Lane Dataset

Lane detection datasets are crucial for training and evaluating autonomous driving systems, focusing on accurately identifying lane markings in diverse conditions. Current research emphasizes improving robustness to occlusions, challenging viewpoints (including cross-lane scenarios), and adverse weather or lighting, often employing deep learning models with memory-based refinement, Gaussian splatting for view synthesis, and row-wise processing of LiDAR data. These advancements are vital for enhancing the safety and reliability of autonomous vehicles by providing more comprehensive and realistic training data for perception algorithms.