## 4 Framework Development and Validation
### Lane Identification Algorithm Development  
In the early phase of this study, a single-label annotation approach was applied for lane classification; however, the resulting model exhibited relatively low accuracy metrics. To address this limitation, a multi-label annotation method was introduced, which led to improved performance in both lane detection and classification, enabling more precise lane identification. According to previous research, the application of multi-label annotation can enhance the object differentiation capability of trained models compared to single-label methods [26]. This study incorporated this approach to more effectively distinguish lanes from other objects in the roadway environment. Based on the experimental results, a refined labeling scheme was established to clearly distinguish lane markings from other roadway elements, as illustrated in Figure 5. The labeling criteria were designed to minimize confusion between lanes and other components on the road. Specifically, regions with white backgrounds containing text were labeled as \"road markings,\" those containing dashed lines were labeled as \"safety zones,\" regions with arrows were labeled as \"arrows,\" and areas that did not include any text, dashed lines, or arrows were labeled as \"lanes.\" This multi-labeling strategy was implemented to improve the reliability of lane detection and reduce false positives. Figure 4: YOLO-based lane identification and assessment process: (**I**) Image generation and pre-processing procedures for equipment setup and function check. (**II**) Object labeling process to distinguish lane markings from other classes. (**III**) Model training, evaluation, and additional training through inference results. (**IV**) YOLOv5 architecture structure consisting of backbone, neck, and head. (**V**) Lane condition assessment algorithm that analyzes lane pixel occupancy and classifies the lane as normal or faded based on a threshold value. In the initial experiments, training the model using the originally labeled images resulted in relatively low accuracy metrics. To address this limitation, a multi-labeling technique was applied alongside data augmentation to improve the model's robustness against various types of noise and environmental variations, prevent overfitting, and ensure consistent performance on new datasets. Similar effects have been observed in previous studies, where data augmentation was shown to effectively enhance the generalization capability of deep learning models [27]. The original dataset consisted of 330 images collected at moments when autonomous driving functions (e.g., LKAS) were disengaged. Various data augmentation techniques--such as brightness adjustment, rotation, scaling, and random cropping--were then applied to reflect diverse road conditions, increasing the training dataset to 3304 images. Figure 6 presents examples of images generated by the augmentation algorithm. The corresponding process is described in Algorithm 1. Figure 5: Labeling decision process for lane-related objects, including road markings, arrows, safety zones, and lanes (both dashed and solid lines are labeled as “lane”). Figure 6: Image examples after data augmentation. ```
functionLoad_Image(image_path)
1. Load image as binary data
2. Decode image using OpenCV
3. Return image end function functionApply_Augmentations (image, labels)
4. Define augmentation techniques
5. for each augmentation in list do
6. if augmentation is brightness_contrast then
7. Randomly adjust brightness and contrast
8. else if augmentation is blur then
9. Apply Gaussian blur to reduce detail
10. else if augmentation is noise then
11. Add random Gaussian noise to simulate camera noise
12. else if augmentation is horizontal_flip then
13. Flip image horizontally and adjust bounding boxes
14. else if augmentation is gamma_correction then
15. Randomly adjust gamma for varied lighting
16. else if augmentation is rotation then
17. Rotate image randomly and adjust bounding boxes
18. else if augmentation is crop then
19.