FDG-VTP: A Fully Decentralized Gossip Vehicular Trajectory Prediction Model

Vehicular ad hoc networks (VANETs) play a crucial role in modern intelligent transportation systems by enabling real-time communication between vehicles. To enhance vehicle trajectory prediction in VANETs, increasingly advanced methods are being developed, aiming to improve traffic efficiency and road safety. Resilient and low-cost communication algorithms are also being proposed. However, these methods focus solely on the accuracy of the predictions, reducing communication costs, or improving convergence. In this study, we propose a method that provides both highly accurate trajectory prediction and an efficient fully decentralized communication algorithm with very low overhead, rapid convergence, and reduced computational costs. The proposed prediction approach is based on the Transformer model adapted to enhance trajectory prediction accuracy, while reducing computational and communication overhead. Regarding the communication algorithm, the gossip learning approach is adopted using an aggregation algorithm based on the reliability of the received models and a communication scheme based on a frequency varying according to the progress of local training iterations within each target vehicle. Evaluations on the NGSIM US 101 and US I80 datasets demonstrate that the proposed method outperforms the comparison models in terms of higher prediction accuracy, while also ensuring lower communication costs and computational times compared to both centralized and fully decentralized state-of-the-art models, all within a fully decentralized environment.