Incomplete Trajectory

Incomplete trajectory prediction focuses on accurately forecasting the movement of objects when only partial or noisy trajectory data is available, a common challenge in various applications like autonomous driving and maritime surveillance. Current research emphasizes developing robust models, often employing transformer networks, recurrent neural networks, or graph neural networks, to handle missing data through imputation and prediction techniques, incorporating features like multi-scale attention and adaptive information extraction. These advancements are crucial for improving the reliability and safety of systems reliant on accurate motion prediction in real-world scenarios where complete data is rarely guaranteed.