Lane Road Segmentation Based on Improved UNet Architecture for Autonomous Driving

This paper introduces a real-time workflow for implementing neural networks in the context of autonomous driving. The UNet architecture is specifically selected road segmentation due to its strong performance and low complexity. To further improve model's capabilities, Local Binary Convolution (LBC) incorporated into skip connections, enhancing feature extraction, elevating Intersection over Union (IoU) metric. evaluation model focuses on detection, utilizing IOU Two datasets are used training validation: widely KITTI dataset custom collected within ROS2 environment. Simulation validation performed both assess our model. demonstrates an impressive IoU score 97.90% segmentation. Moreover, when evaluated dataset, achieves 98.88%, which comparable conventional models. Our proposed method reconstruct structure provide input extraction can effectively existing lane methods.