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

TL;DR

GeoLaneRep introduces a behavior-grounded lane representation learning framework that encodes static and dynamic traffic information into a shared semantic space, enabling cross-camera matching, anomaly detection, and lane synthesis for traffic digital twins.

Contribution

It presents a novel joint encoding framework that captures lane geometry and behavior, supporting cross-camera transfer, anomaly detection, and goal-directed lane synthesis.

Findings

Achieves 0.004 lateral-rank error and perfect F1 in cross-camera matching.

Attains 0.991 AUROC in anomaly detection.

Synthesizes lane geometries with 87.9% accuracy for specified operational goals.

Abstract

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.