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中一个长期存在的任务和基本模块。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、长度等相关信息及可视化。我们的工作基于CNN骨干网络DLA-34，并结合亲和场（Affinity Fields），旨在无需预设车道数量的情况下实现多种车道的鲁棒检测。此外，我们研究了新颖的解码方法，以开发更高效的车道检测算法。