  Trajectory forecasting, or trajectory prediction, of multiple interacting
agents in dynamic scenes, is an important problem for many applications, such
as robotic systems and autonomous driving. The problem is a great challenge
because of the complex interactions among the agents and their interactions
with the surrounding scenes. In this paper, we present a novel method for the
robust trajectory forecasting of multiple intelligent agents in dynamic scenes.
The proposed method consists of three major interrelated components: an
interaction net for global spatiotemporal interactive feature extraction, an
environment net for decoding dynamic scenes (i.e., the surrounding road
topology of an agent), and a prediction net that combines the spatiotemporal
feature, the scene feature, the past trajectories of agents and some random
noise for the robust trajectory prediction of agents. Experiments on
pedestrian-walking and vehicle-pedestrian heterogeneous datasets demonstrate
that the proposed method outperforms the state-of-the-art prediction methods in
terms of prediction accuracy.
