# Predictive Analysis of Vehicular Lane Changes: An Integrated LSTM Approach
## 3 Results and Analysis
### Maneuver Classification
#### 3.1.2 Support Vector Machine (SVM)  
When initiating our investigation into support vector machines (SVMs), we commenced with a comprehensive training phase that incorporated all available features. These were input into each of four distinct SVM models for evaluation. The subsequent testing phase relied on a separate dataset to determine the test accuracy for each model. A model registering high values for both training and testing accuracies was subsequently considered superior in performance.  
In the next phase, training was narrowed to incorporate only those features deemed most significant. The performance metrics--specifically the training and testing accuracies--were utilized as yardsticks to gauge the efficacy of the models, akin to the approach in the preliminary phase.  
An in-depth analysis of these preliminary SVM experiments ensued, with Table 5 elucidating the training and testing accuracies for each model. Remarkably, the discrepancies between the training and testing accuracies across models were minimal, suggesting consistent performance. The support vector classification (SVC) model with a sigmoid kernel was found to underperform, yielding the least accurate predictions. Models using both polynomial and radial basis function (RBF) kernels secured accuracies around 81% when trained on all features. However, this accuracy escalated dramatically to nearly 98% following feature optimization. The linear SVC model stood out, maintaining consistently high accuracies throughout both experimental phases.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Best Training Set**} & \multicolumn{2}{c}{**Predicted Labels**} & \multicolumn{3}{c}{**Predicted Labels**} \\ \cline{2-7}  & \(-\)**1** & **+1** & & & \(-\)**1** & **+1** \\ \hline True & \(-\)1 & 38,611 & 93 & True & \(-\)1 & 5517 & 9 \\ Labels & +1 & 93 & 8855 & Labels & +1 & 8 & 1273 \\ \hline \end{tabular}
\end{table}
Table 4: Confusion matrices of the top-performing regression model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**All Features**} & \multicolumn{2}{c}{**Selected Features**} \\ \cline{2-5}  & **Training (\%)** & **Testing (\%)** & **Training (\%)** & **Testing (\%)** \\ \hline Logistic Regression & 99.63 & 99.63 & 99.63 & 99.63 \\ SGD Regression & 99.59 & 99.59 & 99.61 & 99.59 \\ Ridge Regression & 97.60 & 97.54 & / & / \\ Lasso Regression & 97.60 & 97.54 & / & / \\ LinearSVC & 99.18 & 99.17 & 99.63 & 99.63 \\ \hline \end{tabular}
\end{table}
Table 3: Accuracy of regression models.  
Given its exceptional performance, the linear SVC model was identified as the best-performing model and therefore earmarked for more detailed evaluations in subsequent phases of the study. As we transitioned to a deeper evaluation of the linear SVC model, we employed K-fold cross-validation, a universally endorsed methodology for mitigating overfitting and enhancing model generalization. Specifically, the data were partitioned into eight subsets. Each subset alternately served as the validation set, while the remaining subsets formed the training dataset. The model that achieved the highest validation accuracy was deemed optimal for the study.  
Table 6 presents the confusion matrices for both the training and validation sets corresponding to the top-performing SVM model. Across the eightfold validations, the peak training accuracy reached 99.61%, and the peak validation accuracy climbed to 99.75%. Importantly, these figures were closely aligned with the consistent 99.63% accuracy observed when the model was trained and tested on the full dataset.  
Concluding our comprehensive SVM analysis, we shifted our attention to exploring the potential of multilayer perceptrons as another instrumental tool in our research arsenal.