In highly dynamic and interactive vehicular environments, accurate and efficient trajectory prediction is critical for driving safety and traffic efficiency. To overcome the limitations of existing methods, including degradation of long-range historical context, high computational cost for long sequences, attention dis...
Qiong Shi, Xin-Jie-Pu-,-Ping-Jing-Shao-,-Yan-Meng-Zhang-,-Sen Zhang, Hong Zhao et al.· Italian National Conference...· 0 citations
Vehicular edge computing (VEC) faces critical challenges regarding limited onboard computational capacity and the potential for location privacy leakage. To address these issues, this paper proposes a novel, privacy-aware task offloading framework driven by deep reinforcement learning (DRL). First, we design a dynamic...
Experimental results show that, compared with the best-performing baseline in terms of RMSE for each forecasting task, TA-STGAT reduces RMSE by 10.89% and 11.29% in workload-representation prediction and traffic flow prediction, respectively, demonstrating its effectiveness for short-term edge-state forecasting.
