Graph-Based Spatial-Temporal Attentive Network for Vehicle Trajectory Prediction in Automated Driving

When Automated Vehicles (AVs) navigate dynamic, interactive driving scenarios, they must consider the spatio-temporal layout of surrounding traffic, including social interactions among agents, to accurately predict their trajectories. Existing approaches often fail to handle complex inter-agent interactions with the necessary adaptive attention to dynamic contexts. This paper introduces a multi-agent trajectory prediction algorithm that leverages attentive spatio-temporal modelling to capture interactions among agents. Our approach integrates weighted Distance Graph Attention Networks (wDGAT) with dynamic attention assignment and Multi-Head Attention (MHA)-based Transformers to learn multi-headed social interaction patterns, preserving this critical information throughout the learning pipeline. This enables efficient aggregation of information from any number of neighbouring agents, allowing for robust processing of complex, time-dependent data and consistent retrieval of spatio-temporal knowledge across extended prediction horizons. We validate our model through extensive experiments on the NGSIM (US-101 and I-80) highway datasets. The results demonstrate that our approach consistently achieves the lowest prediction error over a 5-second horizon, producing diverse outcomes for different agents and outperforming state-of-the-art methods. Numerical results demonstrate that, compared with state-of-the-art models, the proposed model reduces the average prediction root-mean-square error over a five-second time horizon by 40% and achieves a median performance gain of 20% on large-scale public datasets. Ablation studies further confirm the effectiveness of our algorithm.