  A map, as crucial information for downstream applications of an autonomous
driving system, is usually represented in lanelines or centerlines. However,
existing literature on map learning primarily focuses on either detecting
geometry-based lanelines or perceiving topology relationships of centerlines.
Both of these methods ignore the intrinsic relationship of lanelines and
centerlines, that lanelines bind centerlines. While simply predicting both
types of lane in one model is mutually excluded in learning objective, we
advocate lane segment as a new representation that seamlessly incorporates both
geometry and topology information. Thus, we introduce LaneSegNet, the first
end-to-end mapping network generating lane segments to obtain a complete
representation of the road structure. Our algorithm features two key
modifications. One is a lane attention module to capture pivotal region details
within the long-range feature space. Another is an identical initialization
strategy for reference points, which enhances the learning of positional priors
for lane attention. On the OpenLane-V2 dataset, LaneSegNet outperforms previous
counterparts by a substantial gain across three tasks, \textit{i.e.}, map
element detection (+4.8 mAP), centerline perception (+6.9 DET$_l$), and the
newly defined one, lane segment perception (+5.6 mAP). Furthermore, it obtains
a real-time inference speed of 14.7 FPS. Code is accessible at
https://github.com/OpenDriveLab/LaneSegNet.
