Digital Twin for Context Awareness, Security, and Resilience in Vehicular Networks for 5G and Beyond: A Comprehensive Survey

Abstract

Effective vehicular network management is essential for enabling reliable communications between vehicles and infrastructure in dynamic environments. The integration of terrestrial and non-terrestrial networks (NTNs), together with the growing adoption of the Internet of Things (IoT) and diverse vehicular applications, introduces multifaceted challenges in context awareness, security, spectral resource management, and network resilience. Digital twin (DT) technology offers a promising paradigm for addressing these challenges by providing high-fidelity virtual representations of physical network entities that enable predictive simulation, what-if analysis, and proactive closed-loop control. This paper presents a comprehensive survey of DT-enabled vehicular network management. First, the bidirectional relationship between artificial intelligence (AI) and DT is explored, in which AI serves as a key enabler of DT-assisted operations by supporting intelligent sensing, state prediction, anomaly detection, and adaptive decision-making, while DT environments facilitate safe and efficient AI training and validation. Subsequently, a novel DT-driven functional taxonomy is proposed, organizing the literature by DT functionality layers (sensing, prediction, control, feedback, resilience and security), with NTNs and terrestrial networks serving as cross-layer enablers. Based on the identified research gaps, an NTN-integrated, context-aware, and multi-function DT framework is presented to support proactive, resilient, and intelligent vehicular network management for 6G and beyond. The framework addresses critical operational requirements, including DT state modeling and prediction, DT synchronization in dynamic environments, and integrated multilevel data management and computing. Two illustrative case studies are discussed, focusing on DT-assisted radio resource management under channel-modeling uncertainty and DT synchronization under constrained communication resources. Finally, open research challenges and future directions are outlined toward scalable, interoperable, and trustworthy DTs for vehicular networks.