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
this https URL

基于车道几何距离和车道查询相似性的车道拓扑推理方法 TopoLogic 在车道拓扑方面提供更全面的信息，显著优于现有的方法，并且可以在无需重新训练的情况下将几何距离车道拓扑推理方法应用于经过充分训练的模型中，大大提升车道拓扑推理性能。

TopoLogic：用于驾驶场景上车道拓扑推理的可解释性流程

TopoLogic: An Interpretable Pipeline for Lane Topology Reasoning on  Driving Scenes

RGB-T semantic segmentation is a key technique for autonomous driving scenes
understanding. For the existing RGB-T semantic segmentation methods, however,
the effective exploration of the complementary relationship between different
modalities is not implemented in the information interaction between multiple
levels. To address such an issue, the Context-Aware Interaction Network
(CAINet) is proposed for RGB-T semantic segmentation, which constructs
interaction space to exploit auxiliary tasks and global context for explicitly
guided learning. Specifically, we propose a Context-Aware Complementary
Reasoning (CACR) module aimed at establishing the complementary relationship
between multimodal features with the long-term context in both spatial and
channel dimensions. Further, considering the importance of global contextual
and detailed information, we propose the Global Context Modeling (GCM) module
and Detail Aggregation (DA) module, and we introduce specific auxiliary
supervision to explicitly guide the context interaction and refine the
segmentation map. Extensive experiments on two benchmark datasets of MFNet and
PST900 demonstrate that the proposed CAINet achieves state-of-the-art
performance. The code is available at this https URL

RGB-T 语义分割技术在自动驾驶场景理解中起着关键作用。本研究提出了一种用于 RGB-T 语义分割的上下文感知交互网络（CAINet），通过建立交互空间来利用辅助任务和全局上下文进行显式引导学习。通过引入上下文感知的互补推理模块、全局上下文建模模块和细节聚合模块，以及特定辅助监督，对多模态特征之间的互补关系进行建模，进一步提高分割效果。对 MFNet 和 PST900 两个基准数据集进行的大量实验表明，CAINet 能够达到最先进的性能。