Trajectory prediction is a crucial undertaking in understanding entity movement or human behavior from observed sequences. However, current methods often assume that the observed sequences are complete while ignoring the potential for missing values caused by object occlusion, scope limitation, sensor failure, etc. This limitation inevitably hinders the accuracy of trajectory prediction. To address this issue, our paper presents a unified framework, the Graph-based Conditional Variational Recurrent Neural Network (GC-VRNN), which can perform trajectory imputation and prediction simultaneously. Specifically, we introduce a novel Multi-Space Graph Neural Network (MS-GNN) that can extract spatial features from incomplete observations and leverage missing patterns. Additionally, we employ a Conditional VRNN with a specifically designed Temporal Decay (TD) module to capture temporal dependencies and temporal missing patterns in incomplete trajectories. The inclusion of the TD module allows for valuable information to be conveyed through the temporal flow. We also curate and benchmark three practical datasets for the joint problem of trajectory imputation and prediction. Extensive experiments verify the exceptional performance of our proposed method. As far as we know, this is the first work to address the lack of benchmarks and techniques for trajectory imputation and prediction in a unified manner.

翻译：轨迹预测是从观测序列中理解实体运动或人类行为的关键任务。然而，现有方法通常假设观测序列是完整的，忽视了因目标遮挡、范围限制、传感器故障等导致的缺失值。这一局限不可避免地影响了轨迹预测的准确性。为解决此问题，本文提出统一框架——基于图的条件变分循环神经网络（GC-VRNN），可同时实现轨迹插补与预测。具体而言，我们引入新型多空间图神经网络（MS-GNN），从非完整观测中提取空间特征并利用缺失模式。此外，通过设计含时域衰减模块的条件VRNN，捕获非完整轨迹的时间依赖性与时域缺失模式。时域衰减模块的加入使有效信息得以沿时间流传递。我们还整理并基准测试了三个实用数据集，用于轨迹插补与预测的联合问题。大量实验验证了所提方法的卓越性能。据我们所知，这是首个以统一方式解决轨迹插补与预测基准缺失及技术空白的工作。