Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection

• URL: http://arxiv.org/abs/2507.08743v1
• Date: Fri, 11 Jul 2025 16:45:59 GMT
• Title: Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection
• Authors: Rei Tamaru, Pei Li, Bin Ran,
• Abstract summary: Geo-ORBIT is a unified framework that combines real-time lane detection, DT synchronization, and federated meta-learning.<n>We extend this model through Meta-GeoLane, which learns to personalize detection parameters for local entities.<n>Our system is integrated with CARLA and SUMO to create a high-fidelity DT that renders highway scenarios and captures traffic flows in real-time.
• Score: 17.09138102827048
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Digital Twins (DT) have the potential to transform traffic management and operations by creating dynamic, virtual representations of transportation systems that sense conditions, analyze operations, and support decision-making. A key component for DT of the transportation system is dynamic roadway geometry sensing. However, existing approaches often rely on static maps or costly sensors, limiting scalability and adaptability. Additionally, large-scale DTs that collect and analyze data from multiple sources face challenges in privacy, communication, and computational efficiency. To address these challenges, we introduce Geo-ORBIT (Geometrical Operational Roadway Blueprint with Integrated Twin), a unified framework that combines real-time lane detection, DT synchronization, and federated meta-learning. At the core of Geo-ORBIT is GeoLane, a lightweight lane detection model that learns lane geometries from vehicle trajectory data using roadside cameras. We extend this model through Meta-GeoLane, which learns to personalize detection parameters for local entities, and FedMeta-GeoLane, a federated learning strategy that ensures scalable and privacy-preserving adaptation across roadside deployments. Our system is integrated with CARLA and SUMO to create a high-fidelity DT that renders highway scenarios and captures traffic flows in real-time. Extensive experiments across diverse urban scenes show that FedMeta-GeoLane consistently outperforms baseline and meta-learning approaches, achieving lower geometric error and stronger generalization to unseen locations while drastically reducing communication overhead. This work lays the foundation for flexible, context-aware infrastructure modeling in DTs. The framework is publicly available at https://github.com/raynbowy23/FedMeta-GeoLane.git.

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