## 5 Conclusions and Future Works  
In the field of intelligent driving for new energy vehicles, a traffic sign recognition model optimization method based on SJ-LLE pruning is proposed. This paper primarily introduces the general framework of the method, detailing its process and providing an in-depth exposition of the formula derivation and theoretical research. The experimental results fully verify the hypothesis. Experiments show that the SJ-LLE method achieves excellent pruning effects across multiple datasets, significantly improving the model's precision loss and compression ratio. The results indicate that this method achieves better model compression and enhances the recognition speed of traffic signs with minimal accuracy loss.  
However, the correlation between the improved accuracy of the model and energy consumption was not considered. Compared to other pruning methods, the proposed method demonstrates superior accuracy in testing the pruned model under equal proportion pruning conditions. Nevertheless, experimental results reveal that, without signifi  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Pruning Method\(\backslash\)Pruning Rate** & **Baseline** & **10\%** & **20\%** & **40\%** & **50\%** & **70\%** \\ \hline SJ-LLE & 99.03 & 99.09 & 98.72 & 98.05 & 97.58 & 96.04 \\ ASFP & 99.03 & 98.26 & 96.82 & 95.94 & 94.48 & 93.84 \\ HYDRA & 99.03 & 98.28 & 97.72 & 96.12 & 95.51 & 94.66 \\ Filter & 99.03 & 97.22 & 95.81 & 95.95 & 93.47 & 92.95 \\ FPGM & 99.03 & 98.53 & 98.01 & 96.95 & 96.03 & 95.18 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of accuracy (%) of the ViT model on the GTSRB dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Pruning Method\(\backslash\)Pruning Rate** & **Baseline** & **10\%** & **20\%** & **40\%** & **50\%** & **70\%** \\ \hline SJ-LLE & 98.78 & 98.72 & 98.13 & 97.95 & 96.88 & 96.09 \\ ASFP & 98.78 & 98.06 & 96.67 & 95.85 & 94.75 & 93.19 \\ HYDRA & 98.78 & 98.13 & 97.10 & 95.42 & 94.84 & 94.15 \\ Filter & 98.78 & 97.96 & 95.64 & 93.82 & 92.96 & 92.12 \\ FPGM & 98.78 & 98.03 & 97.15 & 95.39 & 95.04 & 94.51 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of accuracy (%) of the ViT model on the BelgiumTS dataset.  
cantly sacrificing model accuracy, the pruning ratio achieved by this method is only 70%. Therefore, further improvements to the pruning method are necessary to achieve a higher model compression ratio. Future research will focus on the relationship between model performance and energy consumption in the context of new energy intelligent driving vehicles, combined with the traffic environment.  
Conceptualization, W.W.; Data curation, X.L.; Writing--original draft, W.W. and X.L.; Writing--review & editing, X.L.; Funding acquisition, W.W. All authors have read and agreed to the published version of the manuscript.  
This work was financially support by the National Natural Science Foundation of China (Grant Nos. 61772249, 61702241), the Basic Research Projects of the Liaoning Provincial Department of Education (Grant Nos. LJKZ0362).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to data privacy restrictions.  
The authors would like to thank all the reviewers who participated in the review during the preparation of this manuscript.  
The authors declare no conflict of interest.