## 2 Literature Review  
Recent advances in autonomous driving technologies have highlighted two critical challenges in lane-related research: accurately detecting lane markers under varying conditions and objectively evaluating their degradation. Addressing these issues is essential to ensure AV safety and infrastructure adaptability.  
Research on lane markings can largely be categorized into two major objectives: improving lane detection accuracy and evaluating lane degradation. Studies targeting detection accuracy mainly focus on the precise identification and tracking of lane markers. A real-time lane modeling and tracking method using distance transform techniques has shown reliable performance in relatively simple urban environments [11].  
Among Convolutional Neural Network (CNN)-based approaches, a representative example is the end-to-end learning method for autonomous driving, in which steering angles are predicted directly from road images to maintain lane position [12]. Another study introduced a Spatial CNN that not only recognizes lane markers but also contributes to scene understanding [13]. A combined method using a CNN and Random Sample Consensus (RANSAC) was proposed to improve the robustness of lane detection [14], while a deep learning model based on OverFeat showed the effective recognition of lanes and vehicles in highway environments [15]. These studies mainly focused on improving detection accuracy and real-time performance. The YOLO (You Only Look Once) algorithm, developed by [PERSON] et al., provides real-time object detection and has been widely used in autonomous driving applications [16]. [PERSON] and [PERSON] proposed a YOLOv5 model with a transformer-based head to classify damaged and intact lane markings using improved augmentation techniques [17]. [PERSON] and [PERSON] further extended YOLO techniques to detect lanes in complex multi-lane environments under diverse conditions [18]. More recently, [PERSON] et al. proposed a Transformer-based lane detection framework (LDTR) with an anchor chain representation that improves robustness in nighttime and occluding conditions [19], and [PERSON] et al. introduced a contrastive learning framework (CLLD) utilizing cross-similarity to enhance lane detection under fading and low-visibility conditions [20].  
Studies assessing lane degradation have increasingly adopted segmentation-based models, which are critical in this domain. For example, an encoder-decoder architecture using DeepLabv3+ was proposed to analyze lane markings at the pixel level and evaluate their deterioration [21]. A semantic segmentation method based on generative adversarial networks (GANs) has also been developed for lane detection tasks [22]. An instance segmentation approach showed high accuracy in identifying lanes under complex conditions [23]. In addition, ENet, a lightweight segmentation network, was introduced for real-time detection [24]. [PERSON] et al. proposed a dual-branch network combining detection and segmentation, enhancing robustness against occlusion and improving the accuracy of lane degradation assessment [25].  
However, most previous studies on lane maintenance still face critical limitations. First, a consistent and quantitative criterion for evaluating the condition of lane markings remains underdeveloped [20; 21; 22; 23; 25]. Second, although some recent studies have attempted to assess degradation in complex road environments and under adverse weather conditions [20; 25], establishing a practical and generalizable evaluation framework remains a challenge. Lastly, labeling-based damage assessments often reflect the subjective judgment of researchers, leading to inconsistencies in evaluation [15; 17; 19; 23].  
In summary, this study contributes to the literature by introducing a dual-stage algorithm that enables both precise lane identification and quantifiable degradation assessment, addressing the limitations of subjective or inconsistent evaluations in prior work.