## 1 Introduction  
Road traffic sign recognition is a research hotspot in the intelligent driving of new energy vehicles. \"Made in China 2025\" requires intelligent driving vehicles to reduce energy consumption by more than 10% compared with conventional vehicles. This is achieved by optimizing the driving path of the car, reasonably planning the speed of the vehicle, and rapidly recognizing traffic signs. Correct and rapid identification of traffic signs can help smart cars drive safely in accordance with traffic rules, improve energy utilization, and reduce polluting emissions, thereby preventing violations and accidents. In 2011, [PERSON] [1] applied convolutional neural networks to traffic sign recognition. Deep neural networks perform well in traffic sign experiments, but most classification networks for traffic sign identification are still limited by the performance of onboard hardware devices in practical applications. Additionally, these networks with good classification effects often require deeper network layers for training, which indicates that more parameters and computing resources are needed. Thus, in the case of limited hardware capabilities, using pruning algorithms to trim the network model of traffic sign recognition can reduce the number of redundant parameters and the scale of the network model, making it more widely applicable in real life. Therefore, research on pruning algorithms for traffic sign recognition models is significant. Currently, methods proposed to reduce computing costs and scale down large-scale network models mainly include parameter quantization [2], low-rank decomposition [3], knowledge distillation [4], parameter sharing [5], and pruning [6; 7; 8; 9]. Among these, the compression work for traffic sign recognition models includes:  
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Specific** & **Advantage** & **Shortcoming** \\ \hline VGG [10] & Based on the VGG network, the channel pruning method based on Lasso regression is used to reduce the convolutional channel and compress the width of the network horizontally. Two lightweight convolutional framework [11] & This method compresses the scale of the traffic sign recognition network, which is conducive to the real-time application of traffic sign systems in urban road conditions. & If there is a set of highly correlated features, Lasso regression tends to choose one of the features and ignore all the others, which leads to instability in the results. pruning algorithms primarily focus on evaluating the importance of model filters but often overlook the impact of pruning on the manifold structure of the model's weight parameters. To address these issues, this paper considers the influence of changes in the manifold structure of weight parameters on model performance during filter pruning. Using the Vision Transformer (ViT) model [13] to classify traffic sign images, we propose a similarity filter pruning algorithm based on local linear embedding (SJ-LLE). During model training, the Local Linear Embedding (LLE) method and the Alternating Direction Method of Multipliers (ADMM) are used as loss functions during iterative update training before model pruning. After obtaining the model with updated weights, the similarity of filters is evaluated by calculating the Jensen-Shannon (JS) divergence value between filters in each network layer. Finally, by calculating the cross-entropy of filters, those with small cross-entropy among similar filters are pruned. This compresses the model size, reduces the hardware requirements for model deployment, and ensures the ViT model remains effective. The experimental performance loss during traffic sign recognition is within an acceptable range. This paper is organized as follows. The first part describes the overall framework and related theoretical basis of the proposed SJ-LLE pruning method. The second part provides the dataset used in the experiment and the preprocessing operations performed on the images before model training.