  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.
