论文标题
感知中心的自动驾驶系统，没有高清图
A perception centred self-driving system without HD Maps
论文作者
论文摘要
已经讨论了完全自主的自动驾驶系统已有20多年了，但仍未解决。以前的系统规模能力有限。他们的本地化子系统需要劳动密集型的地图记录才能在新区域运行，并且在环境中发生变化后的准确性降低。在本文中，提出了一种新的本地化方法来解决可扩展性问题，并通过检测和理解各种交通线的新方法。就像人类驱动器的方式一样，自动驾驶系统不应依靠在大多数情况下旅行的确切位置。结果，没有HD地图，GPS或IMU，所提出的本地化子系统仅依赖于检测周围与驾驶相关的特征（例如车道线，停止线和合并车道线）。为了发现和推理所有这些功能，建议针对多个数据集进行了新的线路检测器。
Building a fully autonomous self-driving system has been discussed for more than 20 years yet remains unsolved. Previous systems have limited ability to scale. Their localization subsystem needs labor-intensive map recording for running in a new area, and the accuracy decreases after the changes occur in the environment. In this paper, a new localization method is proposed to solve the scalability problems, with a new method for detecting and making sense of diverse traffic lines. Like the way human drives, a self-driving system should not rely on an exact position to travel in most scenarios. As a result, without HD Maps, GPS or IMU, the proposed localization subsystem relies only on detecting driving-related features around (like lane lines, stop lines, and merging lane lines). For spotting and reasoning all these features, a new line detector is proposed and tested against multiple datasets.
