Enhancing Lane Recognition in Autonomous Vehicles Using Cross-Layer Refinement Network

Lanes are different sections of a roadway that are marked or assigned to traffic movement. They are used to organize and govern the passage of vehicles on roads. A lane is essential for the visual navigation system of an autonomous vehicle. The concept of a lane represents a traffic sign that has significant meaning. However, it also exhibits a unique local pattern that requires detailed low-level characteristics to identify its location accurately. Utilizing various feature levels is crucial for achieving effective lane recognition. Therefore, this study used a Cross-Layer Refinement Network (CLRNet) to enhance lane recognition by incorporating high and low-level lane-detecting characteristics. This approach involves identifying lanes based on high-level semantic properties, and refining them using low-level features. The proposed method aims to improve the localization accuracy by leveraging additional contextual information and local-specific lane characteristics. The network architecture combines the elements of LeNet-5 and AlexNet, utilizing the more profound architecture of AlexNet for complex feature learning and localized pattern recognition from LeNet-5. A global context is acquired to enhance the lane feature representation. The Line Intersection over Union (LIoU) loss function, which treats the lane line as a whole unit rather than as individual segments, is employed to enhance the localization accuracy. The experimental results demonstrate the superior performance of the proposed method compared to existing state-of-the-art lane detection algorithms.