. Key innovative points and their implications are listed below. **Performance and Accuracy**:  
* The fuzzy logic model, particularly the 3-5 configuration, achieves an RMSE of 0.126 and an MSE of 0.101, which are competitive with the best-performing ML models (e.g., LightGBM with an RMSE of 0.108). * This highlights the model's ability to deliver accurate predictions of situational awareness, comparable to state-of-the-art machine learning techniques. **Efficiency and Real-World Applicability**:  
* With an average execution time of approximately 4 ms, the fuzzy logic model is efficient enough for real-time applications, ensuring it can be integrated into practical automated driving systems without significant computational overhead. * The simplicity and speed of the fuzzy logic computations make it a viable alternative to more resource-intensive ML models, particularly in scenarios with limited data and computational resources. **Interpretability and Generalization**:  
* Unlike many ML models, the fuzzy logic system offers enhanced interpretability, allowing for easier understanding and fine-tuning of the decision-making process. * The cascade-type fuzzy logic model demonstrates robustness against overfitting and resilience to data variations, ensuring reliable performance across different conditions. **Robustness and Data Efficiency**:  
* Fuzzy logic systems are effective with smaller datasets, addressing a common limitation of many ML algorithms that require large amounts of training data. * This makes the fuzzy logic model particularly suitable for applications where data is scarce or expensive to obtain. In summary, this research underscores the potential of fuzzy logic systems as a practical and efficient alternative to traditional machine learning approaches for SA prediction in automated driving. The proposed model matches the predictive accuracy of leading ML methods, offers real-time applicability, and brings additional benefits in terms of interpretability, and robustness. Future work will focus on integrating our fuzzy logic components into hybrid systems and exploring further optimizations to enhance performance. **Author Contributions**: Conceptualization, G.F., D.S., I.T. and D.T. ; methodology, G.F., D.S., I.T. and D.T. ; software, G.F., D.S., I.T. and D.T. ; validation, G.F., D.S., I.T. and D.T. ; formal analysis, G.F., D.S., I.T. and D.T. ; investigation, G.F., D.S., I.T. and D.T. ; resources, G.F., D.S., I.T. and D.T. ; data curation, G.F., D.S., I.T. and D.T. ; writing--original draft preparation, G.F., D.S., I.T. and D.T. ; writing--review and editing, G.F., D.S., I.T. and D.T. ; visualization, G.F., D.S., I.T. and D.T. ; supervision, G.F., D.S., I.T. and D.T. ; project administration, G.F., D.S., I.T. and D.T. ; funding acquisition, G.F., D.S., I.T. and D.T. All authors have read and agreed to the published version of the manuscript. **Funding**: This work is supported by the EU Commission AWARE2 ALL project, which has received funding from the Horizon Europe research and innovation programme under grant agreement No. 101076868. **Institutional Review Board Statement**: Not applicable. **Informed Consent Statement**: All participants provided written informed consent. The research was approved by the TU Delft Human Research Ethics Committee [24]. Data Availability Statement: Dataset is downloaded from the work in [24]. The videos, raw data, and a MATLAB script that reproduces all results in the paper [24] can be found here: [[https://doi.org/10.4121/uuid:al1f0](https://doi.org/10.4121/uuid:al1f0) ad0f-0033-431f-8a90-0 efcae37 dd6a]([https://doi.org/10.4121/uuid:al1f0](https://doi.org/10.4121/uuid:al1f0) ad0f-0033-431f-8a90-0 efcae37 dd6a) (accessed on 5 March 2024).