Federated spatio-temporal graph neural network for privacy-preserving vehicle trajectory prediction in autonomous driving
TL;DR
A Federated Spatio-Temporal Synchronous Dynamic Graph Neural Network (Federated STSDGNN) framework for privacy-preserving and adaptive trajectory prediction, which achieves approximately 23.5% lower RMSE compared to the centralized STSDGNN baseline in the conducted experiments, and has the potential to support future privacy-preserving V2X (Vehicle-to-Everything) applications.
Abstract
Accurate vehicle trajectory prediction plays a vital role in autonomous driving and intelligent transport systems. Deep learning models like LSTM, CNN, GNN, etc., have shown remarkable performance but often operate in a centralized setting, aggregating raw trajectory data at the server. Furthermore, the majority of models focus on either spatial or temporal features alone, but overlook the information that can be obtained by combining spatio-temporal features. This leads to major privacy concerns, issues with centralized data, and scalability problems. To overcome these challenges, we introduce a Federated Spatio-Temporal Synchronous Dynamic Graph Neural Network (Federated STSDGNN) framework for privacy-preserving and adaptive trajectory prediction. Using the highD dataset, trajectories are segmented into spatiotemporal sequences and represented as dynamic interaction graphs. Each client (vehicle or roadside unit) locally trains an STSDGNN consisting of a pre-processing module, a spatial-temporal synchronization module (GCN/GAT with GRU) and a prediction module (CNN with MLP). Clients send only model updates, which are aggregated by the server using a federated learning algorithm. This design improves privacy, achieves approximately 23.5% lower RMSE compared to the centralized STSDGNN baseline in the conducted experiments, and has the potential to support future privacy-preserving V2X (Vehicle-to-Everything) applications.