# Simplified Machine Learning Model as an Intelligent Support for Safe Urban Cycling
## 4 Results
### Comparison of the Model Against Reference Data  
Once the experimental phase has been carried out, the results obtained with each of the datasets used to evaluate the behavior, the performance and efficiency of the model are presented below. Likewise, a comparison is made of the model developed in this research against the various models present in the state of the art to also observe its performance and efficiency. The main objective of the present research focused on developing a biologically inspired computational model, which would allow simplified machine learning using few examples to detect a possible risk of unexpected collision and thereby assist the cyclist in driving within an urban environment. Therefore, the model was specifically evaluated in terms accuracy in the identification of perceived information (images) using the single-example mode (one-shot), and for the five-example mode (5-Shot), likewise a comparison with the datasets, using the two feature extractors indicated. Tables 3 and 4 show the average accuracy with 95% confidence when performing image classification using the four datasets, the affinity methods separately, and the proposed SML model (\(A_{max}\) layer). Training was performed using the MiniImageNet dataset and the ResNet-18 and EfficientNet-b0 CNNs as feature extractors. The results show that, for all datasets and feature extractor networks, our SML model outperformed one-shot mode learning methods (one-shot classification accuracy) using separate similarity layers \(A_{1}\) (Euclidean) and \(A_{2}\) (Manhattan). Therefore, our SML model using both the ResNet-18 and EfficientNet-B0 feature extractors had the best performance in all cases. As can be seen in Figure 10a, the proposed SML model for the MiniImageNet dataset, with a ResNet-18 feature extractor, in five-shot mode, performs better by 16.85% compared with the best result, which is the similarity layer A1. On the other hand, in the one-shot \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline \multirow{2}{*}{**Feature Extractor**} & \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{\(\mathbf{A_{1}}\)} & \multicolumn{2}{c}{\(\mathbf{A_{2}}\)} & \multicolumn{2}{c}{**SML Model (\(A_{max}\))**} \\ \cline{3-8}  & & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** \\ \hline \multirow{4}{*}{EfficientNet-b0} & MiniImageNet & 64.14 & 70.47 & 61.65 & 64.49 & **66.90** & **81.71** \\ \cline{2-8}  & CIFAR-100 & 68.72 & 73.59 & 66.95 & 68.99 & **71.17** & **79.12** \\ \cline{2-8}  & CUB-200–2011 & 73.58 & 80.38 & 71.83 & 76.90 & **74.34** & **84.42** \\ \cline{2-8}  & DroNet & 61.99 & 69.14 & 60.08 & 64.28 & **64.35** & **80.85** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Identification accuracy. Average accuracy results with 95% confidence of the four datasets against three different affinity methods (\(A_{1}\), \(A_{2}\), and proposed SML model) using EfficientNet-b0 as a feature extractor. All experiments with the maximum affinity layer \(A_{max}\) are highlighted in bold. \begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline \multirow{2}{*}{**Feature Extractor**} & \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{\(\mathbf{A_{1}}\)} & \multicolumn{2}{c}{\(\mathbf{A_{2}}\)} & \multicolumn{2}{c}{**SML Model (\(A_{max}\))**} \\ \cline{3-8}  & & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** \\ \hline \multirow{4}{*}{ResNet-18} & MiniImageNet & 62.93 & 69.66 & 62.86 & 67.66 & **67.05** & **81.40** \\ \cline{2-8}  & CIFAR-100 & 62.83 & 68.50 & 64.30 & 67.99 & **67.14** & **80.17** \\ \cline{2-8}  & CUB-200–2011 & 70.46 & 77.29 & 70.48 & 76.32 & **73.52** & **83.73** \\ \cline{2-8}  & DroNet & 59.93 & 66.56 & 59.28 & 63.07 & **64.51** & **80.85** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Identification accuracy. Average accuracy results with 95% confidence of the four datasets against three different affinity methods (\(A_{1}\), \(A_{2}\), and proposed SML model) using ResNet-18 as a feature extractor.