Vision-based Multi-future Trajectory Prediction: A Survey
This survey helps researchers in autonomous systems and computer vision by organizing and advancing the field of MTP, though it is incremental as it synthesizes existing work rather than introducing new methods.
This paper presents the first survey on multi-future trajectory prediction (MTP), which addresses the challenge of generating diverse and plausible future trajectories for agents based on past data and environment information, by providing taxonomies, analyzing frameworks, datasets, and evaluation metrics, and comparing models on existing datasets including experiments on the ForkingPath dataset.
Vision-based trajectory prediction is an important task that supports 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 diverse 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 multi-future trajectory prediction (MTP) has recently been studied. This task 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 a comprehensive analysis of frameworks, datasets and evaluation metrics. We also compare models on existing MTP datasets and conduct experiments on the ForkingPath dataset. Finally, we discuss multiple future directions that can help researchers develop novel multi-future trajectory prediction systems and other diverse learning tasks similar to MTP.