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. Furthermore, prediction methods often operate under the assumption that observed trajectory waypoint sequences are complete, disregarding scenarios where missing values may occur, which can influence their performance. Moreover, these models may be biased toward particular waypoint sequences when making predictions. We propose a novel approach called Temporal Waypoint Dropping (TWD) that explicitly incorporates temporal dependencies during the training of a trajectory prediction model. By stochastically dropping waypoints from past observed trajectories, the model is forced to learn the underlying temporal representation from the remaining waypoints, resulting in an improved model. Incorporating stochastic temporal waypoint dropping into the model learning process significantly enhances its performance in scenarios with missing values. Experimental results demonstrate our approach's substantial improvement in trajectory prediction capabilities. Our approach can complement existing trajectory prediction methods to improve their prediction accuracy. We evaluate our proposed approach on three datasets: NBA Sports VU, ETH-UCY, and TrajNet++.

翻译：轨迹固有的多样性和不确定性给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间与空间信息，以预测其未来轨迹。现有许多方法通过堆叠模型中的独立组件来学习时间运动特征。此外，预测方法通常假设观测到的轨迹航路点序列是完整的，忽略了可能出现缺失值的情况，这会影响模型性能。同时，这些模型在进行预测时可能对特定航路点序列存在偏差。我们提出了一种名为 Temporal Waypoint Dropping (TWD) 的新方法，该方法在轨迹预测模型训练过程中显式纳入时间依赖性。通过随机丢弃过去观测轨迹中的航路点，模型被迫从剩余航路点中学习潜在的时间表示，从而得到改进的模型。在模型学习过程中引入随机时间航路点丢弃策略，显著提升了模型在缺失值场景下的表现。实验结果表明，我们的方法在轨迹预测能力上取得了显著改进。本方法可补充现有轨迹预测方法，提升其预测精度。我们在三个数据集上评估了所提方法：NBA Sports VU、ETH-UCY 和 TrajNet++。