Age-of-Information-Aware Hierarchical Collaborative Inference in Digital Twin-Enabled Vehicular Edge Computing

Digital twin-enabled vehicular edge computing leverages virtual representations of physical vehicles to facilitate real-time intelligent services such as collaborative perception, trajectory prediction, fault diagnosis, and proactive decision-making through seamless integration of sensing, computing, and communication resources. The effectiveness of these services critically depends on the timely execution of deep learning inference tasks. However, vehicular mobility and dynamic contention for limited edge resources lead to significant delays in inference execution, thereby degrading the accuracy of digital twin-assisted decision-making. To address this challenge, we propose a hierarchical framework for collaborative inference and digital twin deployment. At the upper layer, a mobility-aware strategy for inference task offloading and digital twin deployment is employed, which incorporates dynamic action-space pruning to select edge nodes based on vehicular mobility patterns and link stability. At the lower layer, a delay-resource gradient-based optimization mechanism is designed to adaptively allocate computational resources by matching dynamic inference demands with available edge capacity. Extensive simulations demonstrate the effectiveness of the proposed approach, showing a reduction of at least 15% in Age of Information (AoI) compared to the baseline methods. Moreover, our approach significantly improves the inference efficiency and system resource utilization, and reduces the deadline violation rate.