  Trajectory prediction is an important task to support safe and intelligent
behaviours in autonomous systems. Many advanced approaches have been proposed
over the years with improved spatial and temporal feature extraction. However,
human behaviour is naturally multimodal and uncertain: given the past
trajectory and surrounding environment information, an agent can have multiple
plausible trajectories in the future. To tackle this problem, an essential task
named multimodal trajectory prediction (MTP) has recently been studied, which
aims to generate a diverse, acceptable and explainable distribution of future
predictions for each agent. In this paper, we present the first survey for MTP
with our unique taxonomies and comprehensive analysis of frameworks, datasets
and evaluation metrics. In addition, we discuss multiple future directions that
can help researchers develop novel multimodal trajectory prediction systems.
