## 4 Experiments and Results
### Experiment Result  
The visual segmentation effects are shown in Figures 10 and 11. The figures show almost all methods perform well in noise suppression. Compared with other segmentation methods, the proposed method has great advantages in terms of lane edges and details. The alleys in the field of view can become slender when the driveway tends to curve. The proposed method can completely segment the lane without distortion, which is superior to other methods in terms of accuracy and precision. This elongated part of the lane is the key to the navigation prediction when the vehicle makes a turn. Accurate prediction of the slender lane ahead endows the system with more reaction time. Figure 9: The loss curve of CSTA in the training process. As shown in Figure 11, the proposed network has clear advantages for overexposed images. The lanes in overexposed regions restored by other methods appear incomplete, disconnected, and severely distorted. The proposed method can accurately segment the lanes after MAE reconstruction, and maintain coherence when the distant lanes are thin and narrow. Performance Evaluation Metrics: The performance of the CSTA network is evaluated by three common quantitative metrics for image segmentation:  
1. Pixel Accuracy (PA) is used to evaluate the ratio of correctly predicted pixels to the total number of pixels, which can be expressed as: \[PA=\frac{N_{pred}}{N_{gt}}=\frac{TP+TN}{TP+TN+FP+FN}\] (3) where \(N_{pred}\) is the number of correctly predicted pixels, \(N_{gt}\) is the total number of pixels, \(TP\) is the number of positive samples to be predicted as positive, \(TN\) is the number of negative samples to be predicted as positive, and \(FN\) is the number of positive samples to be predicted as negative;
2. Since the boundaries of overexposed images are blurred after inpainting, F\({}_{1}\)-score is used to evaluate the quality of segmentation boundaries. F\({}_{1}\)-score refers to the average evaluation of _Precision_ and _Recall_. The formula can be expressed as: \[\text{F}_{1}=2\times\frac{Precision\times Recall}{Precision+Recall}\] (4)  
Figure 11: Lane segmentation with overexposure. Figure 10: Lane segmentation with proper exposure. \[Precision=\frac{TP}{FP+TP} \tag{5}\] \[Recall=\frac{TP}{FN+TP} \tag{6}\]
3. Intersection over Union (_IoU_) is used to evaluate whether the lane region can be accurately segmented by the network. That is, the intersection ratio of the predicted samples and the actual samples, which can be expressed as: \[IoU=\frac{TP}{TP+FP+FN}\] (7)  
In addition, the inference speed indicator fwt is added to calculate the time it takes to output the final result from an image. The processing of properly exposed and overexposed images is different. There is no preprocessing for image inpainting for properly exposed images. Therefore, in the calculation of the fwt of overexposed images, only overexposed images are selected for testing, and the mean of their fwts is calculated. In practical applications, there are relatively few cases of overexposure. The average inference speed of the system is much faster and the average time cost is much smaller than this value. The performance of the proposed method is quantitatively compared with ERFNet [17], CCNet [20], PP-LiteSeg [18], and FLANet [19] on IoU, F\({}_{1}\)-score, PA, and fwt. Table 1 shows the comparison results for the testing dataset with properly exposed images. Table 2 shows the comparison results for the testing dataset with 15% of overexposed images. Table 1 shows the quantitative comparison results between the proposed CSTA segmentation network and other segmentation networks on the proper exposure dataset. It can be seen from Table 1 that the proposed network achieves satisfactory results compared with other networks. Specifically, the proposed method has a significant improvement in the evaluation indicators IoU, F\({}_{1}\)-score, and PA compared with ERFNet and CCNet. Moreover, it is slightly improved compared with FLANet.