## 1 Introduction  
Recently, extensive research has been conducted on autonomous driving systems, which encompass a variety of types. For instance, the lane-change assist system utilizes cameras for improved visibility, while the lane-departure warning system detects the changes and alerts the driver, employing audible signals and steering wheel correction when there is a risk of departing from the lane while driving. Furthermore, the lane-keeping assist system (LKAS) helps maintain the vehicle within the lane during operation. Effectively to assist the driver, these systems rely on accurate lane detection and identification to determine the shape and position of the lanes. Advanced driver-assistance systems technologies employ a range of sensors, including cameras, ultrasonic sensors, and radars. Among these, cameras offer a cost-effective solution while providing a substantial amount of information. Additionally, when combined with ultrasonic or radar sensors, cameras can serve as auxiliary sensors. Consequently, cameras are extensively utilized in these systems. Lane detection from camera-captured images can be achieved through two methods: the model-based method, which utilizes mathematical models to detect and predict lane structures, and the feature-based method,which identifies lanes by analyzing pixel information such as patterns, colors, or gradients. However, the emergence and widespread use of deep learning methods employing neural networks has led to an increasing adoption of such techniques for lane detection across various domains. Accordingly, this paper classifies the pre-deep learning methods as tool-based methods, while methods employing neural networks are referred to as deep learning methods, and appropriate explanations are provided accordingly. One prominent method in the tool-based approach involves leveraging lane edges. This method utilizes Canny edge detection to identify edges, followed by the application of the Hough transform on the resulting edge image to detect straight lines. Another approach involves utilizing random sample consensus (RANSAC) to eliminate outliers or employing density-based spatial clustering of application with noise (DBSCAN) for clustering on the edge image. Another avenue is the utilization of color for lane detection. Several methods employ the RGB color space or employ color segmentation in color space models such as HSV and Lab to differentiate pixels and classify them into respective classes. The previously discussed methods primarily focus on lane detection and information about other vehicles during driving. However, for autonomous driving, a comprehensive approach is required, which encompasses not only lanes and other vehicles but also road network topology, signal locations, signage, and buildings. A digital map system called high-definition map (HD map) contains such detailed road information. HD maps are created based on various sensor data, including cameras, lidar, and GPS. On the other hand, in the deep learning-based approach, the U-Net ConvLSTM, a variation of U-Net with skip connections, can be employed for pixel-level segmentation. Additionally, some methods utilize generative adversarial network (GAN) models to generate lane-detection images based on input frames. As the interest in autonomous driving systems continues to increase, numerous companies and countries are actively conducting research and pursuing commercialization in this field. Given the variations in geography, laws, and regulations, road and natural environments differ across different countries. Consequently, autonomous driving systems must adapt and operate reliably in diverse environments. This paper presents a method for robust lane detection in challenging environments where lane detection using cameras becomes difficult. The proposed method involves several steps. First, the brightness of the input image is analyzed to differentiate between day and night conditions.