## 2 Related Works
### Lane Detection  
#### 2.2.1 Tool-Based Methods  
Vehicle-installed cameras often exhibit poor image quality due to cost constraints and spatial environmental limitations. Consequently, the acquired images for lane detection often contain various elements that interfere with accurate detection, such as noise in low-luminance regions. To address this, a smoothing filter is applied to suppress noise and enhance edge sharpness prior to optimal edge detection. Specifically, a bilateral filter is commonly used, which reduces noise while preserving the lane edges [6]. The bilateral filter employs two parameters, intensity sigma and space sigma, which can be adjusted to control the level of edge preservation and smoothing based on the environmental information in the image. The Canny edge detector is a powerful method for edge detection, typically involving four stages: noise reduction, edge gradient computation, nonmaximum suppression, and double thresholding and edge tracking through hysteresis [7]. These four stages can be observed in Figure 2. Although the Canny edge detector offers high detection performance, its processing speed is relatively slow due to the division into multiple stages. As a result, in cases where most lanes appear vertical in vehicle-installed cameras, an alternative approach is to utilize the faster calculation of the Sobel operator [8; 9]. Figure 1: Comparison of histogram equalization and histogram stretching. The Hough transform is a highly effective method for detecting approximate straight lines in edge images. It represents a straight line as a point in the domain of gradient and y-intercept. In this representation, all possible straight lines that can pass through a point are transformed into a single line in the gradient-y-intercept plane [10]. Due to this property, the Hough transform is widely utilized in lane detection. Additionally, another approach for detecting estimated lane lines in edge images involves using the RANSAC algorithm [11; 12]. RANSAC is commonly employed in regression problems and consists of two steps: the hypothesis step and the verification step. The process is iterated a specified number of times set by the user. In the hypothesis step, a set of \(n\) sample data points is randomly selected from the entire dataset, and a model is predicted based on these samples. In the verification step, the number of data points that match the predicted model are counted, and the model with the maximum number of matches is saved. Through this iterative process, a lane model can be identified in the edge image, enabling lane estimation detection. Furthermore, the edge pixels in an edge image form a point cloud, and DBSCAN can be utilized to group the points based on their density in the point cloud image [13]. Several methods employ DBSCAN for lane detection in edge images [12; 14]. During the density-based grouping process, there is an approach that incorporates directional information, known as directional DBSCAN (D-DBSCAN). This technique is particularly useful for detecting parking slot lines [15]. Moreover, lane detection can be performed by converting the input image into a bird's-eye view after acquiring the camera's internal and external parameters through camera calibration [16; 17]. This method is commonly used for detecting curved lanes that are relatively distant from the camera. Autonomous driving systems not only rely on cameras but also actively involve research utilizing distance-detection sensors such as lidar. Three-dimensional lidar enables more precise distance measurements in three-dimensional space compared to lidar, but it may encounter challenges such as occlusion and out-of-range issues when reaching the sensing-range limitations of connected automated vehicles (CAV). To address these problems, HYDRO-3D combines object detection and object tracking information  
Figure 2: The four steps of Canny edge detection. (**1**) Noise Smoothing through Gaussian Filtering.