Federated Learning for Vehicle Trajectory Prediction: Methodology and Benchmark Study

Hongye Wang, Ruonan Li, Zenglin Xu, JinLong Li, Irwin King, Jie Liu

Abstract: Vehicle Trajectory Prediction (VTP) plays a pivotal role in the Internet of Vehicles (IoV), significantly aiding in motion planning and accident prevention. Nonetheless, the field faces challenges in distributed data collection and trajectory privacy protection. Existing approaches often incur substantial communication overheads and are not suited for contemporary road environments. Furthermore, there is a noticeable absence of a standardized benchmark. In response to these challenges, our paper introduces an innovative Federated Learning (FL) methodology for VTP. We leverage roadside units (RSUs) as the FL clients, rather than directly using vehicles. This strategy minimizes resource consumption and suits for the current scenario where trajectories are collected by RSUs. In addition, we present FedVTP, a comprehensive benchmark for federated spatial-temporal graph solutions in VTP. FedVTP integrates various strategies and eases for future expansions. We conduct extensive experiments to evaluate the effectiveness of our approach, with detailed analyses provided within FedVTP. This benchmark further encourages the development of new FL strategies for VTP, while enabling equitable comparisons among research works in the field. To facilitate further research and collaboration, our source code will be accessible at https://github.com/FedVTP/FedVTP.