A Database-Centered Geospatial Infrastructure for Autonomous Vehicle Digital Twins: A Framework Integrating Semantic ODD Layers, Runtime Telemetry, and 3D Urban Context

Abstract

Autonomous Vehicle (AV) Digital Twins require persistent geospatial infrastructures that connect semantic, spatial, temporal, environmental, road-reference, and 3D urban information across simulation and visualization. This study proposes a database-centered framework integrating PostGIS-managed Operational Design Domain (ODD) layers, runtime telemetry, CityGML-based 3D context, an XODR-derived road reference, live environmental observations, and service-mediated delivery through CARLA, FastAPI/WebSocket, Mission Control, and Cesium. Evaluation comprised 30 Route A runs across six background-vehicle densities (0–40) with five paired-seed repetitions per density, plus five Route B repetitions for cross-route functional repeatability. Route A generated 174,214 source states and achieved 100% database-write success, with mean run-level write latency of 1.950 ± 0.055 ms. Mean REST V2V round-trip time increased from 1.388 ms at BG = 0 to 2.379 ms at BG = 40, while background-actor population maintenance averaged 99.975% across nonzero-density runs. Multi-layer ODD overlap occurred in 10.55% of Route A states, with up to three simultaneous contexts. All evaluated road-reference states remained internally ON TRACK under the implementation-specific diagnostic. Route B reproduced the core persistence, semantic, spatial, and traceability functions under a distinct route-exposure pattern. These results support the framework as a reproducible geospatial backbone for AV-DT simulation and analysis within the tested simulation and hardware conditions.