  As an emerging task that integrates perception and reasoning, topology
reasoning in autonomous driving scenes has recently garnered widespread
attention. However, existing work often emphasizes "perception over reasoning":
they typically boost reasoning performance by enhancing the perception of lanes
and directly adopt MLP to learn lane topology from lane query. This paradigm
overlooks the geometric features intrinsic to the lanes themselves and are
prone to being influenced by inherent endpoint shifts in lane detection.
  To tackle this issue, we propose an interpretable method for lane topology
reasoning based on lane geometric distance and lane query similarity, named
TopoLogic.
  This method mitigates the impact of endpoint shifts in geometric space, and
introduces explicit similarity calculation in semantic space as a complement.
By integrating results from both spaces, our methods provides more
comprehensive information for lane topology.
  Ultimately, our approach significantly outperforms the existing
state-of-the-art methods on the mainstream benchmark OpenLane-V2 (23.9 v.s.
10.9 in TOP$_{ll}$ and 44.1 v.s. 39.8 in OLS on subset_A. Additionally, our
proposed geometric distance topology reasoning method can be incorporated into
well-trained models without re-training, significantly boost the performance of
lane topology reasoning. The code is released at
https://github.com/Franpin/TopoLogic.
