论文标题
多车道检测
Multi Lane Detection
论文作者
论文摘要
车道检测是一项长期的任务，也是自动驾驶中的基本模块。任务是检测当前驾驶道路的车道，并提供相关信息，例如ID，方向，曲率，宽度，长度以及可视化。我们的工作基于CNN骨干DLA-34，以及亲和力场，旨在在不假设车道数量的情况下实现对各种车道的强大检测。此外，我们研究了新的解码方法，以实现更有效的车道检测算法。
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.
