Behavior-Grounded Lane Representation Learning for Multi-Task Traffic Digital Twins
Traffic digital twins are powerful tools for advanced traffic management, and most systems are built on static geometric representations. However, these representations fail to capture the dynamic functional semantics required for behavior-aware reasoning, such as how a lane operates under complex traffic conditions. To address this gap, we introduce GeoLaneRep, a behavior-grounded lane representation learning framework for traffic digital twins. GeoLaneRep jointly encodes static lane geometry, observed vehicle trajectories, and operational descriptors into a shared, cross-camera semantic embedding. The encoder is trained with a joint objective combining contrastive cross-camera alignment...
| Authors | Rei Tamaru, Pei Li, Bin Ran |
|---|---|
| Primary category | cs.CV |
| Published | 3 May 2026 |
| Last updated at source | 3 May 2026 |
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