## 4 Conclusions  
Throughout this paper, we have undertaken a comprehensive exploration of ensemble learning techniques applied to vehicular data, aiming to predict driving behavior and road conditions. The experiments conducted have demonstrated the efficacy of utilizing a variety of machine learning models, such as random forest, decision tree, K-nearest neighbors, and voting classifiers, in making accurate predictions based on vehicular sensor data. Our findings reveal that the random forest classifier consistently delivers superior performance across multiple attributes, including driving style, road surface, and traffic conditions. It achieves high accuracy, sensitivity, and specificity, showcasing its robustness as a predictive tool in the automotive domain. The ensemble voting classifier also exhibits strong predictive capabilities, reinforcing the idea that combining different models can lead to improved performance over individual classifiers. The tree interpreter tool highlights the importance of explainable AI by offering valuable insights into each feature's contribution to class prediction. This not only enhances trust in the models but also paves the way for further refinement and understanding of the driving environment. In summary, the models we have evaluated offer promising approaches for real-time vehicular data analysis, with potential applications in intelligent transportation systems, autonomous vehicle guidance, and driver assistance technologies. The ability to accurately predict driving conditions and behavior can lead to safer, more efficient, and intelligent mobility solutions. Future work will include a comprehensive comparison with other ensemble learning techniques, such as boosting, bagging, and stacking methods. This will help to highlight the distinctive performance and characteristics of our approach in various applications. We aim to demonstrate not only the accuracy but also the computational efficiency and scalability of our method compared to these alternatives. We acknowledge the importance of interpretability in machine learning models, especially in domains where decision-making is critical. Therefore, we plan to expand our interpretability analysis using additional case studies that cover a broader range of scenarios. We will also explore other interpretable tools and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Area** & **Model** & **Accuracy** \\ \hline
[50] & Driving Style & CNN-LSTM & 93.81\% \\ \hline Our work & Driving Style & Ensemble Soft Voting & 93.89\% \\ \hline
[51] & Road Surface & CNN & 97.50\% \\ \hline Our work & Road Surface & Ensemble Soft Voting & 97.26\% \\ \hline
[52] & Traffic & Random Forest & 81\% \\ \hline
[41] & Traffic & Naive Bayes & 98.7\% \\ \hline Our work & Traffic & Ensemble Soft Voting & 97.14\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison with existing works. methodologies, such as LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (Shapley Additive Explanations), to provide deeper insights into our moderators' decision-making mechanisms. To address the adaptability and generalization of our model, we will undertake experiments with a more diverse dataset, encompassing a variety of road types and traffic conditions. Cross-environmental testing will be a focal point, designed to assess our model's robustness in different scenarios. The goal is to verify the model's performance in an array of settings, thereby ensuring its practical applicability in real-world situations. Additionally, in our subsequent research endeavors, we plan to delve into the integration of our models within real-time systems, assessing the potential benefits and challenges. The exploration of additional features will also be prioritized, with the intention of enhancing the model's predictive capabilities. Moreover, our commitment to improving the explain ability of AI systems remains steadfast, particularly as it pertains to their application in dynamic and unpredictable environments.