Lane detection is a long-standing task and a basic module in autonomous driving. The task is to detect the lane of the current driving road, and provide relevant information such as the ID, direction, curvature, width, length, with visualization. Our work is based on CNN backbone DLA-34, along with Affinity Fields, aims to achieve robust detection of various lanes without assuming the number of lanes. Besides, we investigate novel decoding methods to achieve more efficient lane detection algorithm.

翻译：车道检测是自动驾驶领域中一项长期存在的任务，也是其基础模块之一。该任务旨在检测当前行驶道路的车道，并提供车道ID、方向、曲率、宽度、长度等可视化相关信息。我们的工作基于CNN骨干网络DLA-34，并结合Affinity Fields，旨在无需预设车道数量的前提下实现鲁棒的多类型车道检测。此外，我们研究了新的解码方法，以构建更高效的车道检测算法。