  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.
