Multiple Trajectory

Multiple trajectory analysis focuses on understanding and modeling the diverse paths objects or agents take over time, aiming to predict future movements or infer underlying patterns from observed data. Current research emphasizes developing robust methods for generating and representing multiple plausible trajectories, often employing generative models, neural networks (like JEPA and Deep Deterministic Policy Gradient), and techniques such as optimal transport and determinantal point processes to enhance diversity and accuracy. These advancements have significant implications for various fields, including autonomous driving, human-computer interaction, and traffic management, by enabling more accurate predictions and improved decision-making in dynamic environments.