The inherently diverse and uncertain nature of trajectories presents a formidable challenge in accurately modeling them. Motion prediction systems must effectively learn spatial and temporal information from the past to forecast the future trajectories of the agent. Many existing methods learn temporal motion via separate components within stacked models to capture temporal features. This paper introduces a novel framework, called Temporal Waypoint Dropping (TWD), that promotes explicit temporal learning through the waypoint dropping technique. Learning through waypoint dropping can compel the model to improve its understanding of temporal correlations among agents, thus leading to a significant enhancement in trajectory prediction. Trajectory prediction methods often operate under the assumption that observed trajectory waypoint sequences are complete, disregarding real-world scenarios where missing values may occur, which can influence their performance. Moreover, these models frequently exhibit a bias towards particular waypoint sequences when making predictions. Our TWD is capable of effectively addressing these issues. It incorporates stochastic and fixed processes that regularize projected past trajectories by strategically dropping waypoints based on temporal sequences. Through extensive experiments, we demonstrate the effectiveness of TWD in forcing the model to learn complex temporal correlations among agents. Our approach can complement existing trajectory prediction methods to enhance prediction accuracy. We also evaluate our proposed method across three datasets: NBA Sports VU, ETH-UCY, and TrajNet++.

翻译：轨迹固有的多样性和不确定性为精确建模带来了严峻挑战。运动预测系统必须有效学习智能体过去的时空信息，以预测其未来轨迹。现有多种方法通过堆叠模型中的独立组件来学习时间特征，以捕捉时序信息。本文提出了一种名为“时序航点丢弃”（TWD）的新型框架，通过航点丢弃技术促进显式时间学习。基于航点丢弃的学习能够迫使模型提升对智能体间时序关联的理解，从而显著增强轨迹预测性能。轨迹预测方法通常假设观测到的轨迹航点序列是完整的，忽略了实际场景中可能出现缺失值的情况，这会影响其预测表现。此外，这些模型在预测时往往对特定航点序列存在偏差。我们的TWD可有效解决上述问题：它融合了随机与固定的处理流程，通过根据时序序列策略性地丢弃航点来正则化投影后的历史轨迹。通过大量实验，我们证明了TWD在迫使模型学习智能体之间复杂时序关联方面的有效性。该方法能够与现有轨迹预测方法互补，从而提升预测精度。我们还在NBA Sports VU、ETH-UCY和TrajNet++三个数据集上对提出的方法进行了评估。