Safe motion planning in advanced driver-assistance systems and autonomous vehicles requires an accurate understanding of how the surrounding traffic scene is likely to evolve. However, many existing lane-change prediction methods focus on a single target vehicle, while multi-agent forecasting approaches often describe scene evolution only through future positions, providing limited explicit information about each vehicle's maneuver.
This study proposes a dynamic scene graph attention framework that predicts the lane-change intention and future trajectory of every relevant vehicle within a local traffic scene. The scene is represented as a time-varying interaction graph where vehicles are modeled as nodes, and their spatial and kinematic relationships are encoded through explicit edge features.
Temporal graph-attention message passing captures evolving inter-vehicle dependencies and pre-maneuver cues, while an intention-guided decoder links each predicted maneuver to its corresponding future motion. A scene-level consistency objective further encourages compatible multi-vehicle futures.
Experiments on the NGSIM I-80, NGSIM US-101, and highD datasets demonstrate consistent improvements over competing baselines. DSiGAT achieves intention prediction accuracies of 90.12% and 90.97% on NGSIM I-80 and US-101, respectively, and reduces trajectory RMSE by up to 52.94% relative to the strongest baseline.
It also produces lower inter-agent collision rates and joint displacement errors, indicating more coherent scene-level predictions. Ablation, sensitivity, robustness, and qualitative analyses further validate the contribution of the proposed components and the effectiveness of the scene-focused formulation.
Blogger's Review: This study introduces a dynamic scene graph attention framework, providing a novel perspective on lane-change intention and trajectory prediction for multiple vehicles. It significantly enhances prediction accuracy and safety, which is crucial for the advancement of autonomous driving technologies. The innovative methodology effectively combines temporal factors with multi-agent interactions, warranting further attention and research.