## 5 Results  
In this section, we discuss the outcomes of our implemented ANN models, presenting their performance through confusion matrices, precision, recall, accuracy, and F1 score. The confusion matrix provides a detailed view of the system's performance, delineating the counts of True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN) for each class. Additionally, the other metrics utilized in our analysis are defined by the following Equations [39]:  
\[Precision=\frac{1}{N}\sum_{i=1}^{N}\frac{TP}{TP+FP} \tag{1}\]  
\[Recall=\frac{1}{N}\sum_{i=1}^{N}\frac{TP}{TP+FN} \tag{2}\]  
\[Accuracy=\frac{1}{N}\sum_{i=1}^{N}\frac{TP}{TP+TN+FP+FN} \tag{3}\]  
\[F1=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{4}\]  
where N is the number of classes. As the percentage of accurately anticipated positive instances among all expected positive instances, precision quantifies the accuracy of positive predictions. Recall measures the percentage of accurately anticipated positive events among all actual positive instances [40]. The F1 score is especially helpful when there is an imbalance between the classes because it integrates recall and precision into a single statistic, offering a balance between the two.  
The outcomes of the ANN models are illustrated without incorporating the dropout layers, then with the dropout, and finally with the bagging technique. Furthermore, SVM model with Gaussian Kernel is trained and tested alongside the ANN models using the same datasets, serving as a benchmark to evaluate the effectiveness of ANN approaches against traditional machine learning methods.  
The results reveal that in the predominant categories of normal driving style and low traffic conditions, all three ANN models outperformed SVM. However, for the less common class labels encompassing aggressive driving, medium, and high traffic, SVM demonstrated  
Figure 5: This figure shows the model training loss with epoch results for the deep neural network with bagging.  
superior performance compared to ANN model without dropout. Remarkably, integrating dropout layers boosted the accuracy of detecting low-labeled classes, although SVM retained a slight edge. Notably, the ANN model employing bagging exhibited the most resilient performance in predicting low-labeled classes. Figures 6 and 7 show the confusion matrix for all the ANN models and SVM model.  
The ANN models show higher precision values when compared to SVM. Among them, the ANN model with bagging stands out for its superior recall in predicting driving style and road conditions. This improvement in recall values implies a reduction in false negatives, especially within the low-labeled classes. Interestingly, the ANN model without dropout exhibits weaker recall scores in predicting driving style, underscoring the benefits of dropout and bagging in enhancing detection within imbalanced datasets.  
The F1 score, which combines precision and recall, offers a balanced evaluation of model performance, particularly in the presence of imbalanced classes. The results reveal that the ANN model with bagging achieves the highest F1 scores in both driving style and road condition predictions, with values of 0.85 and 0.98, respectively. In contrast, SVM exhibits significantly lower precision values compared to the ANN models, although it demonstrates competitive recall values compared to the ANN model without dropout. SVM also records the lowest accuracy and F1 score among the implemented algorithms, underscoring the superiority of the ANN model over SVM as a conventional machine learning technique. Tables 3 and 4 provide a detailed overview of the precision, recall, accuracy, and F1 score for the implemented models in predicting driving style and road traffic conditions.  
Figure 6: Driving style prediction results. (**a**) The confusion matrix using the ANN model without dropout. (**b**) The confusion matrix using ANN model with dropout. (**c**) The confusion matrix using the ANN with bagging. (**d**) The confusion matrix using SVM.