FSGA: Motion Prediction Method Based on Future Scene Graph Attention

Accurately predicting the trajectories of surrounding agents is critically important for the safe operation of autonomous vehicles. However, complex environments not only increase the computational cost but also introduce multimodality in the agent’s future motions, presenting significant challenges for motion prediction. Therefore, we propose FSGA, a trajectory prediction framework that constructs a relationship graph between agents and lanes in future spacetime and utilizes a graph attention mechanism to analyze the interactions between these future spacetime agents and lanes. We employ a vectorized representation method to model the traffic scene as a graph. Through this vectorized scene representation, FSGA extracts spatio-temporal features from agents’ historical trajectories, including interactions between agents, and between agents and lanes. To reduce computational costs, a lane scoring mechanism is employed, allowing the model to focus only on the lanes that have a significant impact on the current agent’s motion. To address complexity and multimodality of future trajectories, we introduce a branching tree goal prediction structure that models multiple possible motion trends by representing waypoints and endpoints at different temporal stages. Based on these predicted goals, we construct a relationship graph that captures the associations between agents and lanes in future spacetime and obtains the interactions between agents and lanes, improving trajectory prediction accuracy. Experiments show that FSGA achieves state-of-the-art performance on Argoverse1.