论文标题
在缺失的特征方案中，地图增强的自我传播检测
Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios
论文作者
论文摘要
作为自主驾驶系统中最重要的任务之一，自我驾驶系统已进行了广泛的研究，并在许多情况下取得了令人印象深刻的结果。但是，在缺失的特征方案中，自我车道检测仍然是一个未解决的问题。为了解决这个问题，以前的方法已致力于提出更复杂的特征提取算法，但是它们非常耗时，无法处理极端情况。本文与其他文章不同，利用了数字地图中包含的先验知识，该知识具有强大的能力，可以增强检测算法的性能。具体而言，我们采用从OpenStreetMap提取的道路形状作为车道模型，这与真实车道形状高度一致，与车道特征无关。这样，只需要几个车道功能来消除道路形状和真实车道之间的位置误差，并提出了基于搜索的优化算法。实验表明，该方法可以应用于各种情况，并且可以以20 Hz的频率实时运行。同时，我们在公共Kitti Lane数据集上评估了提出的方法，在该数据集中可以实现最先进的性能。此外，我们的代码将在出版后成为开源。
As one of the most important tasks in autonomous driving systems, ego-lane detection has been extensively studied and has achieved impressive results in many scenarios. However, ego-lane detection in the missing feature scenarios is still an unsolved problem. To address this problem, previous methods have been devoted to proposing more complicated feature extraction algorithms, but they are very time-consuming and cannot deal with extreme scenarios. Different from others, this paper exploits prior knowledge contained in digital maps, which has a strong capability to enhance the performance of detection algorithms. Specifically, we employ the road shape extracted from OpenStreetMap as lane model, which is highly consistent with the real lane shape and irrelevant to lane features. In this way, only a few lane features are needed to eliminate the position error between the road shape and the real lane, and a search-based optimization algorithm is proposed. Experiments show that the proposed method can be applied to various scenarios and can run in real-time at a frequency of 20 Hz. At the same time, we evaluated the proposed method on the public KITTI Lane dataset where it achieves state-of-the-art performance. Moreover, our code will be open source after publication.
