Multi-Agent Trajectory Prediction with Graph Attention Isomorphism Neural Network

Abstract

Multi-agent trajectory prediction is a challenging task because of the uncertainty of agents' behaviors, interactions between agents, complex road geometry in urban environments, and imperfect/noisy agent histories. Although accurate prediction results are critical for safe and reliable intelligent driving applications (e.g., decision making, motion planning), some other applications may prefer light-weight and computation-efficient trajectory prediction models to handle dynamically changed environments. In this work, we propose a multi-agent, multi-modal Graph Attention Isomorphism Network (GAIN) based trajectory prediction framework to effectively understand and aggregate long-term interactions across agents. We also take the model complexity and computation efficiency into consideration. Experiments on both pedestrian and vehicle datasets demonstrated the effectiveness of our proposed method.