Lane Detection Benchmark

Lane detection benchmarks evaluate algorithms that automatically identify lane markings in images and videos, a crucial task for autonomous driving. Current research focuses on improving accuracy and robustness across diverse conditions, exploring architectures like Transformers and those based on Bézier curves or keypoint detection to represent lane shapes more effectively, and developing self-supervised learning methods to reduce reliance on labeled data. These advancements are vital for enhancing the safety and reliability of autonomous vehicles and contribute significantly to the broader field of computer vision.