ForceFormer: Exploring Social Force and Transformer for Pedestrian Trajectory Prediction
This work addresses trajectory prediction for autonomous driving and intelligent transportation systems, but it is incremental as it builds on existing Transformer and social force concepts.
The paper tackled pedestrian trajectory prediction by integrating social forces into a Transformer-based model to address complex interactions and goal utilization, achieving on-par distance errors with state-of-the-art models while significantly reducing collisions in dense scenarios.
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.