发表机构
Bosch Research North America; Bosch Center for Artificial Intelligence (BCAI); Massachusetts Institute of Technology; Bosch XC China（博世北美研究院; 博世人工智能中心; 麻省理工学院; 博世中国XC部门）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自动驾驶场景拓扑连接推理的性能差距与拓扑图不可靠问题，提出TopoEnhance框架，通过去噪重建提升拓扑指标，无需重训练即可增强现有基准性能。
AI 中文摘要
在自动驾驶中，理解场景拓扑——即车道与交通元素之间的连接关系——对安全路径规划和运动控制至关重要。尽管当前方法在检测单个地图元素方面表现出色，但它们的连接推理往往未能达到其理论潜力，与基于底层检测可实现的理论上限存在显著性能差距。此外，传递给下游任务的可用于决策的拓扑图通常仍不可靠。当前方法通常通过对连续拓扑分数设置阈值来推导连接性；然而，这些分数往往无法反映连接的真实逻辑可能性，导致假阳性或缺失连接。现有基准进一步忽略了这一问题，主要评估连续指标，而非评估决策所需的离散连接性。为弥合这些差距，我们提出TopoEnhance，一种新颖的拓扑增强框架，旨在释放现有方法的潜在潜力并提高可用于决策的拓扑的可靠性。我们将拓扑增强表述为基于去噪的重建过程，其中模型从随机损坏的真值图中学习恢复结构一致性。这种表述使模型能够解决逻辑不一致并纠正不可靠的连接，生成接近理论最大性能的稳健离散拓扑图。在不同基准上进行的大量实验表明，TopoEnhance始终提高了连续拓扑指标（TOP分数）以及我们调整后的拓扑雅可比相似度（TJS）指标测量的离散连接性。作为一种灵活的、与源无关的框架，TopoEnhance在各种最先进的基准上均实现了显著提升，无需重新训练。
英文摘要
In autonomous driving, understanding scene topology - the connectivity between lanes and traffic elements - is critical for safe path planning and motion control. While current methods excel at detecting individual map elements, their connectivity reasoning often falls short of its theoretical potential, leaving a significant performance gap relative to the theoretical upper-bound achievable given the underlying detections. Furthermore, the decision-ready topology graphs passed to downstream tasks often remain unreliable. Current approaches typically derive connectivity by thresholding continuous topology scores; however, these scores often fail to reflect the true logical likelihood of connectivity, resulting in false positives or missing connections. Existing benchmarks further overlook this issue by primarily evaluating continuous metrics, rather than assessing the discrete connectivity required for decision-making. To bridge these gaps, we propose TopoEnhance, a novel topology enhancement framework designed to unlock the latent potential of existing methods and improve the reliability of decision-ready topology. We formulate topology enhancement as a denoising-based reconstruction process, where the model learns to recover structural consistency from stochastically corrupted ground-truth graphs. This formulation enables the model to resolve logical inconsistencies and rectify unreliable connections, producing robust discrete topology graphs that closely approach theoretical maximum performance. Extensive experiments across different baselines show that TopoEnhance consistently improves both continuous topology metrics (TOP score), and discrete connectivity measured by our adapted Topology Jaccard Similarity (TJS) metric. As a flexible, source-agnostic framework, TopoEnhance delivers substantial gains across diverse state-of-the-art baselines without requiring retraining.
