## 5 Results
### Classification Algorithms for DUI
#### 5.3.4 Comparison of the Models  
When comparing the models, their performance across different scenarios was evaluated. In terms of training accuracy, all models show very high performance, reaching up to 100%. While XGBoost and random forest consistently achieved close to 100%, logistic  
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Data** & **Scenario** & **Accuracy\_Train** & **Accuracy\_Test** & **F1\_Value** & **Precision** & **Recall** \\ \hline Vehicle & City & 100.00 & 56.67 & 56.67 & 56.67 & 56.67 \\ \hline Camera & City & 100.00 & 60.00 & 62.50 & 58.82 & 66.67 \\ \hline Fusion\_Data & City & 100.00 & 56.67 & 58.06 & 56.25 & 60.00 \\ \hline Fusion\_Interpretation & City & 100.00 & 63.33 & 63.33 & 63.33 & 63.33 \\ \hline Vehicle & Rural & 100.00 & 51.67 & 50.85 & 51.72 & 50.00 \\ \hline Camera & Rural & 99.76 & 58.33 & 54.55 & 60.00 & 50.00 \\ \hline Fusion\_Data & Rural & 100.00 & 63.33 & 60.71 & 65.38 & 56.67 \\ \hline Fusion\_Interpretation & Rural & 100.00 & 55.00 & 50.91 & 56.00 & 46.67 \\ \hline Vehicle & Highway & 100.00 & 41.67 & 46.15 & 42.86 & 50.00 \\ \hline Camera & Highway & 99.88 & 41.67 & 40.68 & 41.38 & 40.00 \\ \hline Fusion\_Data & Highway & 100.00 & 48.33 & 49.18 & 48.39 & 50.00 \\ \hline Fusion\_Interpretation & Highway & 100.00 & 45.00 & 47.62 & 45.45 & 50.00 \\ \hline Vehicle & Complete Track & 100.00 & 61.67 & 62.30 & 61.29 & 63.33 \\ \hline Camera & Complete Track & 99.88 & 60.00 & 62.50 & 58.82 & 66.67 \\ \hline Fusion\_Data & Complete Track & 100.00 & 56.67 & 56.67 & 56.67 & 56.67 \\ \hline Fusion\_Interpretation & Complete Track & 100.00 & 63.33 & 63.33 & 63.33 & 63.33 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model performance of XGBoost.  
Figure 7: Confusion matrix of XGBoost: city with fusion on interpretation level.  
regression reaches between 67% and 97%, which suggests a lower risk of overfitting compared to the other models. The near-perfect training accuracy of XGBoost and random forest indicates a strong tendency for overfitting, as they almost perfectly memorize the training data.  
In the case of logistic regression, there is a clear trend toward higher training accuracy as the datasets are fused. While this increases the risk of overfitting, it also improves the overall accuracy.  
However, the focus should be on test accuracy, which is a more reliable indicator of model performance. Logistic regression maintains consistent test accuracy across all scenarios, with a slight reduction in performance on rural roads. In contrast, XGBoost and random forest exhibit greater variability in test accuracy across scenarios. Test accuracy is notably higher in city and rural road scenarios compared to the highway. When the entire route is considered, the models achieve test accuracy values that fall between the scenario-specific results.  
Additionally, the results demonstrate that data fusion tends to improve test accuracy. As shown in Table 5, the table highlights which model performs best in each scenario and indicates the data sources used to achieve the corresponding test accuracy.  
In general, logistic regression and random forest tend to outperform XGBoost. Random forest achieves classification accuracies of up to 73%. However, across all three models, overfitting remains a significant issue, with near-perfect training accuracies contrasting sharply with the fluctuating test accuracies and F1 scores.