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:车道检测是自主驾驶中的一项长期任务和基本模块。该任务是检测当前驾驶道路的车道,并提供相关信息,如标识、方向、曲率、宽度、长度和可视化等。我们的工作基于CNN骨干网络DLA-34和Affin Fields,旨在实现对各种车道的稳健检测,而不假设车道数量。此外,我们还研究了新的解码方法,实现更高效的车道检测算法。