Smart Cloud and D2D Communication Driven Trajectories Prediction with Content Caching
Jiaxin Fan, Jun Wu · 2024
As intelligent transportation systems continue to develop, the integration of cloud-assisted device-to-device (D2D) communication and trajectory prediction has become a crucial strategy for enhancing traffic management and safety. However, D2D communication faces challenges such as bandwidth limitations and high latency, which are particularly pronounced in dense urban environments. This paper introduces a novel framework that utilizes self-attention mechanisms and edge content caching to optimize D2D communication and trajectory predictions. By leveraging cloud resources to alleviate computational burdens and employing edge caching to reduce latency and bandwidth consumption, our approach ensures that data transmission between vehicles is both fast and economical. Our innovative method facilitates real-time, accurate trajectory forecasting and efficient data communication among vehicles. We have developed a self-attention model that dynamically prioritizes relevant data points and trajectory information based on historical and contextual traffic data, thereby enabling more precise predictions and a robust communication network. To validate the effectiveness of our proposed framework, we compared its performance with traditional methods. The results demonstrate significant improvements in predictive accuracy and communication efficiency compared to conventional approaches.