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.

翻译：摘要：在高度交互场景中基于目标信息预测行人轨迹，是实现智能交通系统与自动驾驶的关键环节。该任务面临两大核心挑战：（1）高行人密度场景下复杂的社会交互；（2）目标信息与历史运动信息的有效关联利用不足。为应对这些困难，我们将社会力融入基于Transformer的随机生成模型框架，提出新型目标导向轨迹预测器ForceFormer。与多数现有工作将终点位置简单作为输入特征不同，我们利用目标驱动力高效模拟目标对行人的引导作用。此外，引入排斥力作为另一输入特征，描述相邻行人间的避让行为。大量实验表明，本方法在距离误差指标上达到与现有最优模型相当的性能，且在广泛使用的行人数据集中显著降低碰撞率，特别是在高密度行人场景中表现尤为突出。