Cloud-Enabled Vehicular Digital Twins for Real-Time Vehicle Intelligence
Keywords:
Automotive cloud, Connected vehicle, Digital twin, Edge computing, Internet of vehicles, Predictive maintenance, Software-Defined vehicle, Synchronization, V2XAbstract
Connected vehicles generate a continuous but uneven stream of operational evidence. This paper develops a cloud-native, edge-aware reference architecture for a VIN-bound vehicular Digital Twin and defines Age of Vehicular Twin (AoVT) as an application-facing freshness measure. The proposed hybrid synchronization policy combines state deviation, an AoVT ceiling, critical-event priority, uncertainty, and rate control while preserving local authority for safety-critical control. Trace-driven replay of 30 minutes (18,001 samples at 10 Hz) from the public USDOT MMITSS connected-vehicle field-test data compares 1 s periodic, send-on-change, AoVT-only, and hybrid policies under a fixed 0.1 s communication-and-processing delay. With a 2 km/h deviation threshold, 2 s AoVT ceiling, 0.5 s minimum inter-update interval, and 100-byte message model, the hybrid policy reduced traffic by 29.9% versus periodic upload while maintaining zero AoVT violations and a 1.9 s 95th-percentile AoVT; its speed MAE was 2.43 km/h. These results provide bounded trace-driven validation rather than a universal deployment guarantee. The architecture also formalizes state, provenance, assumptions, safety boundaries, and evaluation metrics for maintenance, battery intelligence, fleet services, and governed OTA workflows.
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