VGA: A Virtual-interaction-force Graph Attention Model for Agent Trajectory Prediction in Traffic Scenarios

Trajectory prediction for all road participants constitutes a crucial module in decision-making processes. The primary challenge lies in comprehending the interactions between these agents. Conventional models typically rely on historical trajectories of agents to discern their interactions, often falling short of capturing nuanced expertise. This paper introduces VGA, a novel trajectory prediction model based on Virtual Interaction Force (VIF) and a Graph Attention Network. VGA considers the VIF between agents, employing force a feature vector expressing the impact intensity among agents to encapsulate their interactions. Notably, the model utilizes a graph module to transform raw input into an adjacency matrix. The map encoder and VIF encoder leverage an attention network to extract the agent-lane and agent-VIF relationships. Subsequently, the decoder derives features encompassing the agent-map and agent-VIF relationships, facilitating the prediction of future agent trajectories. Experiment results validate VGA’s ability to yield precise predictions in multi-modal trajectory tasks across the Argoverse 2 Motion Forecast dataset and the Suzhou intersection trajectory dataset. Furthermore, the results of ablation experiment also verify that VIF encoder enhances prediction accuracy.