## 4 Simulations  
### Experimental Results  
The proposed method was implemented on a PC with the following specifications: an Intel i5-6500 processor, 8 GB RAM, running Python version 3.9.12 on Windows 10. Additionally, the U-Net ConvLSTM and DSUNet methods for comparisons have been trained and executed in a different computer environment. This environment consisted of an Intel i9-11900K processor, 32 GB RAM, and an RTX 4090 GPU. The U-Net ConvLSTM and DSUNet implementation utilized Python version 3.9.12 on Linux Ubuntu version 22.0.4.s. Figures 17-20 present the comparison results among various lane-detection methods including the U-Net ConvLSTM and DSUNet neural network architectures. The DSUNet is a modification of the U-Net architecture, where all layers except the first layer are replaced with depthwise and pointwise convolution layers from the standard convolution layer structure. Additionally, drop-out is incorporated to prevent overfitting. The conventional method incorporates the Gaussian filter, Canny edge, Hough transform, and Kalman  
Figure 16: Experiments for determining the Distance factor and Theta factor (result of k-means clustering of distance–theta points cloud). The red arrows indicate the range of Distance and Theta of the lanes (Blue, red, and yellow: information about lines other than the lanes. Green: lane information). filter [2; 10; 11]. Furthermore, the U-Net ConvLSTM is a neural network architecture that replaces the skip connection part of the U-Net with long short-term memory (LSTM) [29]. The neural network was trained using a dataset comprising 401,052 images. As mentioned earlier, the experiments were conducted by comparing different environments. These environments can be broadly categorized as follows: cases where the image contains a wiper, cases where the lane markings on the road are not clearly visible due to rainwater, cases where there are obstructions such as guidelines in the road area, and cases where the road area is overexposed by headlights from vehicles and other factors during nighttime. Figure 17 demonstrates the results of the lane detection when a wiper is present in the image. The U-Net ConvLSTM method struggles to detect the lanes accurately. In the case of the DSUNet method, it increases the number of unnecessary detections, such as pedestrian crossings or windshield wipers. The conventional method achieves relatively successful lane detection. However, in videos where the windshield wiper is constantly moving, the detection results are not satisfactory due to wiper interference. In contrast, the proposed method performs well in both scenarios, accurately detecting the lanes even when rainwater reflection or wiper interference is present. Figure 18 shows the results of lane detection in the case where the road area is heavily reflected due to rainwater. In this  
Figure 17: Experimental results in the case where the wiper obstructs the image: (**a**) conventional; (**b**) DSUNet; (**c**) U-Net ConvLSTM; and (**d**) proposed method. case, where the reflection is severe, other methods fail to detect the lane markings, while the proposed method successfully identifies the lane markings. Figure 19 displays the results of lane detection in the case where there are lines other than lane markings present in the road area, causing interference with the detection process. DSUNet shows a better performance compared to U-Net ConvLSTM as it avoids detecting irrelevant lines and provides more accurate lane detection. Figure 20 demonstrates the challenges posed by dark environments where there is no sunlight and the lane area is overexposed by headlights from vehicles and streetlights. DSUNet demonstrates a certain level of robust detection even under challenging conditions. On the other hand, the U-Net ConvLSTM method, due to severe noise, fails to detect lanes in several areas. Additionally, the conventional method struggles to properly detect the lanes when they are obstructed by guidelines before entering tollgates.