## 3 Methods
### CSTA Network Structure  
The structure of the proposed CSTA is shown in Figure 5. The encoder consists of 4 convolution modules. The decoder consists of 4 deconvolution modules and 3 attention modules. The main function of the encoder is to down-sample the image to extract its features. The input is the lane image restored by MAE and the size is 3 \(\times\) 224 \(\times\) 224. The first two encoder modules have the same structure, consisting of one convolutional layer with a kernel size of 3 \(\times\) 3, one nonlinear layer, and one max-pooling layer. The following two encoder modules consist of two convolutional layers, two nonlinear layers, and one max-pooling layer. The size of the convolution kernel is also 3 \(\times\) 3. The convolutional layer is used to extract features. The function of the max-pooling layer is to retain the main features while reducing the size of the data. The network doubles the number of feature channels with each down-sampled module. The encoder finally outputs a 512 \(\times\) 14 \(\times\) 14 high-level feature map. Figure 4: Impact of different Transformer blocks on image inpainting results. Figure 5: CSTA network structure diagram. The decoder performs the up-sampling process, which compresses the number of feature map channels through the deconvolution layer and enlarges the size. The size of the kernel of the deconvolution layer is 3 \(\times\) 3. After the feature image passes through the nonlinear layer and batch normalization (BN) layer, the corresponding feature layer in the down-sampling process is synchronously input into the attention module through skip connection. There are two reasons for using skip connections here. First, as the network deepens, the image may lose some details, which are difficult to be recovered if only deconvolution is used in the up-sampling process. The feature maps transmitted through skip connections contain a lot of detailed information, which helps to improve the image quality during the up-sampling deconvolution process. Secondly, Skip connections can speed up model convergence and make network training easier when using gradient backpropagation. The feature maps are up-sampled by 4 deconvolution modules and 3 attention modules. After up-sampling, the size of the feature maps is changed to 32 \(\times\) 224 \(\times\) 224. Finally, the feature maps go through a 1 \(\times\) 1 convolutional layer to output a 1 \(\times\) 224 \(\times\) 224 single-channel lane segmentation image. The segmentation of narrow lanes in the far field of view is incomplete and the effect of general image segmentation is not ideal. Due to the better effect of the attention mechanism in the field of image processing and image enhancement, the triple attention module is designed to improve the segmentation ability of the proposed CSAT network. With the help of the attention module, the blurred edges in the image can be well-segmented and the noise can be effectively reduced. At the same time, more key features can be extracted for and the robustness of the network model can be enhanced. The proposed triple attention modules are Attention Gate (AG), Channel Attention (CA), and Spatial Attention (SA), respectively. The schematic diagram of the triple attention module is shown in Figure 6. The AG module in the triple attention module first process the data. The AG module is used to analyze context and drive the network to pay more attention to local regions by scaling the attention coefficients. The down-sampled feature map is fed into the Attention Gate of the up-sampling process through skip connections, while the corresponding up-sampled feature map is used as another input of the Attention Gate. After the two feature maps undergo 1 \(\times\) 1 convolution and one layer of BN, the size of image is changed from C \(\times\) W \(\times\) H to C/2 \(\times\) W \(\times\) H. Both results conduct the element-wise adding. The down-sampled and up-sampled feature maps are first fused to retain part of the features obtained by convolution.