Geometry-Guided Representations for Coherent Lane and Traffic Topology Reasoning in Driving Scenes

2026-07-27 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

RelTopo, a new model by researchers from FNii, CUHK-Shenzhen, HKUST-Guangzhou, and Tencent Map T-Lab, significantly enhances road topology reasoning for autonomous driving. It introduces relational modeling into both lane perception and topology reasoning, addressing limitations in existing methods. Key components include a relation-aware lane detector with geometry-biased self-attention and curve-guided cross-attention, alongside relation-enhanced topology heads for Lane-to-Lane (L2L) and Lane-to-Traffic-element (L2T) relationships. A contrastive learning strategy further refines relationship embeddings. Evaluated on OpenLane-V2, RelTopo achieves leading performance, improving DETl by +3.1, TOPll by +5.3, TOPlt by +4.9, and overall OLS by +4.4 on subsetA, with similar gains on subsetB.

Key takeaway

For AI Scientists and Machine Learning Engineers developing autonomous driving systems, this research suggests prioritizing explicit relational modeling in perception and reasoning pipelines. Your systems can achieve more robust lane detection and topology understanding by integrating geometry-biased attention and cross-view feature fusion. Consider implementing contrastive learning with hard negative mining to refine relational embeddings, potentially leading to significant performance gains on benchmarks like OpenLane-V2.

Key insights

Relational modeling, integrating geometric and contextual cues, significantly improves autonomous driving scene perception and topology reasoning.

Principles

• Explicitly encoding geometric relationships enhances lane perception.
• Cross-view fusion aligns disparate feature spaces for robust reasoning.
• Contrastive learning refines discrimination of relational pairs.

Method

RelTopo uses a relation-aware lane decoder with geometry-biased self-attention and curve-guided cross-attention, plus geometry-enhanced L2L and X-view L2T reasoning modules, all refined by InfoNCE contrastive loss.

In practice

• Adopt Bézier curve representation for compact lane modeling.
• Integrate inter-lane distances and angular differences as attention biases.
• Employ hard negative mining with InfoNCE for topology learning.

Topics

• Autonomous Driving
• Road Topology Reasoning
• Lane Detection
• Relational Modeling
• Self-Attention Mechanisms
• Contrastive Learning
• OpenLane-V2

Code references

• OpenDriveLab/OpenLane-V2

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.