# Grid Anchor Lane Detection Based on Attribute Correlation
## 1 Introduction  
Autonomous vehicles represent a significant advancement in the automotive industry during the era of intelligent technology, and their core functionalities are heavily dependent on Advanced Driver Assistance Systems (ADASs) [1]. In autonomous driving scenarios, lane lines serve as essential indicators of road traffic regulations, and their detection performance is directly linked to a vehicle's ability to operate in compliance with legal and regulatory standards. As a result, lane detection has become a key task within the perception module of ADASs. Lane lines are typically marked on the road surface as solid or dashed lines, which are two-dimensional planar features and lack depth data. Consequently, cameras have become the main sensor for lane detection. The detection model provides accurate lane line shape and position information for the vehicle by detecting and analyzing the captured road images in real time. Benefiting from the powerful feature extraction capabilities of a Convolutional Neural Network (CNN) [2, 3], CNN-based models can achieve high accuracy in lane detection whenprocessing images with well-defined features [4; 5]. However, in complex scenarios, such as those involving vehicle occlusions or shadow interference (Figure 1a), the model must deeply capture local detail features to accurately locate lane lines, imposing higher demands on the extraction of low-level features. On the other hand, discontinuous lane lines present another significant challenge in detection tasks. For example, the left lane line in Figure 1b exhibits feature loss due to its fragmented structure, requiring the model to leverage global information for feature completion, thereby recovering the missing features. In addition, in some scenarios without lane lines, such as intersections (Figure 1c), models must rely on global features to capture the semantic information of the surrounding environment and make judgments based on the semantic context to avoid false detections. However, anchor-based detection models [6; 7; 8; 9; 10] typically utilize only local features, making them prone to misjudgment due to feature similarity. For high-curvature lane lines on curved roads (Figure 1d), the slope undergoes significant variations. Segmentation-based [11; 12; 13; 14; 15] and anchor-based detection methods often struggle to perceive these slope changes and make corresponding predictive adjustments. Similarly, parameter-based methods [16; 17; 18; 19] face difficulties in effectively modeling the complex shapes of such lane lines. As a result, detecting curved lane lines remains one of the key challenges in lane detection tasks. Although semantic segmentation methods can precisely depict the shape of lane lines and produce detection results consistent with their corresponding positional features, their high computational complexity makes it challenging to meet real-time requirements. In contrast, anchor-based detection methods represent the classification results of regional features using a limited amount of data. However, because predefined anchor positions are not well suited to the diverse shapes of lane lines, these methods rely on offset adjustments to correct positional errors. Consequently, anchor predictions may be computed based on features outside the anchor region. To align anchor classification results more strictly with the corresponding feature regions, this paper proposes a novel representation for lane lines called the \"grid anchor\". Current methods fail to capture slope and directional information inherent in lane lines. To address this limitation, the grid anchor incorporates various attributes to encode the relevant physical information. Global features play a crucial role in scenarios where lane lines are difficult to detect.