  Predicting trajectories of pedestrians based on goal information in highly
interactive scenes is a crucial step toward Intelligent Transportation Systems
and Autonomous Driving. The challenges of this task come from two key sources:
(1) complex social interactions in high pedestrian density scenarios and (2)
limited utilization of goal information to effectively associate with past
motion information. To address these difficulties, we integrate social forces
into a Transformer-based stochastic generative model backbone and propose a new
goal-based trajectory predictor called ForceFormer. Differentiating from most
prior works that simply use the destination position as an input feature, we
leverage the driving force from the destination to efficiently simulate the
guidance of a target on a pedestrian. Additionally, repulsive forces are used
as another input feature to describe the avoidance action among neighboring
pedestrians. Extensive experiments show that our proposed method achieves
on-par performance measured by distance errors with the state-of-the-art models
but evidently decreases collisions, especially in dense pedestrian scenarios on
widely used pedestrian datasets.
