Lane Representation

Lane representation in computer vision focuses on accurately modeling lane markings for autonomous driving and related applications. Current research emphasizes robust and efficient methods, employing various architectures including transformers, autoencoders, and curve-based models (e.g., B-splines, Bézier curves) to represent lane geometry, often incorporating techniques like deformable attention and multi-level feature integration. These advancements aim to improve the accuracy and speed of lane detection, particularly in challenging conditions like low light or occlusion, ultimately contributing to safer and more reliable autonomous navigation systems.