State of the Art Trajectory

Trajectory prediction, aiming to forecast the future movement of agents (e.g., vehicles, pedestrians), is a crucial area of research with applications in autonomous driving and robotics. Current efforts focus on improving model accuracy and robustness using various neural network architectures, including those incorporating probabilistic representations (e.g., normalizing flows, diffusion models), memory mechanisms, and attention mechanisms to capture agent interactions and environmental context. Research also emphasizes addressing challenges like cross-dataset transferability, uncertainty quantification, and developing more meaningful evaluation metrics beyond simple distance-based measures, particularly those sensitive to the underlying road structure. These advancements are vital for enhancing the safety and reliability of autonomous systems.