Title: RelTopo: Multi-Level Relational Modeling for Driving Scene Topology Reasoning
Primary area: applications to robotics, autonomy, planning
Keywords: autonomous driving, lane topology reasoning, scene understanding

Abstract: Accurate road topology reasoning is critical for autonomous driving, as it requires both perceiving road elements and understanding how lanes connect to each other (L2L) and to traffic elements (L2T). Existing methods often focus on either perception or L2L reasoning, leaving L2T underexplored and fall short of jointly optimizing perception and reasoning. Moreover, although topology prediction inherently involves relations, relational modeling itself is seldom incorporated into feature extraction or supervision. As humans naturally leverage contextual relationships to recognize road element and infer their connectivity, we posit that relational modeling can likewise benefit both perception and reasoning, and that these two tasks should be mutually enhancing. To this end, we propose RelTopo, a multi-level relational modeling approach that systematically integrates relational cues across three levels: **1) perception-level:** a relation-aware lane detector with geometry-biased self-attention and \curve\ cross-attention enriches lane representations;  **2) reasoning-level:** relation-enhanced topology heads, including a geometry-enhanced L2L head and a cross-view L2T head, enhance topology inference via relational cues; and **3) supervision-level:** a contrastive InfoNCE strategy regularizes relational embeddings. This design enables perception and reasoning to be optimized jointly. Extensive experiments on OpenLane-V2 demonstrate that RelTopo significantly improves both detection and topology reasoning, with gains of +3.1 in $\text{DET} _ l$, +5.3 in $\text{TOP} _ {ll}$, +4.9 in $\text{TOP} _ {lt}$, and +4.4 overall in OLS, setting a new state-of-the-art. Code will be released.

TL;DR: This work improves road topology reasoning by introducing relational modeling in both lane perception and topology reasoning, along with a contrastive learning strategy. Experiments show SOTA results with significant improvements.