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:通道探测是一项长期的任务,也是自主驾驶的基本模块。 任务是探测当前行驶道路的车道,提供身份、方向、曲线、宽度、长度、长度等相关信息,并进行可视化。 我们的工作以CNN主干线DLA-34和“亲近场”为基础,目的是在不假定车道数量的情况下对各行道进行强力探测。 此外,我们还调查新的解码方法,以实现更有效的通道探测算法。