MaTF: Maneuver-Aware Temporal Fusion for Trajectory Prediction Under Arbitrary Observation Length

Abstract

Trajectory prediction is essential for many robotic applications, yet most existing models rely on fixed-length observations and struggle with temporally irregular inputs. In real-world settings, prediction difficulty further increases when agents exhibit strong maneuverability, as their future motions depend on distinct short-term and long-term temporal cues. A Maneuver-aware Temporal Fusion framework is proposed to separate short-term dynamics from long-term intentions and fuse them through a motion-complexity-guided attention mechanism. The framework first extracts temporal features at different scales, and then adaptively balances them according to the maneuver patterns of each agent. To support incomplete or short observations, a self-distillation strategy is introduced to reconstruct missing motion segments, enabling consistent prediction without relying on explicit teacher-student models. Furthermore, a Mamba-Transformer hybrid backbone is employed to enhance computational efficiency and improve generalization under arbitrary observation lengths. Experiments on the ETH/UCY and SDD datasets show that MaTF consistently outperforms existing methods, particularly in scenarios with irregular or shortened observations.