SafeDrive-DigitalTwin: Digital Twin-Based Risk Prediction and Scenario Generation with Unified World Models for Autonomous Vehicles
Keywords:
digital twin, autonomous vehicles, world model, risk prediction, scenario generation, safety assurance, socio-technical infrastructureAbstract
Ensuring the safety of autonomous vehicles operating in complex, open-ended environments remains one of the most formidable challenges in contemporary systems engineering. Physical road testing alone cannot economically or temporally cover the combinatorial space of rare and hazardous scenarios. This paper presents SafeDrive-DigitalTwin, a conceptual architecture that fuses high-fidelity digital twin infrastructure with unified world models to enable continuous risk prediction and automated safety-critical scenario generation. The digital twin serves as a live mirror of vehicle state, environmental dynamics, and infrastructure conditions, while a unified generative world model jointly captures perception, planning, and scene evolution across diverse operational design domains. The work provides a system-level analysis of this integration, examining architectural trade-offs between edge-side lightweight twins and cloud-side comprehensive simulation engines, data governance models that span vehicle manufacturers and public agencies, and the sustainability implications of large-scale generative inference. Furthermore, the paper addresses robustness against sensor degradation and adversarial perturbations, fairness in risk allocation across demographic and geographic contexts, and the policy frameworks required to translate virtual safety evidence into regulatory acceptance. By emphasizing structural interdependencies rather than algorithmic minutiae, this discussion illuminates a pathway toward trustworthy, continuously validated autonomous mobility systems. The SafeDrive-DigitalTwin paradigm proposes that persistent synchronization of physical fleets with their digital counterparts, augmented by generative scenario expansion, can fundamentally reshape the safety assurance lifecycle.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
