###### Abstract  
This study proposes a vision-based framework to support AVs in maintaining stable lane-keeping by assessing the condition of lane markings. Unlike existing infrastructure standards focused on human visibility, this study addresses the need for criteria suited to sensor-based AV environments. Using real driving data from urban expressways in Seoul, a YOLOv5-based lane detection algorithm was developed and enhanced through multi-label annotation and data augmentation. The model achieved a mean average precision (mAP) of 97.4% and demonstrated strong generalization on external datasets such as KITTI and TuSimple. For lane condition assessment, a pixel occupancy-based method was applied, combined with Canny edge detection and morphological operations. A threshold of 80-pixel occupancy was used to classify lanes as intact or worn. The proposed framework reliably detected lane degradation under various road and lighting conditions. These results suggest that quantitative, image-based indicators can complement traditional standards and guide AV-oriented infrastructure policy. Limitations include a lack of adverse weather data and dataset-specific threshold sensitivity. m 20252626 June 2  
rely on sensors and Artificial Intelligence (AI) to perceive the road environment and make decisions without human intervention, future roadway standards may need to adopt approaches better suited for AV operations [2]. For instance, the lane keeping assistance system (LKAS) enables AVs to maintain lane position using onboard devices such as cameras and LiDAR sensors [3; 4; 5]. If lane markings are poorly maintained, AVs may fail to recognize them, increasing the risk of unintentional lane departure and collisions. However, current standards for installing longitudinal pavement markings mainly focus on retroreflectivity, ensuring nighttime visibility for human drivers under manual driving [6]. Whether these standards suit autonomous driving remains uncertain. Prior studies have linked many ADAS disengagements to environmental factors like degraded lane markings [7]. Since AVs use onboard sensors to detect lane shapes, the marking shape may be more critical than visibility. Thus, AV-oriented standards should include shape criteria alongside retroreflectivity, yet no formal research has addressed this need. Numerous studies have been conducted to improve the lane detection capabilities of AVs, which are essential for the proper functioning of autonomous driving systems [8; 9]. However, most of these efforts have focused on enhancing the lane recognition performance of the vehicle itself. As lane departure accidents still occur, it is difficult to conclude that AV technology has yet reached complete reliability. Therefore, realizing a fully autonomous driving environment--capable of standalone operation--will require not only advances in vehicle-based technologies but also support from road infrastructure systems. This study aims to support autonomous driving by enabling more reliable lane positioning through infrastructure analysis. It proposes a vision-based framework to quantitatively evaluate the impact of lane marking conditions on AV operations. The framework integrates two core algorithms: (1) a YOLOv5-based algorithm for accurately identifying lane markings among various road symbols and (2) a degradation assessment algorithm that determines whether the identified markings are faded, based on pixel-level occupancy thresholds. Specifically, this study makes three key contributions: (i) a customized YOLOv5-based lane identification module that distinguishes lane markings from other road surface symbols using class redefinition and lane-specific data augmentation; (ii) a quantitative lane degradation assessment algorithm that determines whether detected lane markings are faded using a pixel occupancy threshold; and (iii) an integrated framework that combines detection and degradation assessment to enable practical and explainable infrastructure-level support for AV lane-keeping tasks.