With the rapid development of intelligent transportation systems, multimodal applications such as autonomous driving and real-time video analytics are increasingly common. Cloud-edge-end computing has emerged as a promising solution to support these latency-sensitive tasks through distributed computing and edge content delivery. However, in urban hotspot areas, limited resources and frequent backhaul transmissions degrade network performance. Optimizing content caching to reduce task execution remains a key challenge. To address this, Unmanned Aerial Vehicles (UAVs) are introduced into vehicular networks due to their low cost and high mobility, serving as aerial base stations to assist ground infrastructure. We propose an cloud-edge-end collaborative caching framework, deploying algorithms on UAVs with computing and storage capabilities, working with Roadside Units (RSUs) and idle vehicles to alleviate resource constraints in hotspots. Within this framework, we apply the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm for UAV deployment optimization. Then, we propose a content request prediction model using Bidirectional Gated Recurrent Unit (Bi-GRU) and attention mechanisms. Finally, a content caching algorithm based on Soft Asynchronous Advantage Actor-Critic with Action Mask Module (SA3C-AM) is introduced to minimize latency. Experimental results show that compared to baseline methods, our approach improves cache hit rate by 15.4%, reduces content fetches by 34.08%, and lowers average request latency by 17.6%.
Tang et al. (Wed,) studied this question.