# Behavior-Grounded Lane Representation Learning for Multi-Task Traffic Digital Twins

arXiv: https://arxiv.org/abs/2605.01901 · HTML: https://mlsanity.com/paper/2605.01901/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 37 references that are in the map.
- First posted on arXiv 3 May 2026.

## Builds on (most cited first, 20 of 20)

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- [TopoLogic: An Interpretable Pipeline for Lane Topology Reasoning on Driving Scenes](https://mlsanity.com/paper/2405.14747/) (2024)
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- [Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives](https://mlsanity.com/paper/2402.02968/) (2024)
- [Smart Mobility Digital Twin Based Automated Vehicle Navigation System: A Proof of Concept](https://mlsanity.com/paper/2402.12682/) (2024)
- [Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning](https://mlsanity.com/paper/2502.06870/) (2025)
- [Road Network Representation Learning: A Dual Graph based Approach](https://mlsanity.com/paper/2304.07298/) (2023)
- [Scenario Understanding of Traffic Scenes Through Large Visual Language Models](https://mlsanity.com/paper/2501.17131/) (2025)
- [Persistent Autoregressive Mapping with Traffic Rules for Autonomous Driving](https://mlsanity.com/paper/2509.22756/) (2025)
- [Unifying Lane-Level Traffic Prediction from a Graph Structural Perspective: Benchmark and Baseline](https://mlsanity.com/paper/2403.14941/) (2024)
- [Fine-Grained Traffic Inference from Road to Lane via Spatio-Temporal Graph Node Generation](https://mlsanity.com/paper/2507.19089/) (2025)
- [Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection](https://mlsanity.com/paper/2507.08743/) (2025)

## Abstract (arXiv)

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, auxiliary role supervision, and temporal anomaly detection. Across 16 roadside cameras and 132 lanes, the learned embeddings achieve a 0.004 lateral-rank error and an edge-role F1 of 1.000 in zero-shot cross-camera matching, and an AUROC of 0.991 for window-level anomaly detection. We further show that the same behavioral embeddings can condition a diffusion-based generator to synthesize lane geometries that satisfy targeted operational specifications, with 87.9% overall specification accuracy across 38 lane groups. GeoLaneRep thus provides a semantic interface between roadside observations and downstream digital twin tasks, supporting cross-camera transfer, behavior-aware monitoring, and goal-directed lane synthesis. The framework is openly available at https://github.com/raynbowy23/GeoLaneRep.