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Trajectory forecasting, or trajectory prediction, of multiple interactingagents in dynamic scenes, is an important problem for many applications, suchas robotic systems and autonomous driving. The problem is a great challengebecause of the complex interactions among the agents and their interactionswith the surrounding scenes. In this paper, we present a novel method for therobust trajectory forecasting of multiple intelligent agents in dynamic scenes.The proposed method consists of three major interrelated components: aninteraction net for global spatiotemporal interactive feature extraction, anenvironment net for decoding dynamic scenes (i.e., the surrounding roadtopology of an agent), and a prediction net that combines the spatiotemporalfeature, the scene feature, the past trajectories of agents and some randomnoise for the robust trajectory prediction of agents. Experiments onpedestrian-walking and vehicle-pedestrian heterogeneous datasets demonstratethat the proposed method outperforms the state-of-the-art prediction methods interms of prediction accuracy.

Robust Trajectory Forecasting for Multiple Intelligent Agents in Dynamic Scene