## 3 Methods
### Image Inpainting Module  
The image inpainting module is used to restore the overexposed region in the original image to improve the quality of lane segmentation. In the proposed method, MAE, which is a scalable self-supervised learning scheme applied to computer vision, is adopted as the network of image inpainting module. The architecture of MAE is shown in Figure 3.  
As Figure 3 shows, the architecture of MAE is an asymmetric encoder-decoder structure [30]. To preserve position information, the encoder position embeddings and decoder position embeddings are initialized by the 2D sine-cosine method. The size of encoder position embeddings is m \(\times\) e and the size of decoder position embeddings is m \(\times\) d. The input image is divided into n \(\times\) n patches in horizontally and vertically, m = n \(\times\) n; e and d are the numbers of nodes of a single patch vector of encoder and decoder position embeddings. The input of the MAE encoder includes the original image, the encoder position embeddings, and the Id Store matrix. The image is reshaped and linearly transformed into an m \(\times\) e sequence of patch vectors by the encoder. Then the sequence of patch vectors is added to the encoder position embeddings. The information of image and position are merged. The sequences set of unmasked patches in properly exposed regions can be obtained from the Id Store matrix. The properly exposed patch vectors in the merged embeddings are selected and input into the Transformer encoder blocks [41]. The encoded latent vectors are finally output by the MAE encoder. The size of encoded latent vectors is n \(\times\) e, where n represents the number of unmasked patches. The input of the MAE decoder includes encoded latent vectors, decoder position embeddings, and Id Store matrix. The encoded latent vectors are linearly transformed into n \(\times\) d by the MAE decoder. Next, an (m \(-\) n) \(\times\) d vector set of mask tokens is initialized by the MAE decoder, and each mask token is a learnable vector. Mask tokens and encoded latent vectors are concatenated by the decoder. The size of the concatenated sequence of vectors is m \(\times\) d. The unmasked patches vectors are restored to their corresponding positions, and the complete set of tokens is output. The complete set of tokens and decoder position embeddings are added and input into Transformer decoder blocks. The masked patch vectors in the tokens set are reconstructed by Transformer decoder blocks. The m \(\times\) d vector sequence is output by Transformer decoder blocks, containing n \(\times\) n patch  
Figure 1: Data preprocessing procedure. Figure 2: The result of marked overexposed region. vectors. Then the vector sequence is linearly transformed and reshaped to obtain the reconstructed image. In the basic MAE model, the number of layers in the Transformer encoder block is 12 and the number of layers in the Transformer decoder block is 8. Deeper network layers can make the model have better generalization ability and can adapt to a variety of application scenarios. However, the model parameters and inference time increase. The application scenario in the manuscript is relatively single, which is only used for the repair work of AGV lanes. The application scenarios do not require high generalization ability of the model. So too, deep network layers are not needed. Since the application scenario of the model is a real-time lane detection and navigation system, the real-time requirement of the model is high. Under the premise of meeting the segmentation requirements, the complexity of the network should be reduced as much as possible to speed up the model inference time. A comparative experiment was set up to verify the impact of different number of Transformer blocks on the inpainting effect. The reduced model was compared with Transformer blocks 12-8. Transformer blocks were set to 6-4 and 3-2, respectively.