# Effects of Hybridizing the U-Net Neural Network in Traffic Lane Detection Process
### Current Challenges and Research  
Despite significant progress, there are currently several challenges in the lane detection process. These challenges relate to variability/instability of environmental conditions, road infrastructure condition, different shapes and colors of road infrastructure markings, computational resources, and the need for data related to improbable scenarios. The variation of environmental conditions can lead to situations where glare, shadows, low light, and strong sunlight can obstruct the identification of lane markings. Similarly, adverse and/or extreme weather conditions (heavy rain, dense fog, snow, and heavy dust) can significantly reduce visibility and obstruct the detection of lane lines. Faded, worn or missing road markings as well as various complex road layouts (areas with multiple or irregular markings) can confuse detection systems. Adequate computational resources are crucial for real-time lane detection, especially when using deep learning models, which can be a barrier to widespread integration in automobiles due to high processing requirements. Figure 1: Main classification of traffic lane detection systems. Deep learning models need to be trained under a wide range of conditions, including edge cases and unusual situations, to ensure robustness and generalization across different traffic scenarios (including rare or extreme cases) [13]. Implementing a vision-based lane detection system in automobiles is challenging due to the peculiarities of the algorithms, neural network architecture, constraints, and strict hardware/software requirements [14]. Research to eliminate these deficiencies presented above has been conducted by various researchers, in various directions and through a multitude of field-specific approaches, the newest and most relevant being presented below. Increasing the robustness of lane detection, especially in various weather conditions, was studied in [15]. A new adaptive preprocessing method using a fuzzy inference system (FIS) is proposed. The algorithm dynamically adjusts the parameters in geometry-based image processing functions and improves adaptability to various weather conditions. The accuracy obtained was 95.81%, even though some limitations of the proposed algorithm appeared (by adding the module to the CLRNet neural network, an inevitable increase in processing time was observed). Research conducted in [16] focused on improving the performance of neural networks for detecting traffic lanes in foggy conditions. A data augmentation method was proposed to synthesize fog images from clear weather conditions, using 107,451 fog images synthesized based on the CULane dataset. This method was able to increase the original CULane dataset/volume with a positive effect in solving the problem of insufficient dataset for detecting traffic lanes in foggy conditions. Thus, the accuracy of the F1 score measurement of the SCNN model in different foggy weather densities was improved from 74.65, 51.41 and 11.09 to 86.65, 81.53, and 70.41, respectively. A robust multi-stage method for detecting road markings in difficult real environments was proposed in [17]. The edge detection stage proposes a two-step lane verification technique, and the lane tracking method can predict the lane position in the next frame by defining a range of horizontal lane positions (RHLP) in the next frame relative to the horizontal lane position in the previous frame. The performance evaluation of the proposed method was carried out using the DSDLDE dataset with different resolutions at 24 and 25 frames/sec. The results showed that the average detection rate is 97.55% at an average processing time of 22.33 msec/frame, which led the authors to claim that the proposed method performs better for noisy road conditions than other algorithms. Figure 2: Key steps in the lane line detection process. Lane detection based on front-facing RGB cameras has poor performance in low-light conditions.