## 6 Conclusions  
This paper has shown the importance and advantages of using traffic sign detection and classification in ITS in smart cities through different techniques and technologies, especially computer vision and machine learning techniques. This paper presents a novel LE-CNN architecture that combines high accuracy with real-time processing capabilities. This efficient and robust model paves the way for reliable, secure, and efficient traffic sign recognition in self-driving cars, contributing to safer and more autonomous navigation across diverse regions like the Arabic world. Our findings demonstrated that the provided LE-CNN model was highly accurate at classifying the traffic signs while outperforming optimized ResNet v2 in this regard, scoring a test accuracy of 96.5% with a training time of 719 s as opposed to 96.14% with a training time of 8425 s.  
In conclusion, in this paper, we have proposed a system that has the potential to alleviate the problems associated with traffic sign classification, which ensures the reduction in traffic congestion to enhance user satisfaction. Considering that latency in applications such as self-driving cars is highly important, more work is required to consider the process of capturing the sensory image, framing, and pre-processing. Also, collision avoidance timing and distance requirements in relation to vehicle speed on highways versus on crowded streets need to be considered.  
Conceptualization, A.A.K., W.M.A., H.M.E. and R.A.S.; methodology, A.A.K. and R.A.S.; software, A.A.K.; validation, W.M.A., H.M.E. and R.A.S.; writing--original draft preparation, A.A.K.; writing--review and editing, W.M.A., H.M.E. and R.A.S.; project administration, H.M.E. and R.A.S.; funding acquisition, W.M.A. All authors have read and agreed to the published version of the manuscript. This research was funded by [PERSON] Nourah Bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R500), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. Not applicable. Not applicable. Not applicable. Data Availability Statement: The data that support the findings of this study are openly available at [[https://data.mendeley.com/datasets/4](https://data.mendeley.com/datasets/4) tzkn45 mx/1]([https://data.mendeley.com/datasets/4](https://data.mendeley.com/datasets/4) tzkn45 mx/1) (accessed on 29 April 2024). The authors would like to acknowledge [PERSON] Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R500), Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia for supporting this project.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Layers** & **Val. Acc.** & **Test Acc.** & \begin{tabular}{c} **Val. Time** \\ **without K-Fold** \\ \end{tabular} & \
\begin{tabular}{c} **Val. Time** \\ **with k-Fold** \\ \end{tabular} &
\begin{tabular}{c} **Testing** \\ **Time** \\ \end{tabular} \\ \hline
**ResNet v1, 2023**[15] & 8 & 95.73\% & 87.45\% & 1758 s & - & - \\ \hline
**Optimized ResNet v1, 2023**[15] & 56 & 99.18\% & 96.14\% & 8425 s & - & - \\ \hline
**VIT** & 12 & 79.19\% & 73.63\% & 271 s & - & 0.04 s \\
**Transformer**[26] & 11 & 99.4\% & 96.5\% & 719 s & 3595 s & 1.65 s \\ \hline
**LE-CNN** & 10 & 10 & 10 & 10 & 10 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between the proposed LE-CNN model and other recent models.