## 5 Conclusions  
This study proposed a vision-based framework for lane identification and condition assessment to support the stable lane-keeping functionality of AVs. Unlike conventional road standards focused on human perception and nighttime visibility, the proposed framework addresses the need for evaluation criteria suited to sensor-based AV driving environments by focusing on the geometric integrity of lane markings. A YOLOv5-based lane detection algorithm was developed using real-world driving data collected from major urban expressways in Seoul. Multi-label annotation and data augmentation techniques were employed to enhance the model's accuracy and generalization. The trained model achieved a mean average precision (mAP) of 97.4%, and its performance was validated on external datasets such as KITTI and TuSimple, confirming its robustness. Based on the detection results, a lane condition assessment algorithm was constructed using pixel occupancy analysis combined with edge and morphological operations. A threshold of 80% pixel occupancy was introduced to distinguish between intact and worn lanes, and this criterion was shown to perform reliably under various road and lighting conditions. The findings suggest that AI-based evaluation techniques can complement existing visibility-based standards and support the development of AV-compatible lane maintenance policies by providing quantitative, interpretable criteria. The framework is also applicable in real-world conditions, offering both technical feasibility and policy relevance. However, several limitations should be noted. The training data were limited to urban expressways and typical day/night scenarios, lacking adverse weather or rural  
Figure 17: Framework for lane identification and condition assessment: (**a**) Lane identification algorithm using YOLOv5 to detect various road markings and isolate objects classified as “lane” for further evaluation. (**b**) Lane condition assessment algorithm that determines whether each lane is faded or normal through pixel-level edge detection and morphological processing; the assessment logic is embedded in the head stage of the YOLOv5 architecture to enable integrated lane detection and degradation analysis. road conditions. Additionally, the 80% pixel occupancy threshold was derived from the characteristics of the training dataset and may vary depending on lane material, color, and brightness contrast. Its generalizability should be validated with diverse datasets and environments. To address these limitations, future research should explore integrating more advanced techniques capable of improving performance in challenging environments. For example, Transformer-based models (e.g., LDTR) can offer improved robustness under nighttime and occluded scenes, contrastive learning frameworks (e.g., CLLD) enhance detection in fading or low-visibility conditions, and dual-branch architectures allow simultaneous detection and degradation segmentation. While not applied in the current study, these techniques hold potential to reinforce the proposed framework by enabling adaptive evaluation under complex road and environmental conditions. Building on these insights, future research should aim to integrate such approaches to improve performance under adverse conditions and to establish more adaptive evaluation criteria beyond fixed pixel occupancy thresholds. By laying the groundwork for such advancements, the proposed framework contributes a practical baseline for infrastructure assessment in autonomous driving. Continued research should focus on expanding environmental diversity in training data and refining evaluation methods to ensure broader applicability and consistency. Conceptualization, H.N., D.G.K. ; data curation, H.N., J.M.K. ; formal analysis, H.N. ; methodology, H.N. ; project administration, H.N. ; supervision, H.N. ; validation, H.N., C.L. ; visualization, H.N. ; writing--original draft preparation, H.N. ; writing--review and editing, H.N.