IoT-Enabled Cooperative Autonomous Driving: A Hierarchical Spatial–Temporal Transformer Framework for Trajectory Prediction

Accurate trajectory prediction for surrounding agents is of paramount importance for autonomous vehicles (AVs). Given that a single AV’s perception systems may face challenges in detecting and predicting agents that are occluded or located at relatively long distances, the Internet of Things (IoT) has enabled cooperative autonomous driving through networked sensing and communication. In this context, vehicle-infrastructure cooperative (VIC) solutions have emerged as a promising approach for accurate trajectory prediction. Therefore, this study proposes a hierarchical spatial-temporal transformer framework for VIC trajectory prediction. The framework employs an Encoder-Decoder architecture that takes trajectory sequences from different views and vectorized maps as inputs. The encoder leverages multi-head attention mechanisms and VIC fusion module to extract spatial-temporal interaction features and aggregate cross-view information, while the decoder generates multimodal trajectory predictions in a sequential manner. Specifically, the spatial-temporal transformer in this study adopts a two-level hierarchical structure: agent-level and block-level. The agent-level transformer executes temporal encoding and interaction feature extraction from each agent’s historical trajectory sequences while aggregating features from both ego-vehicle and infrastructure views. The block-level transformer alternately extracts spatial-temporal interaction features across blocks, capturing long-range temporal dependencies and maintaining longterm spatial-temporal coherence. Experimental analysis on real-world vehicle-infrastructure cooperative datasets demonstrates that the proposed method effectively utilizes VIC data to enhance trajectory prediction accuracy. Moreover, our proposed method achieves favorable performance even in challenging scenarios involving occlusion, long distances, and sparse trajectories, which could serve as an efficient approach for cooperative autonomous driving.